Stylometry, that is the application of the study of linguistic style, offers a means of capturing the elusive character of an author’s style by quantifying some of its features. The basic stylometric assumption is that each writer has a “human stylome” [Van Halteren et al. 2005], that is a set of certain stylistic idiosyncrasies that define their style. Analysis based on stylometry is often used for Authorship Attribution (AA) tasks, since the main idea behind computationally supported AA is that by measuring some textual features, it is possible to distinguish between texts written by different authors [Stamatatos 2009]. One of the less investigated stylistic features is the way in which authors use words from a semantic point of view, e.g. if they tend to use more, when dealing with polysemous words, a certain sense over the others, or senses that differ (even slightly) from the one that is more commonly used (as it happens, typically, in poetry). It would then be interesting to discover if the semantics an author bestows to words is actually part of its “human stylome.” Computing semantics, though, is far from easy.

A possible approach to the analysis of this characteristic is to consider the textual contexts in which certain words appear. According to Distributional Semantics (DS), certain aspects of the meaning of lexical expressions depend on the distributional properties of such expressions, or better, on the contexts in which they are observed [Lenci 2008] [Miller and Charles 1991]. The semantic properties of a word can then be defined by inspecting a significant number of linguistic contexts, representative of the distributional behavior of such word.

In this work, we would like to investigate if the analysis of the distribution of words in a text can be exploited to provide a stylistic cue. In order to inspect that, we have experimented with the application of DS to the stylometric analysis of literary texts belonging to a corpus constituted by texts pertaining to the work of six Italian writers of the late nineteenth century. In the following, Section 2 both provides a brief survey on the works related to stylometry and introduces the fundamental Distributional Semantics concepts on which this works relies upon. Section 3 describes the approach together with the corpus used to conduct our investigation and Section 4 discusses results. Finally, Section 5 draws some conclusions and outlines some possible future works.

First, we want to specify that it is not our purpose to propose new ways to improve state-of-the-art AA algorithms. Indeed, our aim is just to verify the hypothesis that the distribution of words can provide an indication of a distributional stylistic fingerprint of an author. To do this, we have set up a simple classification task. Subsection 3.1 briefly depicts the data set we used, Section 3.2 describes why and how we chose the authors that would constitute our dataset and Section 3.3 depicts the steps implemented in our experiment.

Since we wanted to focus on the analysis of the semantic distribution of words, we decided to exclude any possible “lexical bias.” For this reason, we restricted the analysis on a common vocabulary, i.e. a vocabulary constituted by the intersection of the six authors’ vocabularies. In this way, we prevent our classifier to exploit, as a feature, the presence of words used by some (but not all) of the authors. Moreover, we removed from the RWPtest lists all those pairs of words occurring frequently together in the same context, since they might constitute a multiword expression that, once again, could be pertaining with the signature lexicon of each author. To remove them, we computed the number of times (#co-occ in Figure 4) they appeared together in the context window, as well as their total number of occurrences (#occa and #occb) and we excluded from the analysis those pairs for which the ratio between the number of co-occurrences and the total occurrences of the less frequent word was higher than the empirically set threshold of 0.5. The first two pairs of Figure 4 would be removed as probable multiword (PM column in Figure 4): “scoppio” (burst) and “risa” (laughter) could mostly co-occur in “scoppio di risa” (meaning “burst of laughter”) and the words “man” and “mano” (both meaning “hand”) could mostly co-occur in “man mano” (meaning “little by little,” or “progressively”). Finally, we reduced the size of the six RWPref and RWPtest lists by sorting them in decreasing order of the cosine value and then by keeping the pairs with the highest cosine, selected using a percentage parameter θ as a threshold. We chose to introduce the parameter θ for two reasons: first of all we wanted to avoid the classification algorithm to be disturbed by noisy (i.e. not significative) pairs which would not hold any relevant stylistic cue, also, we would like to ease a literary scholar in the interpretation of the results by having to analyze just a limited selection of (potentially) semantically related word pairs. For the last phase of our experiment, we defined a classification algorithm to test the effective presence of stylistic cues inside the obtained RWPtest lists. We defined a classifier using a nearest-cosine method to attribute each test list to an author. The method consisted in searching for a pair of words contained in the test list inside each reference list and incrementing by 1 the score of the author whose reference list included the pair with the more similar cosine value (i.e. having the minimum difference): the chosen author was the one with the highest score. Figure 5 shows the classification results for θ = 5%. As summarized in Figure 6, the correct classification of all RWPs in RWPtest lists has been obtained with a θ value of 5%. To help in interpreting the failure of the algorithm in classifying Tozzi’s test list for θ values lower than 5% (as shown in Figure 6) we calculated the cardinality of the RWPtest lists for each author with the change in θ value (Figure 7). It is possible to observe how the choice of θ influences the correct classification of Tozzi’s test list. Indeed, the use of a θ sense below 5% has the effect of remarkably reducing an already small test list (RWPtextTozzi) as shown in Figure 7. It is apparent that increasing the value of θ and consequently the number of significant RWPs that are analyzed, the system is able to correctly classify RWPtestTozzi (see the values in Tozzi’s row of Figure 6).

In this paper, we investigated the possibility that an analysis of the semantic distribution of words in a text can be potentially exploited to get cues about the style of an author. In order to validate our hypothesis, we conducted the first experiment on six different Italian authors. Of course, it is not our intent, with this paper, to define new methods for enhancing state-of-the-art authorship attribution algorithms. However, the obtained results seem to suggest that the way words are distributed across a text, can provide a valid stylistic cue to distinguish an author’s work. In light of what we have shown up to this point, the direction of our next steps can be twofold. On the one hand, our research will focus on detecting and providing useful indications about the style of an author. This can be done by highlighting, for example, atypical distributions of words (e.g. with contrastive methods) or by analyzing their distributional variability. Furthermore, it could be interesting to use a different distributional measure than the cosine, to test our hypothesis. On the other hand, it would be interesting to confront the computational task of authorship attribution, by measuring the effective contribution that a feature based on distributional semantics would provide to a canonical classification process. Also, as highlighted in Section 3.2.3, another interesting development of this work would regard the investigation of the ability of our method in recognizing the literary movement the texts pertain to, instead of the author that wrote them.