DHQ: Digital Humanities Quarterly
2018
Volume 12 Number 1
Volume 12 Number 1
Computational Models for Analyzing Data Collected from Reconstructed Cuneiform Syllabaries
Abstract
This study used three interdependent techniques to help understand the use and distribution of syllabic values of the cuneiform signs during the second half of the third millennium and early second millennium BCE. The results suggest that, during this period, cuneiform syllabaries were variable. That variation can further inform us about the regional, temporal, and dialectical contexts in which they existed. The addition of this research to the wider literature on the early adaptation of cuneiform enhances the field's understanding of how cuneiform syllabic values began to emerge and spread across the ancient Near East, and demonstrates how computational methods of analysis can be applied to research questions in humanities subjects.
1. Background
Cuneiform is the earliest writing system attested in history, emerging towards
the end of the fourth millennium B.C. in a region of ancient Mesopotamia that
corresponds with the southern part of modern-day Iraq. The population first
associated with cuneiform spoke an isolate language called Sumerian. However,
Semitic-speakers who co-inhabited the same region quickly adapted the script to
write their own language, Akkadian. Akkadian is the earliest attested language
of the Semitic family, which includes better-known languages such as Arabic,
Hebrew, and Aramaic.
The signs that made up this writing system consisted of four types: logograms
(sometimes called ideograms), which represented whole words or ideas;
syllabograms, which represented whole syllables such as tu, ta, or
ti; determinatives, which were a set of signs that indicated,
in limited circumstances, the category a word belonged to (i.e., gods, trees,
precious stones, personal names, etc.); and numerical signs, which belonged to
either a decimal or sexagesimal counting system (both systems were used
simultaneously in ancient Mesopotamia). Each sign in these categories was
crafted by impressing one or more wedge-shaped markings with the aid of a
stylus, usually made from a reed cut to have a triangular tip, into the still
soft surface of clay tablets (Figure 1). These clay tablets naturally dried and
hardened in the arid and hot climate, and as a result they survived in great
numbers. This script survived for over 3000 years from around 3300 BC to 70 AD
and became the hallmark of Mesopotamian history and culture.
The spread of Mesopotamian culture from southern Iraq into neighboring regions,
including northern Iraq, Turkey, and Syria, coincided with the development of
the cuneiform writing system, during the late fourth millennium and first half
of the third millennium BC. During the next few hundred years, this writing
system was adopted by the populations outside of Mesopotamia that were
interacting regularly with Sumer and Babylonia primarily through trade. As the
cuneiform system developed and spread, it was adapted to express non-Sumerian
words, including personal names. In general, Akkadian and other languages
adapted the cuneiform script by means of the same rebus principle used by
Sumerian cuneiform to express grammatical affixes [Cooper 1996, 45].
Figure 1.
An example of a cuneiform tablet (OSP 1, 131; left) and the line drawing
of that tablet (right). Image courtesy of the Cuneiform Digital Library
Initiative (http://cdli.ucla.edu).2. Introduction
In this study[1],
interdisciplinary techniques were used to answer questions about how the
cuneiform script, the earliest-attested writing system used between 3300 BCE and
100 CE in the ancient Near East, was adapted by Semitic-speaking peoples across
Mesopotamia and Syria. The earliest, but scarce, evidence of cuneiform signs
being used syllabically to write Semitic words and proper nouns comes from
around 2600-2500 BCE. Between 2350 and 1800 BCE, there is an increase in the
development and use of signs with syllabic values across Mesopotamia and Syria.
These syllabic values (together called “syllabaries”)
continue to develop until standardizations of cuneiform signs and their values
begin to be enforced around 1800 BCE, which essentially ends any major
variability in the script within specific periods. This provides us with a
period of almost 600 years, spanning the second half of the third millennium and
early second millennium BCE, during which there is a wealth of textual data
documenting the first full adaptation of the cuneiform script to syllabically
write Semitic words and proper nouns. I investigated differences in the values
of cuneiform signs used to write Semitic words excavated from nine sites that
produced cuneiform texts during the late third millennium and early second
millennium BCE in order to understand the extent to which any variation
occurred. This analysis of the variation could then inform other questions about
dialect differences, educational practices, and power dynamics across the
region.
Individual studies of the signs attested with a syllabic value among the selected
Syrian corpora form the basis of this investigation. These studies aim to
provide a clear, consistent, and complete description of syllabic value
attestations in Syria and Mesopotamia, and the information they provide is
concatenated into a table of reconstructed syllabaries for each site. Together,
these reconstructed syllabaries form a pan-Mesopotamian dataset informed by the
most current knowledge of syllabic values attested at these sites during the
roughly 600-year period being examined.
After curating a dataset of the syllabic values attested at each site, I analyzed
the data using three complementary computational methods: phylogenetic
estimation, hierarchical clustering, and principal
component analysis (PCA). These tools organized the data into visual
hierarchies and identified the principal drivers behind the variation observed
in the data. The results strongly support the conclusion that geography and time
are the most significant factors affecting syllabic value observations across
Mesopotamia, which indicated that during this period scribal communities adapted
the cuneiform script’s syllabic system independently and continued to use their
local syllabaries despite the differences that any individual scribe encountered
through his interactions with other communities across the region. The results
of the computational analyses then suggested directions of further inquiry: I
examined the linguistic environments of the syllabic values that most directly
influenced the variation found in the dataset[2]. This component of the research
provided evidence for greater dialectical variation across the geographic region
than was previously assumed.
In summary, this research uses a series of three interdependent techniques to
determine and understand the use and distribution of syllabic values within the
cuneiform writing system during the second half of the third millennium BC and
early second millennium BC. The results suggest that during this period
cuneiform syllabaries are variable, and that variation can further inform us
about the regional, temporal, and dialectical contexts in which they existed.
The addition of this research to the wider literature on the early adaptation of
cuneiform enhances the field’s understanding of how cuneiform syllabic values
began to develop and emerge across the ancient Near East, and demonstrates how
computational methods of analysis can be applied to research questions in
humanities subjects.
3. Sites Examined
The aim of this study was to examine the spread and adaptation of syllabic signs
used to write Semitic words and proper nouns, so the sites initially considered
for examination must have produced texts wholly or partially written in a
Semitic language or dialect. In the third millennium, there were 19 such sites,
and ten of these have been chosen for inclusion in this study due to the number
of relevant tablets attested at each site and easy access (either electronic or
physical) to the tablets: Ebla, Mari, Nabada, Tuttul, Adab, Kish, Tutub,
Eshnunna, Assur, and Gasur[3]. Sites were excluded from this
study if the entire collection of tablets is housed in Syria, Iraq, or Turkey,
or if I was not granted access to the collections housed in museums Europe.
By examining these texts, the syllabaries attested at each of these sites were
reconstructed (except Ebla, see below) by examining the published photos or
transliterations of the texts of each site, and by examining a few of the
tablets in person that have not yet been sufficiently published. I have
therefore relied on a combination of analog data, digital data,[4] and in-person examination of texts for the
collation of the syllabaries of the sites included in this study.
Figure 2.
The ten sites included in this study are Ebla, Mari, Nabada, Tuttul, Adab,
Kish, Eshnunna, Tutub, Assur, and Gasur.The reconstructed syllabaries of Adab, Kish, Assur, and Gasur were collected
using texts published on the Cuneiform Digital Library Initiative’s database.
The syllabary from Tutub was reconstructed using texts available both in the
Cuneiform Digital Library Initiative (CDLI) database and Sommerfeld’s (1999)
“Die Texte der Akkade-Zeit. 1. das Dijala-Gebiet:
Tutub.” The data from Eshnunna was collected from texts published in
Gelb’s (1952) “Sargonic Texts from the Diyala Region”
(MAD 1, nos. 1-195, 270-336.) and from the CDLI database. Many of these tablets
are fragmentary and retain few discernible lines of text.
For the Tuttul and Nabada corpora, I relied on the works of Krebernik [Krebernik 2001] and Ismail [Ismail et al. 1996]
respectively, as well as well-structured and collated digital data on the CDLI
for both. Because of the size of the Mari and Ebla corpora, limitations had to
be imposed on the type of texts examined in the reconstruction of the
syllabaries of these two sites. For Mari, I have chosen to include only the
texts published by Limet [Limet 1976] and for Ebla I only consider
the already-published syllabaries of the lexical texts [Krebernik 1982/3, 178–236]
[Conti 1990, 3–60] and the texts published on the Ebla
Digital Archives.[5]
The table below outlines the sites that are examined in this study. It includes
the periods examined, the number of texts used for the collection of the data,
and the genres[6] attested among the texts
included (plus the numbers within each genre in parentheses). For all sites
examined, the majority of the texts are administrative in genre, with a small
number being epistolary, literary, or lexical texts.
| Site | Region | Period(s) | No. of Texts | Genres (no.) |
| Ebla | Syria | Old Akkadian[7] | ca. 7000 | Lexical[8]; Administrative Letter |
| Mari | Syria | Ur III / Shakkanakku[9] | 463 | Administrative (463) |
| Nabada | Syria | Old Akkadian | 223 | Administrative (222); Legal (1) |
| Tuttul | Syria | Early Old Babylonian[10] | 54 | Administrative (51); Letter (2); Uncertain (1) |
| Adab | S. Mes.[11] | Old Akkadian, Ur III | 1946, 130[12] | Administrative (1854, 102)[13]; Letter (27, 1); Royal/monumental (25, 21); Legal (22, 3); Uncertain (16, 0); Lexical (1, 0); Mathematical (1, 0); School (1, 2) |
| Eshnunna | S. Mes. | Old Akkadian | 261 | Administrative; Uncertain (26); School (8); Letter (6)l Literary (1) |
| Kish | S. Mes. | Old Akkadian | 80 | Administrative (68); Letter (5); Royal/monumental (3); Votive (2); Lexical (1); Literary (1) |
| Tutub | S. Mes. | Old Akkadian | 73 | Administrative (65); Royal/monumental (7); Legal (1) |
| Assur | N. Mes.[14] | Old Akkadian | 20 | Royal/monumental (7); Lexical (6); Administrative (4); School (3) |
| Gasur | N. Mes. | Old Akkadian | 220 | Administrative (190); Lexical (15); Letter (9); Legal (2); School (2); Mathematical (1); Uncertain (1) |
Table 1.
The ten sites examined for this study.4. Methodology
4.1 Computational methods of analysis: a three-step approach
Three different methods of analysis were used to visualize this dataset.
These three methods are phylogenetic estimation, hierarchical clustering,
and principal component analysis. Phylogenetic estimation was included
because it is a common method used in the study of language and manuscript
evolution [Platnick and Cameron 1977]
[Atkinson and Gray 2005] and the results could therefore be more easily
compared with previous studies. However, because of the novelty of using
phylogenetic systematics to understand writing system evolution, and because
of the relative inflexibility of phylogenetic programs to filter out
relevant data from noise, two other computational methods, programmed in R
within the IDE RStudio, were used to test the results of the phylogenetic
estimation model. All three types of analysis were conducted on an Apple
laptop. The second two techniques help manipulate, filter, and visualize the
data in different ways; they organize the data according to similarities and
differences, and can isolate key data points that influence the results.
These three methods will be discussed further below.
4.2 Unfiltered and filtered datasets
An initial examination of the dataset reveals that there are two particular
aspects of the data that are skewing the preliminary results: the lack of
sufficient data from Assur, and the presence of a large number of signs that
only occur at Ebla. The insufficient data from Assur is due to my lack of
access to museums in which the corpus is housed. Ebla’s larger number of
syllabic sign values may be due to the different methods of determining
syllabic values used by me and by the team that published the Ebla
syllabary, to dialectical variation, or due to the larger number of
non-administrative texts. In order, therefore, to avoid these issues and to
avoid possible human error in the syllabaries I reconstructed, all hapax
values, or syllabic values at each site that are attested at only one site,
were removed in the filtered dataset.
Table 2 below shows the number of syllabic values at each site that occur at
at least two sites; Assur is a clear outlier with only 6 of these signs
attested. Because there is not enough data for Assur to be informative, this
site will be removed from further analysis.
| Ebla | Mari | Nabada | Tuttul | Adab | Eshnunna | Kish | Tutub | Assur | Gasur |
| 128 | 100 | 70 | 105 | 78 | 60 | 105 | 108 | 6 | 90 |
Table 2.
The number of syllabic values at each site that occur at at least one
other site.Table 3 shows the number of hapax syllabic values. The number of hapax
syllabic values at Ebla is much higher than at the other sites examined, and
were subsequently filtered out of the dataset for the secondary
analysis.
| Ebla | Mari | Nabada | Tuttul | Adab | Eshnunna | Kish | Tutub | Assur | Gasur |
| 34 | 10 | 4 | 11 | 9 | 1 | 6 | 5 | 0 | 5 |
Table 3.
The number of hapax syllabic values, or syllabic values that occur at
only one site, attested at each site.The three methods of analysis described below were applied to both the
initial unfiltered dataset – which includes hapax syllabic values (or signs
that occur at only one site), ubiquitous syllabic values (or syllabic values
that occur at all sites), and the syllabic values from Assur – as well as
the filtered dataset which excludes the previous three features of the data.
The results of both the unfiltered and filtered datasets will be presented
for each method below.
5. Phylogenetic Estimation
An analysis based on phylogenetic estimation[15] can
scientifically test our hypotheses about the nature of the adaptation of
cuneiform to write Semitic language(s) across Mesopotamia and Syria. The primary
strength of phylogenetic analysis is its ability to reconstruct tree- or
network-like relationships across time; because the spread and adaptation of
cuneiform must have necessarily occurred over a period of time – even a
relatively short one – these methods can provide interesting insights into the
nature of the script’s spread and help us determine which sites cluster or
diverge. Phylogenetic methods are advantageous because the data input and
methods used are always transparent, and therefore the results should be
repeatable.[16]
Phylogenetic methods, which were originally developed by evolutionary biologists
for the analysis of trait inheritance and gene expression in particular phyla or
species over time, have been used with increasing frequency in the field of
linguistics for at least twenty years [Nichols and Warnow 2008, 760].
These methods have been employed in the analysis and historical reconstruction
of Indo-European [Gray 2003], African [Marten 2006, 43–55], and Semitic [Kitchen et al. 2009] languages families;
in the reconstruction of language and dialect relationships [Nakhleh et al. 2005a]; and to reconstruct manuscript evolution [Barbrook et al. 1998]. Although many of these studies have produced
promising results, this application of phylogenetic inference techniques to
reconstruct writing system evolution is still relatively new and untested [Skelton 2008]. The application of phylogenetic methods to this
type of data is therefore unique, and the optimal search criteria and program
settings have not yet been established. In this study, I used an optimization
criterion called maximum parsimony (described further below).
5.1 Experimental Method
Phylogenetic analyses can employ a number of different methods of searching
for and evaluating phylogenetic trees [Swofford et al. 1996, 478–93]. These methods tend to be either algorithm-based or
optimality-criterion-based. Algorithm-based methods rely on algorithms to
search for trees and to determine which tree is the correct one, and have
the advantage of short computation time. Optimality-criterion-based methods,
on the other hand, use different criteria for determining which tree is the
best — called the optimality criterion — to find the tree in the first place
— or the search strategy. This method is advantageous because the use of two
different criteria for searching for the trees and for determining which one
is best makes it easier to determine the likelihood that the tree produced
is the correct tree [Swofford et al. 1996, 408–9].
Optimality-criterion-based methods were used for this analysis because the
relatively small dataset (compared to datasets of millions of data points in
genomic studies) does not make computation time a concern.
Based on her work on Linear B, Skelton determined that the most appropriate
optimality criterion for a phylogenetic analysis of a writing system is
maximum parsimony, and I therefore consider it here as well [Skelton 2008, 170]. Maximum parsimony has been used in
the study of several language families, including the Bantu language family
on a variety of datasets, with [Bastin 1983], [Holden 2002], and [Holden et al. 2005] using only
lexical data, and Rexova et al. (2006) using grammatical data. Others have
used lexical data [Nakhleh et al. 2005a]
[Rexovà et al. 2003] and a combination of types of data [Nakleh et al. 2005b] to study Indo-European languages. Maximum
parsimony has also been used by Cysouw et al. to study Mixe-Zoquean [Cysouw et al. 2006].
Maximum parsimony is an optimization problem that aims to produce a tree in
which the minimum number of character state changes occurs; using this
optimization problem follows the assumption that the path of least
resistance would be not to adapt or create new syllabic values. It uses a
simple model of character state change, which assumes that each change is
equally likely to occur as any other change. While this assumption may not
be correct for any given dataset, it is usually not possible to estimate
actual probabilities of character change, and so an assumption of equal
probabilities is necessary and appropriate; other optimality criteria, such
as maximum likelihood, require explicit models of evolutionary change, which
is not possible in this case.
Maximum parsimony analyses can allow characters with missing entries; for
example, maximum parsimony encounters no problem when a language under study
has no word for a given semantic slot. Since the data used here contains
characters that are not always present in each taxon, this feature of
maximum parsimony is necessary.
With maximum parsimony, the phylogenetic estimation program creates a series
of possible trees and then assigns each one a “tree
length,” which is the sum of the weights of the character
state changes that occurred on all branches of the tree. Maximum parsimony
considers the tree with the shortest tree length – or fewest character state
changes – to be the best solution [Skelton 2008, 171].[17]
It is not uncommon for a maximum parsimony method to produce a number of
trees with the same tree length. In this situation, it is possible to run an
algorithm that produces a consensus tree. The algorithm examines the trees
of equal tree length in order to determine which feature(s) the trees all
share, or which a majority of the trees share. The usual features that are
relevant in this determination are the splits, or bipartitions, on the leaf
set induced by the edges of each tree; in other words, a consensus tree can
be a tree that has exactly all the splits that each of the input trees have
[Nichols and Warnow 2008, 770–3].[18] Since the phylogenetic estimation method
produced only one tree in this case, this consensus method was not
needed.
Another technique for estimating the support values (or the likelihood
values) for a single tree, or for the branches of a given tree, is a
statistical re-sampling technique called bootstrapping. Bootstrapping
creates new, random datasets using characters from the original data matrix
and runs them through the same set of parameters. This technique can be used
simply to estimate support values for the edges of a tree, where the support
values are the fraction of times that that particular bipartition appears in
the random bootstrap trees; it can also be used as input data in a consensus
method, like those described above, which would then be annotated with the
support value estimates [Nichols and Warnow 2008, 773]. This
technique dictates that a high support value for a particular bipartition
increases the likelihood that that bipartition is accurate. This technique
will be applied to the data for both the phylogenetic analysis and the
hierarchical clustering.
5.2 Taxa
Taxa are the independent variables, or more basic entities being studied. In
biology, species or gene sequences are often used as taxa; this study uses
sites[19] as taxa. Using individual tablets as taxa is
not an option because there is not enough data on most tablets for this to
be viable. Using scribal hands as taxa (as were used in [Skelton 2008]) is also not an option since hands have so far
not been established, or even suggested, for these corpora [Biggs 1973, 39].
5.3 Characters
Characters are the dependent variables of a study. In biology, molecular or
phenotype data are often used as characters; this study uses syllabic values
of signs used in Semitic words and proper nouns as characters. The character
states are either 1 or 0, indicating presence or absence (respectively) of
that particular character in a taxon.
Using syllabic values as the characters in this study is not without its
problems. While in most words or personal names it is clear what the
syllabic value is meant to be, there are some cases where it is unclear.
There is also the issue of human error, either in my own transliterations or
on the transliterations and sign lists compiled by other scholars that have
been relied upon.
5.4 Program and Settings
There are a number of programs available that perform phylogenetic analyses,
each of which specializes in specific methods for estimating phylogenies.
The program PAUP*, version 4.0a146 for Macintosh [Swofford 2001], specializes in parsimony methods, and so was used to analyze this
dataset. Other programs such as TNT [Goloboff et al. 2003], Mesquite
[Maddison and Maddison 2001], or PHYLIP [Felsenstein 2005] could also have been used to estimate the phylogeny of the data through
maximum parsimony.
The data matrix that was imported into PAUP* consists of 326 total characters
(or sign values); 116 of these are determined to be parsimony-uninformative,
and 200 are parsimony-informative. Because of the relatively small dataset,
an exhaustive search was used to generate the optimal tree (see Figure 3). A
bootstrapping resampling method was then run using a full heuristic search
strategy; the resulting consensus tree retained groups with a frequency of
greater than 50 percent (see Figure 4). For both trees, no outgroup was
defined so the trees are arbitrarily rooted at the first taxon (Ebla).[20]
5.5 Results
Figure 3.
The first tree resulting from the phylogenetic estimation of the
unfiltered dataset using maximum parsimony in the program PAUP*.
Figure 4.
The consensus tree with p-values generated from a bootstrap resampling
method on the unfiltered dataset.The trees resulting from this analysis will represent similarities and
differences – not the interdependence or genealogy – in the data, and could
reflect three different realities and therefore support three different
conclusions about the syllabaries. The possible results could be:
- That the syllabaries exactly mirror the geography of the sites. This would indicate a relationship between the syllabaries that was based purely on geographic proximity of the sites. In other words, this tree would support the conclusion that there was an organic spread of the development and use of syllabic values from site to site.
- That the trees mirror the geography of the sites to a certain extent. This would indicate that geography was perhaps one factor in how similar the syllabaries examined are. In other words, this tree would suggest that sites nearer to each other were more influenced by each other’s syllabaries and sites further away from each other developed “genetic mutations” or independent changes in their syllabaries, but that this was not the sole influencing factor.
- That the trees reflect geography in no way. This would indicate that the differences observed in the syllabaries must be attributed to another cause or causes.
The tree in Figure 4 shows the tree resulting from the phylogenetic analysis
on the unfiltered dataset. The tree in Figure 5 shows the consensus tree
with p-values created using a bootstrapping re-sampling technique, also on
the unfiltered dataset.
The implications of these results would suggest either that these adaptations
occurred either gradually across the region as the technological innovation
spread from site to site, or that there are other contributing factors, such
as local scribal innovation (perhaps based on dialect differences between
the sites).
A few preliminary observations can be made based on these results. First, it
is notable that Ebla is an outlier among the taxa. The Mari and Tuttul
syllabaries seem to be more similar to the rest of the cohort; Nabada and
Assur are clustered together. Finally, the phylogenetic estimation groups
together the syllabaries of Eshnunna, Kish, Tutub, and Gasur. Aside from the
slightly closer relationship between the Mari and the Tuttul syllabaries,
which could theoretically be explained by the geographic proximity of these
two sites, these results are unexpected and not immediately explainable.
Upon closer examination, the grouping of Nabada and Assur together can be
explained by the relatively low number of syllabic values attested at each
site; although the reasons for this differ for each. As explained above
(Section 4.2), insufficient data was available from Assur which impacted the
number of syllabic sign values in the reconstructed syllabary. At Nabada, at
the other hand, the scribes writing these tablets seem to have used
significantly fewer syllabic signs compared to logographic signs. This is an
interesting and unexpected result[21]. The other patterns observed in these
results are more difficult to explain.
Figure 5.
The tree resulting from the phylogenetic estimation of the filtered
dataset using maximum parsimony in the program PAUP*.
Figure 6.
The consensus tree resulting from the bootstrapping resampling method
on the filtered dataset.Figures 5 and 6 show the results of the phylogenetic analysis and the
bootstrapping re-sampling technique on the filtered dataset. Most
interesting is that filtering hapax syllabic values and ubiquitous syllabic
values did not affect the results. The bootstrap consensus tree of the
filtered dataset reveals that Ebla and Nabada are most distant from the
other sites, and closely similar to each other, and that Mari and Tuttul are
similar with a p-value of 50%. According to this model, there are no strong
associations between the Mesopotamian sites except for Kish and Gasur, whose
sub-cluster has a p-value of 52% (see the section on hierarchical clustering
below for an explanation of the relevance of p-values)
Based on this initial examination, it appears that the geography of the sites
themselves is mirrored to a certain extent in the phylogenetic analysis.
This supports one hypothesis that, if significant variation is attested
within the syllabaries of these sites, that variation would be correlated
with the geographic situation of the sites. In other words, local variation
and innovation in syllabic sign values existed and permeated nearby cities.
These localized sign values remained the most prominent feature of each
site’s syllabary. In terms of practical relevance for modern scholars, this
indicates that the composition of the syllabary of a corpus of cuneiform
texts can determine where those tablets originated.
Clustering techniques such as phylogenetic analysis are useful for
determining general tendencies within a dataset by finding the natural
clusterings of that given dataset. However, since most clustering algorithms
create clusters regardless of any inherent cluster structure in the dataset,
other methods are required to externally validate the results. Hierarchical
clustering and principal component analysis were therefore used to verify
the results of the phylogenetic analysis and to determine other factors
contributing to the observed variation in the syllabaries.
6. Hierarchival Clustering
Figure 7.
The hierarchical clustering on the unfiltered (a) and filtered (b)
datasets show different results. The hierarchical clustering of the filtered
dataset indicates a stronger connection between syllabary and geography that
the hierarchical clustering of the unfiltered dataset.Hierarchical clustering[22] is a
visual clustering technique that organizes data into dendrograms; in this case,
the technique was used to organize sites and syllabic values according to
similarities and differences. Because the data collected and used in this study
is straightforward, binary data, using different clustering algorithms does not
produce different results. This method is in many ways similar to phylogenetic
analysis, but uses different algorithms and relies on different underlying
assumptions about the dataset. If these two techniques produce the same results,
that is a good sign that the branchings and clusterings observed are present in
the dataset and not a coincidence of the particular algorithm used.
Figures 7a and 7b show the results of the hierarchical clustering (using a
Manhattan distance function and a Ward clustering metric) on the filtered and
unfiltered datasets. The light grey indicates absence of a syllabic value at a
site; dark grey indicates presence. The trees along the upper x-axes of the
graphs shows how the sites cluster or diverge; the trees along the left-hand
y-axes of the graphs shows the resulting clusterings of the syllabic values. It
is the clusterings of the syllabic values that determine how the sites will
cluster; because of the focus on how the syllabaries of these sites compare and
relate to one another, the focus in this work is on the trees and clusterings of
the sites themselves.
Compared to the results of the analysis on the unfiltered data, the results of
the filtered data (to exclude Assur, any values that occur at only one site, or
hapax syllabic values, and syllabic values that occur at all sites, or
ubiquitous syllabic values) appear different. The results of this analysis
indicate that Eshnunna, Adab, Gasur, Tutub and Kish are distinct from Nabada,
Ebla, Mari, and Tuttul, the four sites in Syria. This suggests that there is a
stronger geographically driven pattern in the data than the phylogenetic
estimation originally seemed to indicate.
A bootstrap resampling technique was run on the results of the hierarchical
clustering on the filtered dataset (Figure 8) using the package pvclust with a
Manhattan distance method and a Ward D2 cluster method (the same methods used in
the hierarchical clustering). This package provides two types of p-values: AU
(Approximately Unbiased) p-value and BP (Bootstrap Probability) value. AU
p-value, which is computed by multiscale bootstrap resampling, is a better
approximation to unbiased p-value than BP value computed by normal bootstrap
resampling [Suzuki and Shimodaira 2014, 4]. The clusters in the tree that
group the Syrian syllabaries and the Mesopotamian syllabaries together are
strongly supported by the data, having AU p-values of 93% and 94%,
respectively.[23] The partitions grouping Mari and
Tuttul together and Ebla and Nabada are supported by the data, though less
strongly, with AU p-values of 83% and 86% respectively. The grouping of the
Mesopotamian syllabaries is less strongly supported by the data (the Kish and
Gasur cluster has an AU p-value of 69%; the Adab, Eshnunna, and Tutub cluster
has an AU p-value of 71%; and the Eshnunna and Tutub cluster has an AU p-value
of 73%). Overall, these support values suggest that the results of this analysis
are supported by the data, and suggest very close affinities between the Syrian
syllabaries of Ebla, Nabada, Mari, and Tuttul. The data also strongly support a
grouping of the Mesopotamian syllabaries.
Figure 8.
The AU and BP p-values support the results of the
hierarchical clustering on the filtered dataset.Principal component analysis can be used to visualize these patterns of
clustering and to determine which syllabic values are driving these results.
7. Principal Component Analysis
Principal component analysis (PCA)[24] is a powerful yet simple tool for
analyzing multivariate data [Wold et al. 1987, 37]. It uses linear
algebra to take a very high-dimensional space (in this case, a 326-dimensional
space because of the 326 data points, or syllabic values) and projects it into a
two-dimensional space. An analysis using PCA always begins with a data table
whose rows are termed “objects” and whose columns are termed
“variables.” By analyzing the data points within this
data table, one can accomplish one or several common goals: data reduction or
simplification; data modeling; outlier detection; variable selection;
classification; prediction; or unmixing [Wold et al. 1987, 38]. The
primary goals of using PCA against the dataset in this study are to determine
outliers and select variables. To address these goals, PCA rotates the axes of
the data table in order to find the two axes that represent the most variation,
or principal components, within the data. In other words, PCA finds the set of
variables that explain the most variance found in the dataset. These variables,
or syllabic values in this case, can then be further examined using
complementary techniques if desired.
7.1 PCA on the unfiltered dataset: the number of syllabic values attested at each site is driving the observed variation
Figure 9.
(a) The variance distribution across principal components derived from
the unfiltered dataset shows that 25% of the total variation can be
attributed to the first principal component. (b) The first principal
component, which can be attributed to the number of syllabic values
attested at each site, is driving the observed variation: the large
number of hapax syllabic values attested at Ebla (the first principal
component) and the lack of sufficient data at Assur (the second
principal component) are driving the observed variation.The principal component analysis on the unfiltered dataset (Figure 9a) shows
that approximately 25% of the variation in this dataset can be attributed to
the first principal component; this component accounts for significantly
more variation than the subsequent principal components, which account for
between 15% and 5% of variation in the dataset. By plotting the first
principal component against the second principal component (Figure 9b), it
becomes clear that the first principal component can be characterised by
Ebla and Assur being outliers on either end of the spectrum. This suggests
that the primary factor driving the results of the principal component
analysis on the unfiltered dataset is the number of syllabic values attested
at each site within the dataset; further, the large number of hapax syllabic
values attested at Ebla and the lack of sufficient data at Assur are the
significant factors influencing the first principal component.
7.2 PCA on the filtered dataset: geographic, temporal, and unknown variation drive the observed variation
Figure 10.
The variance distribution across principal components derived from the
filtered dataset shows that when excluding hapax syllabic values,
ubiquitous syllabic values, and the data from Assur the data is more
complex.
Figure 11.
The plot graph comparing the first and second principal components of
the filtered dataset shows that the first principal component can be
attributed to geographic variation in the sites examined.
Figure 12.
The plot graph comparing the second and third principal components of
the filtered dataset shows that the second principal component can be
attributed to temporal variation in the corpora of the sites
examined.
Figure 13.
The plot graph comparing the first and third principal components of
the filtered dataset shows that there is no apparent pattern to the
clustering observed in the third principal component.The graph in Figure 10 shows what percentage of the total amount of variation
can be attributed to each main component, or sets of variables, of the
filtered dataset (the nature of the first three principal components will be
described further below). The results of the principal component analysis on
the filtered dataset reveal that the first three components account for
nearly half of the total observed variance; only those three principal
components will be examined further. Plotting these principal components can
provide a clearer picture of the patterns: the first two principal
components can be attributed to geographic and temporal variation between
the datasets.
The results of the principal component analysis on the filtered dataset
reveal that geographic variation is the most significant factor contributing
to the observed variation. This can be clearly observed in the plot graph in
Figure 11: the first principal component is plotted along the x-axis of the
graph, and shows that Adab, Gasur, Eshnunna, Kish, and Tutub (the sites in
Mesopotamia, plotted in red for southern sites and green for the northern
site) cluster together on the left-hand side, while Mari, Tuttul, Nabada,
and Ebla (the sites in Syria, plotted in blue) cluster together on the
right-hand side. These results mirror the trend observed in the heatmap in
Figure 7b and seem to indicate that geography can explain the first
principal component, which accounts for almost 20% of the variation observed
in the filtered dataset.
The second principal component (Figure 12, plotted along the x-axis), which
accounts for 15% of the variation observed in the filtered dataset, shows
that Mari and Tuttul, and to a lesser extent Adab, are outliers, while
showing the other sites cluster together. This pattern can be attributed to
the temporal variation in the corpora examined: the syllabaries of most of
the sites examined (Ebla, Nabada, Kish, Eshnunna, Tutub, Assur, and Gasur)
are derived from texts dating from the Old Akkadian period (ca. 2350-2200
BC), designated with a circular plot. The texts from Adab included in this
study date from both the Old Akkadian period and from the Ur III period (ca.
2100-2000 BC), designated with a square plot; from Mari, they date to the Ur
III (or Shakkanakku) period, designated with a diamond-shaped plot; and from
Tuttul, they date from the Early Old Babylonian period (ca. 2000-1900 BC),
designated with a triangular plot.
The third principal component (Figure 12, plotted along the y-axis) displays
no apparent pattern. Nabada and Gasur lie in opposition to Tutub, while the
other sites are distributed evenly between them. This principal component is
included as a point of comparison, and demonstrates that random similarity
in the data can contribute to the results of this type of analysis; it is
only with further inspection that the significance of the results of PCA can
be verified and qualified.
7.3 Summary
These three analytical tools help us visualize the data in different ways.
They organize the data according to similarities and differences, and can
isolate key data points that influence the results. These techniques can
prompt us to think about our data in new ways, and helps us interpret the
results based on our knowledge of the historical and linguistic realities of
the period.
The results of the analyses on the unfiltered, or original, dataset suggest
that the driving factor behind the variation in the unfiltered dataset is
the number of syllabic values attested at each site. The principal component
analysis on this dataset reveals that Ebla and Assur are outliers on either
end of the spectrum within the first principal component: the large number
of hapax syllabic values attested at Ebla and the lack of sufficient data at
Assur are the significant factors influencing the first principal component.
A closer examination of the hapax syllabic values reveals that these
variables may be indicators of dialectical variation.[25]
The results of these analyses on the filtered dataset suggest that
geographic, temporal, and random variation are driving the observed
variation within the filtered dataset. The relevant syllabic values from the
Syrian corpora identified through the principal component analysis can then
be examined further: the lexical items that were collected in the sign
studies of the Syrian corpora are used to further interpret the results of
the computational analyses using traditional linguistic and text-analysis
techniques.
The application of statistical and computational models to this dataset has
demonstrated that a close examination of syllabaries can reveal new insights
and confirm previous assumptions about the nature of the relationships
between sites that use this syllabic writing system. While similarities
between syllabaries during particular periods or within particular regions
may have been assumed to exist, this methodology proves that these trends
are both clearly present and strongly supported by the data.
8. Interpretation of the results of the computational analysis
The results of the computational models on the unfiltered dataset indicate that
the primary and secondary driving factors (the first and second principal
components) can be attributed to geography and time. The interpretation of the
third principal component is less clear, but is presented here as a point of
comparison with the first two principal components. The syllabic values that
most significantly influence the variation in the data, as identified through
the first three principal components, will be presented and discussed here.
Tables 4-6 outline the most influential syllabic values of the top three
principal components and provides their respective loadings, or weights. In
multivariate space — or within datasets that have multiple variables — the
correlation between the principal components and the original variables is
called the component loadings. The component loadings are indicative of how much
of the total variation can be attributed to a given variable; in other words,
the higher the component loading is, the more important that variable is for
that component. For this reason, only syllabic values with loadings greater than
a particular threshold are considered further. The threshold varies for each
principal component, and is determined visually based on the graphs in Figures
14, 16, and 18 (for the first, second, and third principal components,
respectively).
| Syl. Value | Loading |
| kam | 1.983368897 |
| ḫir | 1.962428466 |
| ba [4] | 1.962428466 |
| qi [2] | 1.962428466 |
| kun | 1.692715905 |
| ag | 1.612331814 |
| tim [x] (DIN) | 1.612331814 |
| tur [2] | 1.545653809 |
| tu [3] | 1.545653809 |
| il | 1.529594896 |
| ku [8] | 1.529594896 |
| dab [6] | 1.431202981 |
| tap [x] | 1.431202981 |
| u [9] | 1.431202981 |
| sum | 1.431202981 |
| kun [3] | 1.431202981 |
| ib [2] | 1.431202981 |
| re [2] | 1.347629658 |
| par [2] | 1.347629658 |
| gur | 1.347629658 |
| qur | 1.347629658 |
| šim | 1.347629658 |
| kak | 1.347629658 |
| iz | 1.347629658 |
| ḫar | 1.347629658 |
| bar | 1.302612779 |
| tar [2] | 1.302612779 |
| nim | 1.203189376 |
| zum | 1.203189376 |
| su [4] | 1.166967631 |
| ši [2] | 1.103759203 |
Table 4.
Principal Component 1. The syllabic values that are further examined based
on the loading ranges outlined in Figures 14, 16, and 18.| Syl. Value | Loading |
| su | 2.722681481 |
| lul | 2.722681481 |
| ib | 2.722681481 |
| bi [2] | 2.348397624 |
| ṣil [2] | 2.348397624 |
| u [3] | 2.348397624 |
| mi | 2.025140491 |
| ar | 2.025140491 |
| sa | 2.000191641 |
| dar | 2.000191641 |
| pum | 1.895816769 |
| pu | 1.80880675 |
| iš [11] | 1.80880675 |
| kab | 1.645361752 |
| se [11] | 1.619356098 |
| re | 1.568075837 |
| un | 1.568075837 |
| qu [2] | 1.56399021 |
| num | 1.484426073 |
| la [2] | 1.388553884 |
| uz | 1.342110283 |
| de [3] | 1.328955355 |
| er | 1.229854594 |
Table 5.
Principal Component 2. The syllabic values that are further examined based
on the loading ranges outlined in Figures 14, 16, and 18.| Syl. Value | Loading |
| gan [2] | 2.65175942 |
| kum | 2.65175942 |
| gu | 2.408786917 |
| su [2] | 2.222928872 |
| ri | 2.222928872 |
| wi | 2.222928872 |
| lik | 2.104672973 |
| iḫ | 2.062260202 |
| iq | 1.986903485 |
| ul | 1.986903485 |
| ad | 1.948006674 |
| ap | 1.922014923 |
| šu [11] | 1.703013331 |
| nun | 1.703013331 |
| ša [10] | 1.703013331 |
| kur | 1.703013331 |
| we | 1.703013331 |
| pa [2] | 1.69043491 |
| al | 1.69043491 |
| u | 1.69043491 |
| ki | 1.69043491 |
| ṣa | 1.69043491 |
| sar | 1.642244435 |
| sal [4] | 1.422929132 |
| bir [5] | 1.422929132 |
| ub | 1.422929132 |
| ši [2] | 1.408110193 |
| ut | 1.375395679 |
| sa [3] | 1.375395679 |
Table 6.
Principal Component 3. The syllabic values that are further examined based
on the loading ranges outlined in Figures 14, 16, and 18.8.1 Geographic variation: the primary explanation of variation in the data
Figure 14.
The distribution of loadings for syllabic values on the first
principal component (see Tables 4–6 for the list of syllabic values and
their respective loading).
Figure 15.
The 31 variables with loadings greater than 1.2 that inform the first
principal component.The results of the principal component analysis on the filtered dataset
reveal that geographic variation is the most significant factor contributing
to the observed variation. This can be clearly observed in the plot graph in
Figure 11: the first principal component is plotted along the x-axis of the
graph, and shows that Adab, Gasur, Eshnunna, Kish, and Tutub (the sites in
Mesopotamia, plotted in red for southern sites and green for the northern
site) cluster together on the left-hand side, while Mari, Tuttul, Nabada,
and Ebla (the sites in Syria, plotted in blue) cluster together on the
right-hand side.
8.2 Temporal variation: the secondary explanation of variation in the data
Figure 16.
The distribution of loadings for syllabic values on the second
principal component (see Tables 4–6 for the list of syllabic values and
their respective loading).
Figure 17.
The 23 variables with loadings greater than 1.1 that inform the second
principal component.The second principal component (Figure 12, plotted along the x-axis), which
accounts for 15% of the variation observed in the filtered dataset, shows
that Mari and Tuttul, and to a lesser extent Adab, are outliers, while shows
the other sites cluster together. This pattern can be attributed to the
temporal variation in the corpora examined: the syllabaries of most of the
sites examined (Ebla, Nabada, Kish, Eshnunna, Tutub, Assur, and Gasur) are
derived from texts dating from the Old Akkadian period (ca. 2350-2200 BC),
designated with a circular plot. The texts from Adab included in this study
date from both the Old Akkadian period and from the Ur III period (ca.
2100-2000 BC), designated with a square plot; from Mari, they date to the Ur
III (or Shakkanakku) period, designated with a diamond-shaped plot; and from
Tuttul, they date from the Early Old Babylonian period (ca. 2000-1900 BC),
designated with a triangular plot.
8.3 Indeterminable variation: the third explanation of variation in the dataset
Figure 18.
The distribution of loadings for syllabic values on the third
principal component (see Tables 4–6 for the list of syllabic values and
their respective loading).
Figure 19.
The 29 variables with loadings greater than 1.3 that inform the third
principal component.The third principal component (Figures 12 and 13, plotted along the y-axes)
displays no readily apparent pattern. Nabada and Gasur lie in opposition to
Tutub, while the other sites are distributed evenly between them. This
principal component is included as a point of comparison, and demonstrates
that random similarity in the data can contribute to the results of this
type of analysis; it is only with further inspection that the significance
of the results of PCA can be verified and qualified.
9. Summary of results
A close study of the syllabaries used at individual sites produces an informative
dataset; when analyzed using a number of complementary techniques, this dataset
can reveal new insights and confirm previous assumptions about the nature of the
relationships between sites that use the syllabic sub-system within the
cuneiform writing system. While similarities between syllabaries during
particular periods or within particular regions may have been assumed to exist,
this methodology proves that these trends are both clearly present and strongly
supported by the data.
The three computational techniques applied to this data set produced similar
general results: (1) Ebla is an outlier; (2) the Mesopotamian sites tend to
cluster together more closely; and (3) the Syrian sites cluster. The concurrence
in the results of these methods strongly suggests that there is indeed variation
between the syllabaries of the ten sites examined, and that that variation is
not simply random.
The results of the principal component analysis on the unfiltered dataset suggest
that the driving factor behind the variation in the unfiltered dataset is the
number of syllabic values attested at each site. The principal component
analysis on this dataset reveals that Ebla and Assur are outliers on either end
of the spectrum within the first principal component: the large number of hapax
syllabic values attested at Ebla and the lack of sufficient data at Assur are
the significant factors influencing the first principal component. The results
of the principal component analysis on the filtered dataset suggest that
geographic, temporal, and undeterminable variation are driving the observed
variation within the filtered dataset.
By describing and comparing the most important syllabic values behind the
principal components, it became apparent that dialectical variation is likely
another driving factor behind the variation observed[26]. This factor is most significantly
observed in the unfiltered dataset due to the relatively large number of hapax
syllabic values attested at Syrian sites.
No similar studies into the variation of sign values used at individual sites has
been conducted for any region or period in Mesopotamia, so it was not expected
that such significant variation could be observed among the syllabic values used
at these sites. However, it is not unexpected that this variation is associated
with geography and time period, although the strength of the association is
indeed unanticipated and has implications for the utility of these methods in
other studies of script and dialect variation in both ancient Mesopotamia and
other ancient and modern societies.
10. Conclusions
10.1 Methodology
The use of complementary techniques, such as digital and computational
methods, to aid in the study of early scripts and cultures is a relatively
new enterprise. Near Eastern archaeologists have been using complementary
methods for some time, but most scholars of ancient texts and languages have
only recently begun to appreciate the utility of more advanced computational
modeling to help visualize and interpret data. As it stands, there are
several projects that aim to digitize significant numbers of cuneiform texts
for such analysis, and this first step of database compilation is indeed
necessary before further analysis can be conducted. Now that these databases
are in advanced stages of completion, the data can be visualized and
interpreted using more advanced computational modeling in order to answer
questions, which require data too extensive to be answered by examining
these texts by hand, about the language, culture, economy, and history of
early complex societies. Perhaps most relevantly, these methods will also be
readily applicable to other fields in the humanities and social
sciences.
In this study, computational techniques were used to understand and visualize
how the ancient Mesopotamian cuneiform script spread and was adapted across
modern-day Iraq and Syria in the late third millennium BCE. A dataset was
curated of cuneiform sign values used to write Semitic words written on
tablets excavated from key geographic sites and used computational
techniques, including dimensionality reduction and clustering methods, to
achieve a broad view analysis of the curated data. These results directed
further inquiry, in which philological approaches were used to examine and
describe the linguistic environments in which each significant value was
attested.
This investigation into early cuneiform syllabaries has demonstrated the
advantages of using computational methods of analysis on humanities data. It
also highlighted the relative strengths and weaknesses of different methods
of analysis on this type of data: the results of the hierarchical clustering
and PCA were able to provide a more detailed and accurate analysis of the
data than the phylogenetic estimation method was. The results of this study
help in understanding the broad view of cuneiform script spread and
adaptation; however, there are ways such a study could be more rigorous. For
example, it would be preferable to use the encoded cuneiform signs as the
input data instead of using reconstructed syllabic sign values, since the
step of interpreting each sign value may introduce additional human error.
Currently, however, there is not a readily usable infrastructure to do this.
The curation of a digital database of cuneiform signs, encoded in Unicode,
along with all possible sign values for each region and time period would be
beneficial to this and other types of studies. Ongoing efforts by the
Unicode consortium have so far developed fonts for the following script
forms: Proto-Cuneiform (late fourth millennium BC), Early Dynastic cuneiform
(first half of the third millennium BC), and Neo-Assyrian cuneiform (first
half of the first millennium BC), but regional or site-specific variations
are not yet included.
10.2 Assyriological Implications
The advancement of digital tools to answer questions in the humanities is
indeed an exciting prospect for the study of the ancient Near East, a field
whose progress has long been hampered by the destruction of cultural
heritage and political turmoil in Iraq and Syria. With the use of new
methods and technologies, advancement in the field is not only possible, but
will likely produce fruitful and interesting results that will bring
Assyriology into the forefront of digital humanities scholarship.
This study attempts to give a broader view of the spread and adaptation of
cuneiform across a large geographic area as opposed to examining each site
individually as an isolated case. Furthermore, a complete, comprehensive
syllabary allows us to determine whether an experimental phase in the
writing system occurred at each site. The presence of this sort of
experimentation helps us ascertain whether the cuneiform script was fully
adopted by each city in Syria, inclusive of all its orthographic tendencies,
or whether each city’s writing system underwent a phase of experimentation
to create a slightly different result. Preliminary evidence suggests that
there were clear deviations in the syllabaries of Syria from normative
Mesopotamian cuneiform, which indicates that scribes experimented with the
writing system more during this period than during later periods. Precisely
what this can tell us about the linguistic nature of third millennium Syria
should be examined through further study.
The adaptation and use of cuneiform in Syria provides an interesting case
study for examining how people interacted with logosyllabic and syllabic
writing systems. The preliminary evidence suggests that in the third
millennium, particularly at sites farther away from the control of the
Mesopotamian core cities, scribes were more innovative in their adaptation
and use of syllabic values; there are clear deviations in the Akkadian
syllabaries of Syria from normative Mesopotamian cuneiform, and also
inconsistencies in the sign values and number of signs used syllabically
across each of the sites investigated. This suggests that, while there must
have been a more prescriptive educational approach to learning the cuneiform
signs themselves and their Sumerian values, during this time period a
prescribed method of writing and adapting cuneiform to write Akkadian was
not included in the scribal curriculum. This lead to different adaptations
of the cuneiform script and to slight variations in the number, types, and
values of syllabic signs used in the individual syllabaries from sites in
Syria.
11. Future Applications
This preliminary investigation into early cuneiform syllabaries has demonstrated
the strengths of data mining and computational methods of analysis. These
methodologies can be readily expanded and adapted to related and un-related
areas of research within the field of Assyriology; four such areas are outlined
below.
11.1 A more comprehensive investigation into third millennium Akkadian
This project could readily be expanded to include the corpora from all sites
that produced cuneiform texts in the third millennium. This would have
comprised 19 sites in total, including the ten that compose that data
sources for this thesis. The additional nine sites include: Nagar, Shehna /
Shubat-Enlil, Umma, Shuruppak, Abu Salabikh, Nippur, Girsu, Umm al-Jir, and
Susa. As was explained in the introduction of this thesis, many of these
sites were unable to be included as a part of the current work for a number
of practical reasons: (1) inaccessibility to the tablets due to their
location in museums in Syria, Iraq, or Turkey; (2) a lack of published
photographs, hand drawings, or transliterations and transcriptions of the
inaccessible texts; and (3) irrelevance of the texts to this study of the
adaptation of cuneiform to write Semitic languages (in the case of Susa).
Going forward, it would be possible to expand and update this syllabary to
include the orthography of each sign, either in all cases, or, more likely
at first, just in the cases where they obviously appear to differ
drastically. In the process of its expansion, the syllabary can then be
digitized to create a free and easily accessible database.
11.2 Applications of this methodology to all East Semitic dialects
Another application of this methodology would be to examine the development
of the East Semitic dialects across the entire history of the cuneiform
script[27]. The wealth of written sources left behind by the
cuneiform cultures comprise a unique and comprehensive data set through
which we can understand the history and development of these dialects. These
developments and relationships have been determined largely through a
combination of textual analysis and the comparative method [Hetzron 1969]
[Hetzron 1976]
[Faber 1997]
[Huehnergard 2011] (Hetzron 1974; Hetzron 1976; Faber 1997;
Huehnergard 2011), but using computational methods to analyze the relevant
data can provide new insights into the evolution and spread of these
dialects[28]. This
study would likely examine the following East Semitic dialects:
- Eblaite (ca. 2350-2250 BC)
- Old Akkadian (ca. 2350-2200 BC)
- Ur III Akkadian (ca. 2100-2000 BC)
- Old Assyrian (ca. 1950-1850 BC)
- Old Babylonian (ca. 2000-1600 BC)
- Middle Assyrian (ca. 1400-1000 BC)
- Middle Babylonian (ca. 1400-1100 BC)
- Neo-Assyrian (ca. 911-612 BC)
- Neo-Babylonian (ca. 626-539 BC)
11.3 Comparing computational methods to find the optimal approach
The methodology used in this thesis has significant potential to be
informative and relevant not only for this project, but for other
Assyriological research projects as well. A thorough grasp on phylogenetic
analysis programs such as MacClade, Mesquite, and PAUP* in addition to the
programming languages R, Python, and Perl can enable researchers to build
custom code and pipelines specifically for the analysis of syllabaries,
orthographies, or other aspects of the cuneiform script. In terms of future
research, this will be particularly useful in identifying the definitive
syllabaries and sign lists for early phases of the cuneiform script – such
as Uruk and Ur III sign lists – as well as for little-understood relatives
to the cuneiform writing system, such as Proto-Elamite.
11.4 Applications to the problem of texts with no known provenance
The methodology used here has the potential to aid in providing provenance to
looted or misplaced tablets. To do this, a much larger dataset is required.
In particular, a large collection of digitized texts with adequate encoding
of the class of each sign (i.e., syllabic, logographic, determinative,
etc.). This dataset can rely largely on the texts published on the CDLI
database; computational methods could then be employed to identify the class
of the majority of signs attested in most time periods and genres. Once this
dataset is collected, principal component analysis can aid in the
identification of the distinctive signs or combinations of signs that, when
present, suggest a specific provenance.
12. Input Data
12.1 Phylogenetic Estimation
a. Unfiltered data
#NEXUS Begin taxa; Dimensions ntax=10; taxlabels Ebla Mari Nabada Tuttul Adab Eshnunna Kish Tutub Assur Gasur ; End; Begin data; Dimensions ntax=10 nchar=319; Format datatype=standard; Matrix Ebla 1 0 0 1 1 1 1 1 1 1 1 1 1 0 1 1 1 0 0 1 1 1 0 0 1 1 1 1 0 0 1 1 0 1 0 0 1 1 1 1 0 0 0 0 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 0 1 1 1 1 1 1 1 1 1 1 1 0 1 1 0 1 1 1 0 1 1 1 1 0 1 1 1 1 1 0 1 1 0 0 0 0 0 1 1 1 1 1 1 0 1 1 0 1 1 1 0 1 0 0 0 0 1 1 1 1 0 0 1 0 1 1 1 0 0 0 1 0 1 0 1 1 0 1 1 1 0 0 1 1 1 1 0 1 0 0 1 1 0 0 0 1 1 0 1 1 0 1 1 0 1 0 0 1 1 1 1 0 1 1 1 1 1 1 0 0 1 0 1 1 0 1 1 0 0 1 1 0 1 1 0 0 0 1 1 0 1 1 1 0 0 0 0 0 1 1 1 1 1 1 1 1 0 1 1 1 1 1 1 1 0 0 1 0 0 0 1 1 1 1 0 1 1 1 1 1 0 1 0 1 0 1 1 0 1 0 1 1 1 1 1 0 1 0 0 0 1 1 1 1 1 1 1 1 0 1 1 0 0 1 1 0 0 0 1 1 0 1 0 1 0 1 0 1 1 0 0 0 0 0 0 1 0 0 1 0 0 1 0 1 1 1 0 0 1 0 1 1 1 1 1 0 0 0 0 Mari 0 0 0 0 0 1 1 1 0 0 0 0 0 0 0 0 1 0 1 0 0 0 1 1 0 0 1 1 0 1 0 0 1 1 0 0 1 0 0 1 0 0 1 1 1 1 0 1 1 0 0 1 0 1 1 1 0 1 1 1 0 0 0 0 1 1 1 0 0 0 0 1 0 0 0 0 0 0 0 0 1 1 1 0 1 0 0 0 0 1 0 0 0 0 0 0 1 0 1 1 1 1 1 1 1 1 1 1 1 1 0 0 1 1 0 0 0 0 1 0 0 0 1 0 1 0 1 1 0 0 1 0 1 0 0 0 0 1 0 0 0 0 0 0 0 1 1 0 0 0 1 0 0 0 0 1 0 0 0 1 0 0 1 0 1 0 0 1 1 1 1 0 1 0 1 1 1 0 1 0 1 0 1 0 0 1 1 1 0 1 1 1 1 1 0 0 0 0 1 0 1 0 1 1 0 1 1 0 1 0 1 1 0 1 0 1 0 0 1 1 0 0 0 1 1 0 0 1 1 0 1 0 0 1 0 0 0 1 0 0 0 1 0 1 0 0 0 0 1 0 1 1 0 1 0 1 0 1 0 1 0 0 0 1 1 0 0 0 0 1 1 0 1 0 1 1 1 1 0 1 1 0 0 1 1 1 0 1 0 0 0 0 1 0 0 1 1 1 1 0 0 1 1 0 0 1 0 1 1 0 1 1 1 1 0 0 0 0 0 Nabada 1 0 0 0 0 1 0 1 0 0 1 0 0 0 0 0 1 1 1 0 1 0 0 0 1 1 1 0 0 0 1 0 0 1 0 0 1 0 0 1 0 0 0 1 1 1 0 1 1 1 0 0 0 1 0 1 1 1 0 1 0 1 0 0 1 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 1 0 0 0 1 0 0 0 0 0 1 0 1 0 0 0 0 1 1 0 0 1 0 0 1 1 0 1 1 0 0 0 0 0 0 0 1 1 1 0 0 0 1 0 1 0 1 0 0 0 1 0 1 0 1 1 0 1 1 1 0 1 0 0 0 0 0 1 0 0 1 1 0 0 0 1 1 0 1 0 0 0 1 0 1 0 0 0 0 0 1 0 1 0 1 0 1 0 0 0 0 0 0 0 0 0 1 0 0 1 0 0 1 1 0 0 0 0 0 0 1 0 1 0 1 0 0 0 1 0 1 1 0 1 0 0 0 0 1 1 1 0 0 1 0 1 0 0 0 0 0 0 1 0 0 0 0 1 0 0 0 1 0 0 0 0 0 0 1 0 0 0 0 0 1 0 0 0 0 0 0 1 0 1 1 1 1 0 0 1 0 0 0 0 0 0 0 0 0 1 0 1 1 1 0 0 0 1 0 1 0 0 0 0 0 0 0 1 0 0 0 1 0 0 0 0 0 0 1 0 1 0 0 1 1 0 0 0 0 Tuttul 1 0 0 0 0 1 1 1 1 1 0 0 1 1 1 0 1 0 1 1 0 0 1 1 0 0 1 0 0 0 1 0 0 1 1 0 1 0 0 1 1 1 0 0 1 1 0 1 1 0 0 0 0 1 0 1 1 1 0 0 0 1 0 1 1 1 0 0 0 0 1 1 0 0 1 0 0 0 0 0 1 1 0 1 1 0 0 0 0 0 1 0 0 0 0 0 0 1 1 0 0 1 1 0 1 0 0 0 1 1 0 0 0 0 0 1 0 0 1 0 0 1 1 1 1 1 1 0 0 0 1 0 0 0 0 0 1 1 0 0 0 0 0 0 0 1 0 0 0 0 1 1 0 0 0 1 1 0 0 0 0 0 1 0 1 1 1 0 0 1 1 0 1 0 1 0 0 1 0 0 0 0 1 1 1 0 1 1 0 1 1 0 1 1 0 0 1 1 0 0 1 0 1 0 0 0 0 0 1 0 1 1 0 1 0 0 0 0 1 1 0 0 0 1 1 0 0 0 0 1 0 0 1 1 0 0 1 0 0 0 1 1 0 1 1 1 1 0 1 1 0 0 0 1 1 1 0 0 0 1 0 0 1 0 0 1 0 1 1 1 1 1 1 0 1 0 0 1 0 1 1 0 1 1 1 1 0 1 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 1 1 0 0 1 0 1 1 1 1 0 0 0 0 0 Adab 1 1 1 0 0 1 1 1 0 0 0 0 0 0 0 0 1 0 0 0 1 0 0 1 1 1 1 0 0 0 1 0 0 0 1 1 0 0 0 1 0 0 0 0 0 1 0 0 1 1 0 0 0 0 0 1 0 1 0 0 0 0 1 1 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 1 0 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 1 0 1 0 1 1 1 0 0 1 1 1 0 0 0 0 0 1 1 1 1 0 0 0 0 1 0 0 1 1 0 1 1 1 0 1 0 0 0 1 0 0 1 0 1 0 1 0 0 1 1 0 1 0 1 1 0 0 1 0 0 0 1 1 0 0 1 0 1 0 1 1 1 1 0 0 1 0 1 1 1 1 1 1 0 0 1 0 1 0 1 1 0 1 0 1 0 1 1 1 0 0 0 1 1 0 0 0 0 0 0 0 0 0 0 0 0 1 1 0 0 1 0 1 0 0 0 1 1 0 0 1 0 1 1 0 0 0 0 0 1 0 1 0 1 1 1 0 0 0 1 1 0 0 0 0 0 1 0 1 0 0 0 1 1 1 0 1 0 0 0 0 0 0 0 0 0 0 1 1 0 1 1 0 1 1 0 0 1 1 0 1 0 0 0 0 0 0 1 Eshnunna 0 0 0 0 0 1 1 0 1 0 1 0 0 0 0 0 1 0 1 0 0 0 0 0 1 1 1 0 1 1 1 0 1 1 1 1 1 0 0 1 0 0 0 0 1 1 0 1 1 1 0 0 0 1 0 1 0 1 0 0 0 0 1 0 1 1 0 0 1 0 1 0 0 0 0 0 1 0 0 0 1 0 1 0 1 0 0 0 0 0 1 0 0 0 0 1 0 1 1 0 0 1 0 1 1 0 0 1 0 1 0 0 0 0 0 0 0 0 1 0 0 0 1 0 0 0 1 0 1 1 1 0 0 0 0 0 0 1 1 1 0 0 0 0 0 1 0 0 0 0 1 1 1 0 1 1 1 0 0 1 0 0 1 0 1 0 1 0 0 1 1 0 1 0 1 1 0 1 1 1 0 0 1 1 0 1 1 0 0 1 1 1 1 1 0 0 1 0 1 0 1 1 0 1 0 0 0 0 1 1 1 1 0 1 1 1 0 1 1 1 1 0 0 1 1 1 0 0 0 0 1 0 1 0 0 0 1 1 0 0 0 1 0 1 0 0 0 1 1 0 0 1 0 1 1 0 0 1 1 1 1 0 0 1 1 1 1 0 0 1 1 1 1 0 0 0 1 0 0 1 0 1 1 1 1 0 1 1 0 0 1 0 0 1 0 0 1 0 0 0 1 1 1 0 1 1 1 0 1 0 1 1 0 1 1 0 0 0 0 Kish 0 0 0 0 0 1 1 1 1 0 1 0 0 0 0 0 1 0 0 0 0 0 0 1 0 1 1 0 0 1 1 0 0 1 0 0 1 0 0 1 0 0 0 0 1 1 0 1 1 1 0 0 0 0 0 1 0 1 1 0 0 1 0 0 1 1 0 1 0 1 1 0 0 0 0 0 0 0 0 0 1 0 1 0 1 0 0 0 0 0 1 0 0 1 1 1 0 1 0 0 0 1 0 1 1 1 0 1 1 1 0 0 0 0 0 0 0 0 1 0 1 0 1 0 0 1 1 1 0 0 1 0 0 1 0 0 0 1 1 0 1 0 0 0 0 1 1 0 0 0 0 1 1 0 1 1 0 0 0 1 0 0 1 1 1 0 0 0 0 1 1 0 1 0 1 0 0 0 0 1 0 1 1 1 1 0 1 0 0 1 1 1 1 1 1 1 1 1 1 0 1 0 1 0 0 0 1 1 1 0 1 1 0 1 0 0 0 1 1 1 1 0 0 1 0 1 0 0 0 0 1 0 0 1 0 0 1 0 1 0 0 1 0 1 0 0 0 1 1 0 0 1 1 1 1 0 1 1 1 0 0 1 0 0 1 1 1 0 0 1 1 1 1 0 0 0 0 0 0 1 1 1 1 1 1 0 1 1 0 0 1 0 0 1 0 1 0 1 0 1 0 1 1 0 0 0 0 0 1 0 1 1 1 1 1 1 0 0 0 Tutub 1 0 0 0 0 1 1 1 1 1 1 1 0 0 0 0 1 0 0 0 0 0 1 0 0 0 1 0 0 1 1 0 0 1 1 0 1 0 0 1 0 0 0 0 1 1 0 1 1 1 0 0 0 1 0 1 0 1 0 0 0 1 1 1 1 0 1 0 1 1 1 0 0 0 0 1 1 0 0 0 1 0 1 0 1 0 0 0 0 0 1 1 0 0 0 1 0 1 1 0 0 1 1 0 1 0 0 1 1 1 1 0 1 0 1 0 0 0 1 0 0 0 1 0 1 1 1 0 1 0 1 1 0 0 0 0 1 1 1 1 1 0 0 0 0 1 1 0 0 0 0 1 1 0 1 1 0 0 0 1 1 0 1 0 1 0 0 0 0 1 1 1 1 0 1 1 0 0 1 0 0 0 1 1 0 0 1 0 0 1 1 1 1 1 0 0 1 0 1 0 1 1 1 1 0 0 1 0 1 0 1 1 0 1 1 1 0 0 1 1 1 1 0 1 0 1 0 0 0 0 0 0 0 1 1 0 1 1 0 1 0 1 0 0 1 0 1 0 1 0 0 1 1 1 1 0 0 1 1 0 0 1 1 1 1 1 1 0 1 1 1 1 0 0 0 0 0 1 0 1 0 1 1 1 0 1 0 1 0 0 1 1 0 1 0 0 0 0 0 1 1 1 1 0 0 1 1 0 1 0 1 1 1 1 0 0 0 0 0 Assur 0 0 0 0 0 1 0 1 0 0 1 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 1 0 0 0 1 0 0 1 0 0 0 0 0 1 0 0 0 0 0 1 0 1 1 0 0 0 0 0 0 1 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 1 0 0 1 0 0 0 0 0 0 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 Gasur 0 0 0 1 0 1 1 1 0 1 1 0 0 0 0 0 1 0 0 0 0 0 0 0 0 1 1 0 0 1 1 0 0 1 1 0 1 0 0 1 0 0 0 0 1 1 0 1 1 1 0 1 0 0 0 1 0 1 1 0 0 0 1 0 1 1 0 0 1 0 0 1 0 0 0 0 0 0 1 0 1 0 0 0 1 0 0 0 0 0 1 0 1 0 1 0 0 1 0 0 0 1 0 1 1 0 0 1 1 0 0 0 1 0 0 0 0 0 1 0 0 0 1 0 0 0 1 0 1 0 1 0 0 0 0 0 0 1 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 1 1 0 1 0 1 0 0 0 0 1 1 0 1 0 1 1 0 0 0 1 0 1 1 1 1 0 1 1 0 0 1 0 1 0 0 0 0 0 1 0 1 0 1 0 0 0 1 1 1 0 1 1 0 1 1 0 1 0 1 1 1 0 0 1 1 1 0 0 0 0 1 0 1 0 0 0 0 1 0 0 0 1 1 1 1 1 0 1 1 0 0 1 0 1 1 0 0 0 1 0 1 0 0 1 0 1 1 0 0 0 1 1 0 0 0 0 0 1 0 1 1 1 0 1 0 1 1 1 0 0 1 0 1 0 0 0 0 1 0 1 0 1 0 0 1 0 0 0 1 0 1 1 0 1 1 0 1 1 0 ; End;
b. Filtered data
\#NEXUS Begin taxa; Dimensions ntax=9; taxlabels Ebla Mari Nabada Tuttul Adab Eshnunna Kish Tutub Gasur ; End; Begin data; Dimensions ntax=9 nchar=188; Format datatype=standard; Matrix Ebla 1 1 1 1 1 1 1 1 1 0 1 1 0 0 1 1 1 0 0 0 0 1 0 1 1 1 1 1 1 1 0 1 1 1 1 1 1 1 1 1 1 0 0 1 1 1 1 1 1 1 0 0 0 1 1 1 1 1 0 1 1 1 1 0 1 1 0 1 1 0 0 0 0 1 1 1 0 1 1 0 1 0 1 1 1 0 0 1 1 1 0 0 1 1 0 1 1 0 0 1 0 1 1 0 1 0 1 0 1 0 1 1 1 1 0 0 0 1 1 1 1 1 1 0 0 1 1 1 1 1 1 1 1 0 1 1 0 1 1 1 1 1 0 1 0 0 0 1 1 1 1 1 1 1 1 0 1 1 0 0 1 0 0 0 1 0 0 1 0 0 0 0 1 0 0 1 0 0 1 0 1 1 0 1 1 1 1 1 Mari 0 0 1 0 0 0 0 0 0 1 0 0 1 1 0 0 1 1 1 0 0 1 1 1 0 1 1 1 0 1 1 0 0 0 1 1 1 0 0 0 0 1 0 0 0 1 1 1 0 0 0 0 0 0 1 1 1 1 1 1 1 1 1 1 0 0 0 1 0 1 0 0 0 1 0 0 0 0 0 0 1 1 0 1 0 0 0 0 0 1 0 0 1 1 1 1 0 1 0 1 0 1 0 0 1 1 1 1 1 0 0 1 0 1 1 1 0 0 0 1 0 0 0 1 0 1 0 1 0 1 0 0 1 0 0 0 0 1 1 0 1 0 1 0 1 0 1 0 0 0 1 1 0 0 0 0 1 1 0 1 1 1 1 1 0 0 1 1 0 0 1 0 1 1 1 1 0 0 1 1 0 1 0 1 1 1 1 0 Nabada 1 0 0 0 0 1 0 0 0 1 0 1 0 0 1 1 0 0 0 0 0 1 1 1 0 0 1 0 1 0 1 1 0 0 1 0 0 0 0 0 0 0 0 0 1 1 0 0 1 0 1 0 0 1 1 0 0 0 0 1 1 1 0 0 1 1 0 1 0 0 0 0 0 1 1 1 0 1 1 0 0 0 1 1 1 0 0 1 1 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 1 0 1 0 0 0 0 1 0 0 0 0 0 0 0 1 0 0 1 0 1 0 0 1 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 1 0 1 1 1 1 0 0 1 0 0 0 0 0 0 0 1 1 0 0 0 0 0 0 0 0 1 0 0 0 1 0 0 0 0 1 0 0 1 1 Tuttul 1 0 1 1 1 0 0 1 1 1 1 0 1 1 0 0 0 0 0 1 0 1 0 1 0 0 1 0 1 0 0 1 0 1 1 1 0 0 0 0 1 1 1 0 0 1 1 0 1 0 0 0 0 1 1 0 0 1 0 0 0 1 1 0 0 0 0 1 1 0 0 0 0 0 0 0 1 0 0 0 1 0 0 1 1 0 0 1 0 0 0 1 0 0 1 0 1 0 0 0 0 1 1 1 0 1 1 0 1 1 1 0 0 1 0 0 0 0 0 0 0 0 0 1 0 0 1 1 1 0 0 0 1 1 1 1 0 0 0 0 1 1 1 0 0 0 1 0 0 1 0 0 1 0 1 1 1 1 1 1 1 0 1 1 0 1 1 1 0 0 0 0 0 0 0 0 0 1 0 0 1 1 0 1 1 1 1 0 Adab 1 0 1 0 0 0 0 0 0 0 0 1 0 1 1 1 0 0 0 1 1 0 0 0 1 0 0 0 0 0 0 0 1 1 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 1 0 0 0 0 0 0 0 1 1 1 0 0 1 1 0 0 0 0 1 1 1 1 0 0 0 1 1 0 0 0 1 0 1 0 0 1 0 0 1 0 0 0 1 1 0 0 0 0 1 1 1 0 1 1 1 1 1 0 0 0 1 1 0 0 1 0 0 0 0 0 1 1 0 1 0 0 0 1 0 1 0 1 1 0 0 0 0 0 1 0 1 0 1 1 1 0 0 0 1 1 0 0 0 1 0 0 0 1 1 0 0 0 0 0 0 0 1 1 0 1 1 1 1 0 0 1 0 0 0 Eshnunna 0 0 1 1 0 1 0 0 0 1 0 0 0 0 1 1 0 1 1 1 1 1 0 1 1 0 1 0 0 0 0 0 1 0 1 1 0 0 1 0 1 0 0 0 1 1 0 1 1 0 0 0 1 1 1 0 0 0 1 0 1 0 1 0 0 0 0 0 0 0 1 1 0 0 0 0 0 1 1 0 1 0 0 1 1 1 1 1 0 1 0 1 0 0 1 0 1 1 1 0 0 1 1 0 1 0 1 1 1 1 0 1 1 0 1 0 0 1 1 1 1 1 0 1 1 1 1 0 1 1 0 0 1 0 0 0 1 0 1 0 1 1 0 0 1 1 1 1 0 0 1 1 1 1 0 0 1 1 1 1 0 1 0 0 1 1 1 0 1 1 0 1 0 1 0 0 0 1 1 1 1 1 1 1 1 0 1 1 Kish 0 0 1 1 0 1 0 0 0 0 0 0 0 1 0 1 0 1 0 0 0 1 0 1 1 0 0 0 0 1 0 1 0 0 1 1 0 1 0 1 1 0 0 0 0 1 0 1 1 0 0 1 1 1 0 0 0 0 1 1 1 1 1 0 0 0 1 0 1 1 0 0 0 0 0 0 0 1 0 1 1 1 0 0 1 1 1 0 0 1 0 0 0 0 0 0 0 0 1 0 1 1 1 1 0 0 1 1 1 1 1 1 0 1 0 1 1 0 0 0 1 1 0 0 1 1 0 1 1 0 1 0 1 0 0 0 1 0 1 1 1 1 0 1 1 1 0 0 1 0 0 1 1 1 0 0 1 1 1 1 0 0 0 1 1 1 1 0 1 1 0 1 1 0 1 0 1 0 1 1 0 0 0 1 1 1 1 1 Tutub 1 0 1 1 1 1 1 0 0 0 0 0 1 0 0 0 0 1 0 1 0 1 0 1 1 0 1 0 0 0 0 1 1 1 1 0 1 0 1 1 1 0 0 1 1 1 0 1 1 1 0 0 1 1 1 0 0 1 0 0 1 1 1 1 0 0 0 1 1 0 1 0 1 0 0 0 1 1 1 1 1 1 0 0 1 1 1 0 0 1 1 0 0 0 1 0 0 1 0 0 0 1 1 0 0 0 1 1 1 1 0 1 1 1 1 1 0 0 1 1 0 1 1 0 1 0 0 1 1 1 0 1 0 1 0 1 0 0 1 1 1 1 0 0 1 1 0 0 1 1 1 1 1 1 0 1 1 1 1 0 0 0 1 0 1 1 0 1 0 1 0 1 0 0 0 0 1 1 1 1 0 1 1 1 1 1 1 0 Gasur 0 1 1 0 1 1 0 0 0 0 0 0 0 0 0 1 0 1 0 1 0 1 0 1 1 1 0 0 0 1 0 0 1 0 1 1 0 0 1 0 0 1 0 0 0 1 0 0 1 0 1 1 0 1 0 0 0 0 1 0 1 1 0 1 0 0 0 0 0 0 1 0 0 0 0 0 0 1 1 0 0 0 0 0 0 0 0 0 0 1 1 0 0 0 1 0 0 0 1 0 1 1 1 1 0 1 0 0 0 0 0 1 0 1 0 1 1 0 1 0 0 1 0 1 1 1 1 0 0 1 0 0 1 1 1 0 1 0 1 0 1 1 0 0 0 1 0 1 0 0 1 0 1 1 0 0 0 1 1 0 0 0 1 1 1 0 0 1 1 1 1 0 0 0 1 0 1 0 1 0 1 0 0 1 1 0 1 1 ; End; \pagebreak
12.2 RStudio
\begin{verbatim} library("NMF") library("FactoMineR") library("data.table") library("ggplot2") library("cowplot") library("ggdendro") library("pvclust") #data.df<-data.frame(syl_matrix_forR_090416)[1:319,5:14] data.df<-data.frame(syl_matrix_forR_090416)[1:319,4:14] rownames(data.df)<-data.df$Sign.Value data.df<-data.df[,2:11] data.df<-data.df[rowSums(data.df)>0,] #unfiltered w/ Assur colnames(data.df)<-gsub("Esznunna","Eshnunna",colnames(data.df)) rclust<-hclust(dist(data.df,method="manhattan"), method="ward.D2") cclust<-hclust(dist(t(data.df),method="manhattan"), method="ward.D2") aheatmap(data.df,color='grey:2', Rowv=rclust,breaks=c(-0.05,0.5,1.05), labRow=NULL,main="Hierarchical clustering of sites by syllabic value attestations \n", legend=FALSE, fontsize=14, cexCol = 0.8) #unfiltered data w/ Assur forpca<-t(data.df) answer<-PCA(forpca,ncp=10,graph=FALSE) pc.eig.df<-data.frame(answer$eig) ggplot(data=pc.eig.df[c(1:9),], aes(x=gsub("comp","Comp.", rownames(pc.eig.df[c(1:9),])), y=percentage.of.variance)) + geom_bar(stat="identity" +xlab("\nPrincipal Component") +ylab("Percentage of Variance") +ggtitle("Variance distribution across principal components derived from viable syllabic values") coord.rs.df<-data.frame(answer$ind$coord) gsub("Esznunna","E?nunna",row.names(coord.rs.df)) Geography<-factor(c("Syria", "Syria", "Syria", "Syria", "Southern Mesopotamia", "Southern Mesopotamia", "Southern Mesopotamia", "Southern Mesopotamia", "Northern Mesopotamia", "Northern Mesopotamia"), levels=c("Southern Mesopotamia", "Northern Mesopotamia", "Syria")) Period<-factor(c("Old Akkadian\n(ca. 2350-2200 BC)", "\nUr III/ Shakkanakku\n(ca. 2100-2000 BC)", "Old Akkadian\n(ca. 2350-2200 BC)", "\nEarly Old Babylonian\n(ca. 2000-1900 BC)\n", "\nOld Akkadian & Ur III\n(ca. 2350-2200, 2100-2000 BC)", "Old Akkadian \n(ca. 2350-2200 BC)", "Old Akkadian\n(ca. 2350-2200 BC)", "Old Akkadian\n(ca. 2350-2200 BC)", "Old Akkadian\n(ca. 2350-2200 BC)", "Old Akkadian\n(ca. 2350-2200 BC)"), levels=c("Old Akkadian\n(ca. 2350-2200 BC)", "\nOld Akkadian & Ur III\n(ca. 2350-2200, 2100-2000 BC)", "\nUr III / Shakkanakku \n(ca. 2100-2000 BC)", "\n Early Old Babylonian \n(ca. 2000-1900 BC)\n")) ggplot(data=coord.rs.df, aes(x=Dim.1, y=Dim.2))+geom_point(aes(shape=Period, fill=Geography, color=Geography),size=4)+scale_shape_manual (values=c(21,22,23,24))+xlab("Princ. Comp. 1")+ylab("Princ. Comp. 2") +geom_text(label=gsub("Esznunna","E?nunna", row.names(coord.rs.df)), nudge_y=0.7) #filtering out hapax signs and allsites signs data2.df<-data.df[rowSums(data.df)>1 & rowSums(data.df)<9,-9] colnames(data2.df)<-gsub("Esznunna","Eshnunna",colnames(data2.df)) colnames(data.df)<-gsub("Esznunna","Eshnunna",colnames(data.df)) #convert table into final table for thesis for hapax signs data.onesite.df<-data.df[rowSums(data.df)==1,] data.onesite.df$Site<-"DUMMY" data.onesite.df[data.onesite.df$Ebla==1,]$Site<-"Ebla" data.onesite.df[data.onesite.df$Mari==1,]$Site<-"Mari" data.onesite.df[data.onesite.df$Nabada==1,]$Site<-"Nabada" data.onesite.df[data.onesite.df$Tuttul==1,]$Site<-"Tuttul" data.onesite.df[data.onesite.df$Adab==1,]$Site<-"Adab" data.onesite.df[data.onesite.df$E?nunna==1,]$Site<-"E?nunna" data.onesite.df[data.onesite.df$Kish==1,]$Site<-"Kish" data.onesite.df[data.onesite.df$Tutub==1,]$Site<-"Tutub" #data.onesite.df[data.onesite.df$Assur==1,]$Site<-"Assur" data.onesite.df[data.onesite.df$Gasur==1,]$Site<-"Gasur" final.onesite.dt<-data.table(data.onesite.df, final.onesite.dt<-final.onesite.dt[order(Site)] keep.rownames=TRUE)[,.(rn, Site)] write.table(final.onesite.dt, file="hapax_signs.xls", quote=FALSE, sep="\t", row.names=FALSE) #table of signs that occur at all sites data.allsites.df<-data.df[rowSums(data.df[,-9])==9,-9] write.table(rownames(data.allsites.df), file="allsites_signs.xls", quote=FALSE, sep="\t", row.names=FALSE) #table of filtered data write.table(rownames(data2.df),file="data_filtered.xls", quote=FALSE, sep="\t", row.names=TRUE, col.names=TRUE) write.table(data2.df,file="data_filtered.xls",sep="\t") #hierarchical clustering colnames(data2.df)<-gsub("Esznunna","Eshnunna",colnames(data2.df)) rclust<-hclust(dist(data2.df,method="manhattan"), method="ward.D2") cclust<-hclust(dist(t(data2.df),method="manhattan"), method="ward.D2") aheatmap(data2.df,color='grey:2', Rowv=rclust,breaks=c(-0.05,0.5,1.05), labRow=NULL, legend=FALSE, fontsize=14, cexCol = 0.8) result <-pvclust(data2.df, method.dist="manhattan", method.hclust="ward.D2", nboot=10000) plot(result) #PCA colnames(data2.df)<-gsub("E?nunna","Eshnunna",colnames(data2.df)) forpca<-t(data2.df) answer<-PCA(forpca,ncp=3,graph=FALSE) pc.eig.df<-data.frame(answer$eig) ggplot(data=pc.eig.df[c(1:8),], aes(x=gsub("comp","Comp.", rownames(pc.eig.df[c(1:8),])), y=percentage.of.variance)) + geom_bar(stat="identity")+xlab("\nPrincipal Component") + ylab("Percentage of Variance")+ggtitle("Variance distribution across principal components \n derived from informative syllabic values \n") coord.rs.df<-data.frame(answer$ind$coord) #meta.pca.df<-meta.df[rownames(coord.rs.df),] #PC1 gsub("Esznunna","Eshnunna", row.names(coord.rs.df)) Geography<-factor(c("Syria", "Syria", "Syria", "Syria", "Southern Mesopotamia", "Southern Mesopotamia", "Southern Mesopotamia", "Southern Mesopotamia", "Northern Mesopotamia"), levels=c("Southern Mesopotamia", "Northern Mesopotamia", "Syria")) Period<-factor(c("Old Akkadian\n(ca. 2350-2200 BC)", "\nUr III / Shakkanakku \n(ca. 2100-2000 BC)", "Old Akkadian\n(ca. 2350-2200 BC)", "\nEarly Old Babylonian\n(ca. 2000-1900 BC)\n", "\nOld Akkadian & Ur III \n(ca. 2350-2200, 2100-2000 BC)", "Old Akkadian\n(ca. 2350-2200 BC)", "Old Akkadian \n(ca. 2350-2200 BC)", "Old Akkadian\n(ca. 2350-2200 BC)", "Old Akkadian\n(ca. 2350-2200 BC)"), levels=c("Old Akkadian \n(ca. 2350-2200 BC)", "\nOld Akkadian & Ur III\n(ca. 2350-2200, 2100-2000 BC)", "\nUr III / Shakkanakku\n(ca. 2100-2000 BC)", "\nEarly Old Babylonian \n(ca. 2000-1900 BC) \n")) ggplot(data=coord.rs.df, aes(x=Dim.1, y=Dim.2))+geom_point(aes (shape=Period, fill=Geography, color=Geography),size=4) +scale_shape_manual(values=c(21,22,23,24))+xlab("Princ. Comp. 1")+ylab("Princ. Comp. 2")+geom_text(label=gsub ("Esznunna","Eshnunna", row.names(coord.rs.df)),nudge_y=0.5) ggplot(data=coord.rs.df, aes(x=Dim.2, y=Dim.3))+geom_point(aes (shape=Period, fill=Geography, color=Geography),size=4) +scale_shape_manual(values=c(21,22,23,24))+xlab( "Princ. Comp. 2")+ylab("Princ. Comp. 3+geom_text(label=gsub ("Esznunna","Eshnunna", row.names(coord.rs.df)), nudge_y=0.5) ggplot(data=coord.rs.df, aes(x=Dim.1, y=Dim.3))+geom_point(aes(shape=Period, fill=Geography, color=Geography),size=4)+scale_shape_manual (values=c(21,22,23,24))+ xlab("Princ. Comp. 1")+ylab("Princ. Comp. 3"+geom_text(label=gsub("Esznunna","Eshnunna", row.names (coord.rs.df)), nudge_y=0.5) #Extract attestations that define principal components 1-3 signs<-data.table(answer$var$contrib, keep.rownames = TRUE) #dim1 excel table and visualization dim1<-signs[order(-abs(Dim.1))][,1:2,with=FALSE] ggplot(data=dim1, aes(x=seq(from=1, to=188,by=1),y=Dim.1)) +geom_point(size=1, color="black")+xlab("\nSyllabic value index ordered by loadings on the first principal component")+ylab("Loadings on the first principal component\n")+ggtitle("The distribution of loadings for syllabic values suggests that\nloadings greater than 1.2 should be further examined.") +geom_hline(yintercept = 1.25) dim1<-dim1[dim1$Dim.1>1.1,] data2.dim1.df<-data2.df[dim1$rn,] colnames(data2.dim1.df)<-gsub("Esznunna","Eshnunna",colnames(data2.dim1.df)) rclust<-hclust(dist(data2.dim1.df,method="manhattan"), method="ward.D2") cclust<-hclust(dist(t(data2.dim1.df),method="manhattan"), method="ward.D2") anngeo<-list(Geography=Geography) aheatmap(data2.dim1.df,color='grey:2', Rowv=rclust,breaks=c(-0.05,0.5,1.05), annCol = anngeo, legend=FALSE,main="Hierarchical clustering of sites by syllabic value attestations\nimportant in the first principal component", fontsize=10,treeheight=10, cexCol = 1, cexRow=2) write.table(dim1, file="dim1_signloadings.xls", quote=FALSE, sep="\t", row.names=FALSE) #dim2 excel table and visualization dim2<-signs[order(-abs(Dim.2))][,c(1,3),with=FALSE] ggplot(data=dim2, aes(x=seq(from=1, to=188,by=1),y=Dim.2)) + geom_point( size=1, color="black")+xlab("\nSyllabic value index ordered by loadings on the second principal component")+ylab("Loadings on the second principal component\n")+ggtitle("The distribution of loadings for syllabic values suggests that\nloadings greater than 1.1 should be further examined.")+geom_hline(yintercept = 1.1) dim2<-dim2[dim2$Dim.2>1.1,] data2.dim2.df<-data2.df[dim2$rn,] colnames(data2.dim2.df)<-gsub("Esznunna","Eshnunna",colnames(data2.dim2.df)) rclust<-hclust(dist(data2.dim2.df,method="manhattan"), method="ward.D2") cclust<-hclust(dist(t(data2.dim2.df),method="manhattan"), method="ward.D2") annperiod<-list(Period=gsub("^ ","",gsub("\n"," ", Period))) aheatmap(data2.dim2.df,color='grey:2', Rowv=rclust,breaks=c(-0.05,0.5,1.05), annCol=annperiod, legend=FALSE,main="Hierarchical clustering of sites by syllabic value attestations\nimportant in the second principal component", fontsize=10,treeheight=10, cexCol = 1, cexRow=3) write.table(dim2, file="dim2_signloadings.xls", quote=FALSE, sep="\t", row.names=FALSE) #dim3 excel table and visualization dim3<-signs[order(-abs(Dim.3))][,c(1,4),with=FALSE] ggplot(data=dim3, aes(x=seq(from=1, to=188,by=1),y=Dim.3)) +geom_point(size=1, color="black")+xlab("\nSyllabic value index ordered by loadings on the third principal component")+ylab("Loadings on the third principal component\n")+ggtitle("The distribution of loadings for syllabic values suggests that\nloadings greater than 1.3 should be further examined."+geom_hline(yintercept = 1.3) dim3<-dim3[dim3$Dim.3>1.3,] data2.dim3.df<-data2.df[dim3$rn,] colnames(data2.dim3.df)<-gsub("Esznunna","Eshnunna",colnames(data2.dim3.df)) rclust<-hclust(dist(data2.dim3.df,method="manhattan"), method="ward.D2") cclust<-hclust(dist(t(data2.dim3.df),method="manhattan"), method="ward.D2") aheatmap(data2.dim3.df,color='grey:2', Rowv=rclust,breaks=c(-0.05,0.5,1.05), legend=FALSE,main="HierarchicaL clustering of sites by syllabic value attestations \nimportant in the third principal component", fontsize=10, treeheight=10, cexCol = 1, cexRow=2) write.table(dim3, file="dim3_signloadings.xls", quote=FALSE, sep="\t", row.names=FALSE)
Notes
[1] This article is based on certain aspects of my doctoral
research completed at the University of Oxford in 2016.
[2] For the results of this
analysis, see the forthcoming publication of my doctoral thesis (Hawkins
in preparation).
[3] Not included in this study are the following
nine sites: Nagar, Sheḫna / Shubat-Enlil, Umma, Shuruppak, Abu Salabikh,
Nippur, Girsu, Umm al-Jir, and Susa.
[4] This was
obtained from the Cuneiform Digital Library Initiative (www.cdli.ucla.edu), unless
otherwise stated.
[5] http://virgo.unive.it/eblaonline/cgi-bin/home.cgi
[6] These genres are based on those provided in the Cuneiform
Digital Library Initiative Database.
[7] ca. 2350-2200 BC
[8] The majority of the texts from Ebla used here are lexical
and administrative. As an attempt to address the possibility that
the larger number of lexical texts from Ebla could skew the results
of the analysis, the data sets were filtered to exclude any
hapax sign values, or values that occurred at only
one site (with the majority of these hapax signs coming
from Ebla). For a further explanation of the filetered and
unfiltered datasets, see Section 4.2 below. For more about the
numbers of texts attested within each genre, see the following
resources: Krebernik (1982-1983), Conti (1990), and the Ebla Digital
Archives.
[9] ca. 2100-2000 BC
[10] ca. 2000-1900 BC
[11] Southern Mesopotamia.
[12] These are the numbers of texts from the Old Akkadian
and Ur III periods, respectively.
[13] These are the numbers of texts within
each genre from the Old Akkadian and Ur III periods, respectively.
[14] Northern Mesopotamia.
[15] For an introduction to
phylogenetic techniques, see [Nichols and Warnow 2008].
[16] See Section 12 below for the code used for the
phylogenetic analysis, hierarchical clustering, and principal component
analysis.
[17] See [Swofford et al. 1996, 415–24] for a full
discussion of different parsimony methods.
[18] Other types of consensus
trees are majority consensus and greedy consensus. According to [Nichols and Warnow 2008, 773], “the majority consensus tree
contains those edges whose corresponding bipartitions appear in
strictly more than half of the input trees, and the greedy consensus
tree is formed by computing the majority consensus and then refining
the tree by adding bipartitions from the input trees. By
construction, the strict consensus tree is the least resolved, the
greedy consensus tree is the most resolved, and the majority
consensus is in between these two trees with respect to resolution.
Also, however, the greedy consensus tree refines the majority
consensus tree, and the majority consensus tree refines the strict
consensus.”
[19] Ebla, Mari, Nabada, Tuttul, Adab, Eshnunna, Kish, Tutub,
Assur, and Gasur.
[20]
Identifying an outgroup is desirable but not necessary. For more about
outgroups and ingroups, see [Swofford et al. 1996].
[21] See Hawkins in
preparation for a further discussion of this finding [Hawkins forthcoming].
[22] For more about clustering techniques, see L.
Kaufman and P. J. Rousseeuw 1990 [Kaufman and Rousseeuw 1990].
[23] Clusters with a p-value of 95% or higher are considered
to be very strong, while p-values higher than 90% are considered strong [Suzuki and Shimodaira 2014, 6].
[24] For more about principal component
analysis, see [Jolliffe 2002]
[Wold et al. 1987].
[25] This is explored
further in a forthcoming publication [Hawkins forthcoming].
[26] This is explored
further in the forthcoming publication of my doctoral dissertation [Hawkins forthcoming]).
[27] One of the two prominent languages recorded in these
documents was Akkadian – which is, in fact, not a single language but an
umbrella term for a series of closely related Semitic dialects that were
written and spoken primarily in the regions of modern-day Iraq, Syria,
and Turkey from around 2500 BC until 70 AD [Cooper 1996, 37]. The Akkadian dialects, along with their relative
Eblaite, form the entirety of the eastern branch of the Semitic language
family.
[28] The data of this study would consist of the Swadesh
200-word list [Swadesh 1955] (Swadesh 1955).
Works Cited
Atkinson and Gray 2005 Atkinson, Q. D. and Gray,
R. D. “Curious parallels and curious
connections–phylogenetic thinking in biology and historical
linguistics.”
Systematic Biology 54, no. 4 (2005): 513-526.
Barbrook et al. 1998 Barbrook, A. C. et al.
“The phylogeny of the Canterbury Tales.”
Nature 394 (1998): 839.
Bastin 1983 Bastin, Y. La
finale verbale-ide et l’imbrication en bantou. Tervuren: Musée Royal
de l’Afrique Centrale (1983).
Biggs 1973 Biggs, R. D. “On
Regional Cuneiform Handwritings in Third Millennium Mesopotamia.”
Orientalia 42 (1973): 39–46.
Conti 1990 Conti, G. “Il
sillabario della quarta fonte della lista lessicale bilingue
Eblaita.”
Miscellanea Eblaitica 3. Ed. by P. Fronzaroli. Vol.
3. Quaderni di Semistica 17 . Firenze: Dipartimento di Linguistica Università di
Firenze (1990).
Cooper 1996 Cooper, J. S. “Sumerian and Akkadian.”
The world’s writing systems. Ed. P. T. Daniels and
W. Bright. Oxford University Press (1996).
Cysouw et al. 2006 Cysouw, M., Wichmann, S., and
Kamholz, D. “A critique of the separation base method for
genealogical subgrouping with data from Mixe-Zoquean.”
Journal of Quantitative Linguistics 13, no. 2-3
(2006): 225–264.
Faber 1997 Faber, A. “Genetic Subgrouping of the Semitic Languages.” The Semitic Languages. Ed. by R. Hetzron. London: Routledge (1997): 3–15.
Felsenstein 2005 Felsenstein, J. PHYLIP (phylogeny inference package). Department of
Genome Sciences, University of Washington. Distributed by author (2005).
Goloboff et al. 2003 Goloboff, P., Farris, J.,
and Nixon, K. “TNT: Tree Analysis Using New
Technology.” Program and documentation available from the authors
(2003).
Gray 2003 Gray, R. D. and Atkinson, Q. D. “Language-Tree Divergence Times Support the Anatolian Theory of
Indo-European Origin.”
Nature. 426, 435-439 (2003).
Hawkins forthcoming Hawkins, L. F. The Adaptation of Cuneiform to Write Semitic: an examination
of the use of syllabic sign values in late third and early second millennium
Mesopotamia and Syria. Wilbour Studies in Egyptology and
Assyriology: Lockwood Press (in preparation).
Hetzron 1969 Hetzron, R. “La division des langues sémitiques.” Actes du premier congrès international de linguistique sémitique et chamito-sémitique, Paris 16-19 juillet, 1969. Ed. by A. Caquot and D. Cohen. Paris: Mouton (1974): 181–194.
Hetzron 1976 Hetzron, R. “Two principles of genetic reconstruction.” Lingua 38.2 (1976): 89–108.
Holden 2002 Holden, C. J. “Bantu language trees reflect the spread of farming across sub-Saharan
Africa: a maximum-parsimony analysis.”
Proceedings of the Royal Society of London B: Biological
Sciences 269, no. 1493 (2002): 793–799.
Holden et al. 2005 Holden, C. J., Meade, A., and
Pagel, M. “Comparison of maximum parsimony and Bayesian
Bantu language trees.”
The Evolution of Cultural Diversity: a phylogenetic
approach. Ed. by R. Mace, C. J. Holden, and S. Shennan. London, UK:
University College London Press (2005): 53–66.
Huehnergard 2011 Huehnergard, J. A Grammar of Akkadian. 3rd ed. Scholars Press: Atlanta, GA (2011).
Ismail et al. 1996 Ismail, F. et al. Administrative Documents from Tell Beydar (Seasons
1993-1995). Subartu 2. Turnhout: Brepols (1996).
Jolliffe 2002 Jolliffe, I. Principal Component Analysis. John Wiley and Sons, Ltd (2002).
Kaufman and Rousseeuw 1990 Kaufman, L. and P. J. Rousseeuw. Finding Groups in Data: an Introduction to Cluster Analysis. Wiley, 1990.
Kitchen et al. 2009 Kitchen, A., Ehret, C.,
Assefa, S., and Mulligan, C. J. “Bayesian Phylogenetic
Analysis of Semitic Languages Identifies an Early Bronze Age Origin of
Semitic in the Near East.”
Proceedings of the Royal Society of London B: Biological
Sciences, 276 (2009): 2703-2710.
Krebernik 1982/3 Krebernik, M. “Zu Syllabar und Orthographie der lexikalischen Texte aus Ebla.
Teil 1; Teil 2 (Glossar).”
Zeitschrift für Assyriologie und Vorderasiatische
Archäologie 72-73 (1982): 178–236; (1983): 1–47.
Krebernik 2001 Krebernik, M. “Ausgrabungen in Tall Bi’a/Tuttul - II: Die altorientalischen
Schriftfunde.”” WVDOG 100 (2001).
Limet 1976 Limet, H. Textes
administratifs de l’époque des Šakkanakku. Archives royales de Mari,
Transcriptions et traductions XIX. = ARMT; ersch. 1977. Paris: Geuthner (1976).
Maddison and Maddison 2001 Maddison, W. P. and
Maddison, D. R. Mesquite: a modular system for evolutionary
analysis. http://mesquite.biosci.arizona.edu/mesquite/mesquite.html
(2001).
Marten 2006 Marten, L. “Bantu
classification, Bantu trees and phylogenetic methods.”” In: Foster,
Peter and Renfrew, Colin, (eds.), Phylogenetic Methods and
the Prehistory of Languages. Cambridge: McDonald Institute for
Archaeological Research (2006): 43-55.
Nakhleh et al. 2005a Nakhleh, L., Ringe, D., and
Warnow, T. “Perfect phylogenetic networks: a new methodology
for reconstructing the evolutionary history of natural languages.”
Language 81 (2005): 382–420.
Nakleh et al. 2005b Nakhleh, L. et al. “A comparison of phylogenetic reconstruction methods on an
Indo-European dataset.”
Transactions of the Philological Society 103, no. 2
(2005): 171–192.
Nichols and Warnow 2008 Nichols, J. and Warnow,
T. “Tutorial on computational linguistic phylogeny.”
Language and Linguistics Compass 2, no. 5 (2008):
760–820.
Platnick and Cameron 1977 Platnick, N. I. and
Cameron, H. D. “Cladistic methods in textual, linguistics,
and phylogenetic analysis.”
Systematic Biology 26, no. 4 (1977):
380-385.
Rexovà et al. 2003 Rexovà, K., Frynta, D., and
Zrzavỳ, J. “Cladistic analysis of languages: Indo-European
classification based on lexicostatistical data.”
Cladistics19, no. 2 (2003): 120–127.
Rexovà et al. 2006 Rexovà, K., Bastin, Y., and
Frynta, D. “Cladistic analysis of Bantu languages: a new
tree based on combined lexical and grammatical data.”
Naturwissenschaften 93, no. 4 (2006): 189–194.
Skelton 2008 Skelton, C. “Methods of using phylogenetic systematics to reconstruct the history of the
Linear B script.”
Archaeometry 50, no. 1 (2008): 158–176.
Suzuki and Shimodaira 2014 Suzuki, R and
Shimodaira, H. pvclust: An R package for hierarchical
clustering with p- values. Tech. rep. Division of Mathematical
Science, Graduate School of Engineering Science, Osaka University (2014).
Swadesh 1955 Swadesh, M. “Towards greater accuracy in lexicostatistic dating.” International Journal of American Linguistics 21.2 (1955): 121–137.
Swofford 2001 Swofford, D. L. “PAUP*: Phylogenetic analysis using parismony (and other
methods).”
4.0.b5 (2001).
Swofford et al. 1996 Swofford, D. et al. “Phylogenetic inference.”
Molecular Systematics. Ed. by D. M. Hillis, C.
Moritz, and B. K. Mable. 2nd ed. Vol. 2. Sinauer: Sunderland, MA (1996):
407–514.
Wold et al. 1987 Wold, S., Esbensen, K., and Geladi,
P. “Principal Component Analysis.”
Chemometrics and intelligent laboratory systems 2,
no. 2-3 (1987): 37–52.
URL: http://www.digitalhumanities.org/dhq/vol/12/1/000368/000368.html
Comments: dhqinfo@digitalhumanities.org
Published by: The Alliance of Digital Humanities Organizations and The Association for Computers and the Humanities
Affiliated with: Digital Scholarship in the Humanities
DHQ has been made possible in part by the National Endowment for the Humanities.
Copyright © 2005 -
Unless otherwise noted, the DHQ web site and all DHQ published content are published under a Creative Commons Attribution-NoDerivatives 4.0 International License. Individual articles may carry a more permissive license, as described in the footer for the individual article, and in the article’s metadata.
Comments: dhqinfo@digitalhumanities.org
Published by: The Alliance of Digital Humanities Organizations and The Association for Computers and the Humanities
Affiliated with: Digital Scholarship in the Humanities
DHQ has been made possible in part by the National Endowment for the Humanities.
Copyright © 2005 -
Unless otherwise noted, the DHQ web site and all DHQ published content are published under a Creative Commons Attribution-NoDerivatives 4.0 International License. Individual articles may carry a more permissive license, as described in the footer for the individual article, and in the article’s metadata.
