DHQ: Digital Humanities Quarterly
2009
Volume 3 Number 2
Volume 3 Number 2
Vive la Différence! Text Mining Gender Difference in French Literature
Abstract
In this study, a corpus of 300 male-authored and 300 female-authored French literary and historical texts is classified for author gender using the Support Vector Machine (SVM) implementation SVMLight, achieving up to 90% classification accuracy. The sets of words that were most useful in distinguishing male and female writing are extracted from the support vectors. The results reinforce previous findings from statistical analyses of the same corpus, and exhibit remarkable cross-linguistic parallels with the results garnered from SVM models trained in gender classification on selections from the British National Corpus. It is found that female authors use personal pronouns and negative polarity items at a much higher rate than their male counterparts, and male authors demonstrate a strong preference for determiners and numerical quantifiers. Among the words that characterize male or female writing consistently over the time period spanned by the corpus, a number of cohesive semantic groups are identified. Male authors, for example, use religious terminology rooted in the church, while female authors use secular language to discuss spirituality. Such differences would take an enormous human effort to discover by a close reading of such a large corpus, but once identified through text mining, they frame intriguing questions which scholars may address using traditional critical analysis methods.
Amanda Bonner: What I said was true, there's no difference between the sexes. Men, women, the same.
Adam Bonner: They are?
Amanda Bonner: Well, maybe there is a difference, but it's a little difference.
Adam Bonner: Well, you know as the French say...
Amanda Bonner: What do they say?
Adam Bonner: Vive la difference!
Amanda Bonner: Which means?
Adam Bonner: Which means hurrah for that little difference. (Adam's Rib, 1949)
Introduction
Attempts to identify and characterize differences between male and female discourse
have utilized methods such as close reading, sociolinguistic modeling [Tannen 1994], statistical analysis [Olsen 2004], [Olsen 2005], and, more recently, machine learning [Koppel 2002], [Argamon 2003]. The machine learning approach is closely related to purely statistical analysis
methods; both approaches exploit differences in aggregate word frequencies to highlight
differences between male and female authors in content or style. One advantage of
machine learning over simpler forms of statistical analysis lies in its creation of
a predictive model of testable accuracy, that can be used to assign gender labels
to samples of unknown category, or, as in this study, interrogated to reveal the features
most useful in such a classification. The resultant weighted wordlists can be used
to support or weaken an existing hypothesis about differences between the corpora,
or suggest new directions for investigation, whether by additional machine learning
or other, more traditional, critical methods.
This study was based on the same male and female corpora used by Olsen in previous
statistical analyses [Olsen 2004], [Olsen 2005]. The female corpus was assembled first, due to the more limited digital collection
of women's writing at our disposal. 300 texts roughly balanced by genre, collection
and time period were chosen, from among texts by French women writers available to
us. For each of the 300 texts by 67 female authors (18.5 million words), we selected
the chronologically closest male document available in that same genre and, when possible,
same collection, leading to a comparison corpus of 300 texts by 170 male authors (27
million words). As noted by Olsen [Olsen 2004], although these texts range from the 12th - 20th centuries, the samples are largely
drawn from the 18th-early 20th centuries with strongest representation in the 19th
century, owing to the predominance of romantic novelists in the available collections
of female writing. The sample is also skewed by a disproportionate number of works
by several notable authors, in particular George Sand, with 77 works. Two subsets
of the main corpora, each containing 92 documents selected from either the male- or
female-authored set, were also selected in an effort to avoid the “Sand Effect.”
Comparison With Previous Research
Because we are working with the same corpus previously subjected to a purely statistical
analysis [Olsen 2004], [Olsen 2005], we can bring machine learning tools to bear on the questions posed by that work
and directly compare our results. Machine learning allows us the possibility of approaching
the issue of male and female authorship from a different angle, with a set of metrics
for success fundamentally different from those afforded by traditional text analysis
methods and statistical inquiries. We ask an SVM model to learn, to the best of its
ability, to discriminate between male- and female-authored documents by feeding it
labeled examples of each, and applying an algorithm designed to generate predictive
models by exploiting generalizable differences in word frequencies between documents
in each set. The models give us quantitative feedback regarding their accuracy in
their task, and expose their methods by outputting lists of the words which were their
input, ranked and weighted as being predictive of one gender or the other. While these
metrics do not assure us of an intellectually satisfying outcome from a literary critical
viewpoint, they provide a good test of the validity of our process of analysis.
Because machine learning algorithms are fundamentally rooted in the exploitation of
differential distributions of features (in our case, words), we would expect to see
many of the same words appear as highly weighted features in our machine learning
results that Olsen found to be significant in his statistical analysis. However, we
would not expect the lists to be identical because there are additional factors that
influence SVM trained weights that are not captured by differential frequency statistics
or other statistical measures such as information gain (IG). Differential frequency
and IG are innate properties of an individual word's distribution between sub-corpora,
whereas an SVM weight has meaning only within the context of a particular model generated
by the learning algorithm, and must be considered in relation to the weights of other
features in that model. Differential rates and IG may simply be calculated according
to a set formula with unvarying results, whereas SVM weights are heuristically assigned
and refined by the learning algorithm in a search for maximum performance on the classification
problem.
Information gain and other statistical measures of distribution are commonly used
as heuristics for reducing feature set dimensionality and for setting initial weights
for machine learning algorithms, but there is no guarantee that all words with highly
differential frequencies in the corpora will be assigned high weights by the machine
learner in the final model. SVM produces two weighted sets of words, male and female,
which, taken together, are maximally effective (to the extent of the ability of the
algorithm to produce an optimal solution) at discriminating between texts from the
two corpora. Words which might exhibit interesting distributions but which do not
fit well into a particular model will not be assigned high weights and will escape
our notice. Therefore, it is useful to perform a variety of machine learning runs,
find what works, and search for common threads in the results. Ultimately, results
must find support from a knowledgeable reading of the texts and be fitted with a critical
hypothesis to be of great interest from the literary scholar's point of view, although
predictive models may have practical uses, such as adding guessed metadata to unclassified
documents, independent of their critical value or validity.
Experimental Design
The machine learning algorithm chosen for this classification task is an SVM implementation
called SVMLight [Joachims 1999]. SVM has proven to be a model well-suited for text classification, and our initial
tests showed that SVMLight achieved the best accuracy in classification among learning
algorithm implementations at our disposal, including naive Bayesian and decision tree
learners. The SVMLight implementation is freely available and includes key capabilities
such as cross-validated accuracy measures via leave-one-out estimation and the ability
to extract the weights assigned to each feature. The ability to interrogate the model
in this way is essential, because without it we would learn nothing about what word
usage patterns distinguish male writing from female writing, merely that such a distinction
can be learned with a particular degree of accuracy. A black-box model may be adequate
for industrial applications, where the goal is to classify unclassified instances
with a certain accuracy, but in this experiment, where the correct classification
is already known for all texts, we are far more interested in picking apart the constructed
model to determine the orientation and magnitude of the weights of individual words.
For our preliminary experiments, we prepared 8 sets of vectors, comprised of the two
collections (the full 600 document corpus and 184 document subset) in four versions
each: the surface form of the words, the lemmas, the parts of speech (POS) of the
words as assigned by TreeTagger, and a simplified part of speech grouping, with broader
categories (POSgroup). Each matrix consisted of either 600 or 184 vectors, labeled
with 1 for male-authored and -1 for female-authored documents. For a look at the generic
data preparation process for text classification, see [ARTFL 2008].
Machine Learning Runs
We then trained SVMLight on each matrix, and obtained the accuracies given in Tables
1 and 2, after cross-validation. Surface form and lemma accuracies cluster around
85%, which means that overall, the models generated by SVMLight can correctly predict
the gender of the author about 85% of the time. This is a significant result and indicates
that the model has indeed found generalizable differences between the texts in the
two corpora. The differences in accuracy between the surface and lemma forms of the
words are insignificant, and the POS and POSgroup accuracy differences are generally
quite slight as well. The most notable distinction is that POS/POSgroup accuracies
are consistently much lower than word/lemma accuracies. The former hover around 70%
accuracy, which we have adopted as the borderline for a significant result on a binary
classification problem. 70% accuracy is not a particularly compelling result on a
“coin-flip” problem, because it shows only 20% improvement over the agreement expected by random
chance. Naturally, the more accurate our model is, the more importance we can attach
to the words the model weights toward each author gender.
| Word | Lemma | PoS | PoSgroup | |
| Male | 88.3% | 87.3% | 73.0% | 69.7% |
| Female | 83.3% | 84.4% | 75.7% | 78.7% |
| All | 85.7% | 85.9% | 74.4% | 74.2% |
Table 1.
Preliminary results: 2x300 document sample | Word | Lemma | PoS | PoSgroup | |
| Male | 91.3% | 92.4% | 73.9% | 73.9% |
| Female | 81.5% | 81.5% | 78.3% | 69.6% |
| All | 86.4% | 87.0% | 76.1% | 71.7% |
Table 2.
Preliminary results: 2x92 document sampleIn order to test whether our accuracies were an artifact of the classifier used, rather
than demonstrative of true differences between our corpora, we performed the same
experiment but with each document randomly labeled as male or female, regardless of
true author gender. Over multiple runs, the classifier never achieved more than 50%
accuracy in this random falsification experiment, so we can be confident that SVMLight
cannot reliably distinguish between and random sub-corpora grouping in this corpus.
We can try to learn from our failures here. The fact that SVMLight cannot construct
a very accurate prediction model based on POS vectors is a kind of weak evidence against
any theory of gendered authorship that holds that men and women speak radically different
languages. If, in fact, men and women used the basic building blocks of language in
substantially different ways, we might expect to see strong mechanical differences
between male and female writing reflected in POS usage rates that the model could
exploit to make accurate classifications. That such differences do not widely obtain
in this corpus is strongly suggested by the inability of SVMLight to construct a very
accurate model to distinguish between the gendered corpora on that basis. Of course,
this does not rule out mechanical and stylistic differences that aren't reflected
in the simple metric of POS frequencies, but it does suggest a base level of linguistic
similarity between the two classes.
Based on these initial results, we decided to proceed with further experiments using
the surface forms of the words, that being the simplest method and tied for most accurate
with the lemmatized forms. All runs cited hereforth were executed within the PhiloMine
data mining extensions to the PhiloLogic text search engine [PhiloMine 2007], and are based on vectors of surface forms, and in all cases we achieve an accuracy
greater than 70%, most often between 80 and 90%. Now that we were comfortable that
the accuracy of our models were significant enough to indicate real differences between
our corpora, we investigated the internals of those models to determine where they
get their predictive power. We began by extracting the weights assigned to each word
in the 2 x 300 surface form features SVMLight model, and sorting them in descending
order of magnitude. Words oriented toward male authorship are scored as positive decimals,
while those pointing toward female authorship are negative decimals. We obtained the
weights of the most influential words in the model, given in Table 5.
Our first impulse when examining the feature list was to scan for the presence of
“shibboleth” words that trivially identify some subset of works as definitively male- or female-authored,
either because they are explicit markers of author gender (such as metadata tags inadvertently
retained in the document), or because they are features that occur in only one or
a relative handful of works that are homogeneous for author gender. Such terms are
gifts to the machine learner, greedily seized upon by our classification model but
unlikely to generate any penetrating insight for the scholar. Proper names are the
prime example of such features, and we saw several in Table 5, Consuelo being the highest-ranked of these. We eliminated terms like Consuelo (present in a number of works by Sand) from the input our model receives by stipulating
that we will only use words that occur in more than a certain percentage of documents
in the corpora. Constructing new vectors using only words that occur in at least 5%
of the documents in the combined male and female corpora, we ran the analysis again
and extracted the weights for the word given in Table 6. Consuelo is gone; a few proper names remain lower on the list, but since they occur in at
least 5% of all documents, they may be of broad enough interest to retain.
The highest-ranked words in each category are common function words — pronouns, articles,
quantifiers, adpositions, common verb forms of
être
and
avoir
— likely to occur frequently in texts of either gender. Several patterns are evident.
The female preference for pronouns is quite marked; {
elle, vous, lui, me, ma, moi, mon, il, m', je, toi, tu, votre
} all appear in the top 200 features weighted toward female authors. This is not an
unexpected finding given the observation of Olsen [Olsen 2005] of a usage rate for these terms among female authors that is nearly 1.5 times that
of male authors. Also of note is the female preference for terms of negative polarity:
{
impossible, ne, ni, pas, personne, sans
}. On the male side, we note the preference for determiners such as {
un, le, des, du, les, ce, ces, cette
} and quantifiers such as {
un, deux, une, quelque(s), mille
}.
These results are striking in that they replicate almost exactly those of a similar
analysis of female- and male- authored texts in the British National Corpus (BNC)
[Argamon 2003]. The primary findings of that study were that females tended to use both more personal
pronouns such as {I, you, she, her, their, myself, yourself, herself} and negative particles such as {not, no, never}, and that males used more determiners such as {a, the, that, these} and quantifiers such as {one, two, more, some}. Although reflexive pronouns are not expressed by a single word in French as they
are in English, and hence do not show up distinctly in our analysis, the rest of the
findings match almost exactly. The issue of reflexive pronouns might be investigated
in subsequent tests by using word bigrams as features rather than, or in addition
to, single words. The strong agreement between these two experiments is all the more
remarkable for the very different texts involved in these two studies. Argamon et
al. [Argamon 2003] analyzed 604 documents from the BNC spanning an array of fiction and non-fiction
genres from a variety of sources, all in Modern British English (post-1960), whereas
the current study looks at predominantly fictional French works from the 12th - 20th
centuries. This cross-linguistic similarity could be supported with further research
in additional languages.
Somewhat lower down the list than the function words, we start to encounter content
words, and some of the same phenomena noted by Olsen in his statistical analyses are
apparent. {
aime, aimer, aimable
} all show up on the female list, which squares with Olsen's observation of a use
rate of aim* by females roughly at roughly 1.5 times that of males across all genres. In noting
the female preference for personal pronouns and emotional language, Olsen argues “[female] space may be characterized by a more personal, emotive and interactive frame
that is not explained by differences in genre or period”
[Olsen 2005], and we can support this hypothesis with our machine learning analysis.
Having found support for previous findings in Argamon [Argamon 2003] and Olsen [Olsen 2005], [Olsen 2004], we looked for additional patterns in the heavily weighted terms for each gender.
Our corpus spans a wide time range, and we are most interested in discovering patterns
that persist across that span. To that end, we split our 600 document combined male-
and female-authored corpus into two time range sub-corpora, one comprised of all documents
from 1100-1799 (244 documents) and one for all other documents, spanning 1800-2000
(356 documents). Separate SVMLight training runs were performed on each time range
corpus using those words that appeared in at least 20% of all documents in that corpus,
and the 500 highest-weighted features for male- and female-authored documents from
each period were extracted. Taking the union of the two male lists and the two female
lists, we found 153 male and 192 female features that are among the top 500 features
for both time period runs. No single text or group of contemporary texts can force
the inclusion of any word into these merged lists because each text occurs in only
one time range sub-corpus, so inclusion on both lists indicates a widespread and enduring
trend in usage. The relatively common words in Table 3 are consistently useful in
distinguishing male and female French writing over a wide time range, and must reflect
real differences in style or content between the genders in the corpora.
| 153 persistent features in Male-authored documents: 1, a, abord, action, affaire, ajouta, amie, article, au, aura, auteur, autour, autre, aux, avons, bas, bouche, bras, c, capitaine, cent, chacun, chair, champ, charles, chez, christ, ciel, cinq, comment, comtesse, contre, corps, coup, coups, crime, côté, d', des, deux, diable, dis, docteur, doigts, dont, doute, droite, du, entre, est, face, fait, façon, femme, feu, fin, fit, fois, foule, gens, gros, haut, histoire, homme, hé, hôtel, ils, in, jacques, jean, juge, jusqu', la, laquelle, le, les, leurs, ligne, long, lorsque, main, mains, maîtresse, messieurs, mis, mit, moins, monseigneur, monsieur, montre, mot, même, nez, nom, nombre, nos, oeil, oeuvres, ordre, oreille, ou, oui, où, par, passage, pied, pieds, présente, président, prêtre, quatre, quelqu', quelque, quelques, question, qui, quoi, reprit, reste, rue, récit, saint, saints, salut, sang, second, seconde, selon, ses, seulement, simple, sire, soit, sous, sur, table, tirer, tour, toute, trente, trois, un, v, ventre, vers, vieux, village, vin, vingt, voici, y, yeux, à |
| 192 persistent features in Female-authored documents: 192 persistent features in Female-authored documents: absence, admiration, afin, agréable, ai, aimable, aime, aimer, aller, amitié, amour, anglais, angleterre, auguste, auprès, aurais, avais, avait, avec, avez, avoir, beaucoup, belle, bien, bonheur, bonne, brillante, but, cacher, car, caractère, celle, chagrin, chercher, chère, coeur, comprendre, compte, comte, confiance, conserver, cour, crois, destinée, disant, donner, douceur, douleur, doux, elle, elles, empêcher, encore, enfance, enfant, enfants, entièrement, envie, esprit, espérance, estime, eût, faisait, fallait, faut, fièvre, fleurs, france, frère, fût, gloire, goût, grande, grandes, généreux, henri, hiver, ici, il, imagination, impossible, inquiétude, inspire, inspirer, instant, intérêt, jamais, jardin, jours, liberté, lui, lumières, m, ma, mais, malgré, manière, manières, me, moi, mon, montrer, mère, ne, ni, nécessaire, opinion, parce, parler, parlez, passion, pauvre, pays, personne, personnes, petite, peut, peuvent, plaire, plaisir, pleurs, plusieurs, possible, pourquoi, pourrais, pouvait, prince, princes, princesse, pu, puisque, puissance, père, quand, que, quitter, regarder, reine, repos, retrouver, revenir, roi, sais, sait, sans, savoir, secret, sentiment, sentir, seule, si, son, souffrir, souvenir, souvent, soyez, suis, supporter, surprise, tant, toi, toujours, tous, toutes, trop, trouva, trouver, très, tu, utile, veux, vie, vit, vivre, voir, vois, vos, votre, voulait, voulut, vous, voyage, voyant, véritable, âme, éducation, égard, égards, émotion, épouser, était, êtes |
Table 3.
Features appearing in the top 500 highest-weighted in both time range models Within the male and female lists, it is possible to identify a number of interesting
semantic groupings of words. Reassuringly, the female pronouns and negative polarity
items and male quantifiers discussed earlier are still present. In addition, there
are a number of other semantic categories of words that appear to cohere:
| Enduring Male Terms | Enduring Female Terms |
|
|
Table 4.
Subjective thematic groups among the persistent featuresThe number of strongly cohesive thematic groupings that can be constructed from the
highly-weighted features that obtain in both time periods suggest that male and female
writers in the corpus exercise markedly different topic selection. Although the identification
of these persistent themes marks the endpoint of this machine learning analysis of
the corpus, the themes themselves form a natural starting point for a scholar interested
in pursuing the differences between male and female writing from a traditional literary
critical viewpoint. It would be quite interesting, for example, to explore why male
authors favor religious terminology rooted within the church, whereas female authors
spend more time discussing spirituality in a personal, more secular language. Similarly,
why should so many anatomical terms rank in the very top of male-weighted features,
and are they literal expressions of physicality, or rooted in metaphorical usage?
Clearly, these thematic groupings cannot be taken as definitive, universal statements
about gendered authorship, but they are clearly identifiable trends that provide a
neat snapshot of some basic differences between male and female authors, while suggesting
potentially fruitful areas for further analysis, either computer-assisted or using
traditional methods. Scholars intrigued by these questions could narrow the context
for a close reading by refining the text mining analysis, focusing on questions such
as which authors and works best exemplify the discovered trends, and which provide
exceptions and counter-examples.
Conclusion
Our research demonstrates the utility of using support vector machine models to find
contrasting features of male and female writing by interrogating the trained models
to identify patterns of word usage that distinguish the gendered corpora. We found
little advantage to using lemmatized forms of words as our features and a significant
disadvantage to using parts of speech, and therefore used the surface forms of the
words for the bulk of our research, achieving accuracies in classification between
80% and 90%. Of the words found to be most useful in distinguishing male and female
writing, several distinct functional and semantic groupings were identified. The more
personal and emotional frame of reference found in female authors' writing by Olsen
in his statistical analysis of the same corpus was supported by our machine learning
models. The marked male preference for determiners and female preference for personal
pronouns and negative polarity items was a particularly promising finding, as it echoes
very closely previous work by Argamon et al. [Argamon 2003] on a different corpus in a different language (excerpts from the English-language
British National Corpus). Among the other patterns we identified were a number of
cohesive semantic groupings of words that were consistently highly weighted towards
males or females across the wide time range of the corpus, such as anatomical and
religious terms favored by males, and familial and emotional vocabulary favored by
females. The close, contextual reading of a corpus of this magnitude could be the
life's work or more of a dedicated scholar, with no guarantee that such trends would
be salient enough to be noticed. Through the use of machine learning techniques, we
can efficiently analyze vast swathes of texts and achieve results that are interesting
and enlightening both in and of themselves, and as a spur to further research using
other critical methods.
| Male Features | Female Features | ||
| Word | Weight | Word | Weight |
| qui | 3.032 | elle | -4.270 |
| un | 2.706 | ne | -2.768 |
| à | 2.568 | vous | -2.256 |
| le | 2.512 | pas | -1.812 |
| des | 2.392 | et | -1.594 |
| du | 1.993 | avec | -1.435 |
| les | 1.847 | mais | -1.433 |
| au | 1.598 | lui | -1.365 |
| monsieur | 1.396 | était | -1.346 |
| est | 1.302 | si | -1.245 |
| deux | 1.264 | avait | -1.178 |
| de | 1.250 | me | -1.127 |
| sur | 1.033 | ma | -1.069 |
| a | 0.953 | pour | -0.952 |
| homme | 0.884 | sans | -0.811 |
| par | 0.867 | moi | -0.794 |
| ce | 0.746 | consuelo | -0.779 |
| madame | 0.690 | quand | -0.779 |
| d' | 0.656 | bien | -0.702 |
| une | 0.594 | roi | -0.676 |
| ces | 0.590 | l' | -0.666 |
| ses | 0.586 | il | -0.614 |
| dont | 0.566 | beaucoup | -0.570 |
| quelque | 0.554 | n' | -0.560 |
| femme | 0.535 | henri | -0.543 |
| ils | 0.528 | m' | -0.535 |
| où | 0.511 | jamais | -0.523 |
| tems | 0.496 | reine | -0.513 |
| charles | 0.493 | je | -0.482 |
| ou | 0.487 | princesse | -0.479 |
| autre | 0.451 | toujours | -0.470 |
| aux | 0.449 | car | -0.465 |
| yeux | 0.429 | ai | -0.462 |
| main | 0.417 | votre | -0.459 |
| fit | 0.392 | esprit | -0.453 |
| leurs | 0.386 | avais | -0.447 |
| quelques | 0.384 | m | -0.444 |
| leur | 0.380 | personne | -0.430 |
| cette | 0.379 | albert | -0.419 |
| fait | 0.379 | temps | -0.400 |
| après | 0.374 | mon | -0.393 |
| avois | 0.374 | bonne | -0.383 |
| reste | 0.363 | être | -0.381 |
| mille | 0.355 | dans | -0.379 |
| même | 0.327 | ça | -0.371 |
| saint | 0.326 | se | -0.365 |
| fille | 0.324 | liberté | -0.364 |
| francs | 0.309 | la | -0.360 |
| tout | 0.307 | âme | -0.356 |
| lettre | 0.299 | très | -0.356 |
| étoit | 0.298 | enfants | -0.349 |
| entre | 0.287 | peut | -0.347 |
Table 5.
Weights have been scaled to 10,000 times their original values for ease of reading| Male Features | Female Features | ||
| Word | Weight | Word | Weight |
| qui | 3.043 | elle | -4.291 |
| un | 2.716 | ne | -2.780 |
| à | 2.578 | vous | -2.265 |
| le | 2.522 | pas | -1.820 |
| des | 2.400 | et | -1.599 |
| du | 2.000 | avec | -1.441 |
| les | 1.856 | mais | -1.439 |
| au | 1.603 | lui | -1.366 |
| monsieur | 1.400 | était | -1.348 |
| est | 1.305 | si | -1.250 |
| deux | 1.269 | avait | -1.179 |
| de | 1.252 | me | -1.127 |
| sur | 1.037 | ma | -1.072 |
| a | 0.956 | pour | -0.956 |
| homme | 0.888 | sans | -0.814 |
| par | 0.870 | moi | -0.795 |
| ce | 0.749 | quand | -0.782 |
| madame | 0.690 | bien | -0.706 |
| d' | 0.657 | roi | -0.679 |
| une | 0.597 | l' | -0.668 |
| ces | 0.592 | il | -0.621 |
| ses | 0.587 | beaucoup | -0.572 |
| dont | 0.568 | n' | -0.564 |
| quelque | 0.555 | henri | -0.549 |
| femme | 0.537 | m' | -0.536 |
| ils | 0.530 | jamais | -0.526 |
| où | 0.513 | reine | -0.515 |
| tems | 0.498 | je | -0.483 |
| charles | 0.495 | princesse | -0.481 |
| ou | 0.488 | toujours | -0.471 |
| autre | 0.452 | car | -0.466 |
| aux | 0.450 | ai | -0.462 |
| yeux | 0.430 | votre | -0.460 |
| main | 0.418 | esprit | -0.455 |
| fit | 0.394 | avais | -0.447 |
| leurs | 0.387 | m | -0.445 |
| quelques | 0.386 | personne | -0.431 |
| cette | 0.381 | albert | -0.420 |
| leur | 0.381 | temps | -0.402 |
| fait | 0.380 | mon | -0.392 |
| après | 0.375 | bonne | -0.385 |
| avois | 0.375 | être | -0.380 |
| reste | 0.364 | dans | -0.378 |
| mille | 0.356 | ça | -0.375 |
| même | 0.329 | se | -0.366 |
| saint | 0.327 | liberté | -0.365 |
| fille | 0.325 | la | -0.358 |
| francs | 0.311 | très | -0.358 |
Table 6.
Weights have been scaled to 10,000 times their original values for ease of reading.Works Cited
ARTFL 2008 ARTFL Technical Report: “Creating Vectors for Text Classification Machine Learning,”
http://artfl.uchicago.edu/TechReports/VectorsForTextClassification
Argamon 2003 Argamon, S., Koppel, M., Fine, J. and Shimoni, A. “Gender, Genre, and Writing Style in Formal Written Texts”. Text 23(3), August 2003.
Joachims 1999 Joachims 1999 Joachims, T., “Making large-Scale SVM Learning Practical”. Advances in Kernel Methods - Support Vector Learning, B. Schölkopf and C. Burges and A. Smola (eds.). MIT Press, 1999.
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Tannen 1994 Tannen, Deborah. “Gender and Discourse”. New York: Oxford University Press, 1994.
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Copyright © 2005 - 2025
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 - 2025
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.
