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Proceedings of the Third Italian Conference on Computational Linguistics CLiC-it 2016

Anna Corazza
Simonetta Montemagni
Giovanni Semeraro

Investigating the Application of Distributional Semantics to Stylometry

Giulia Benotto, Emiliano Giovannetti et Simone Marchi


The inclusion of semantic features in the stylometric analysis of literary texts appears to be poorly investigated. In this work, we experiment with the application of Distributional Semantics to a corpus of Italian literature to test if words distribution can convey stylistic cues. To verify our hypothesis, we have set up an Authorship Attribution experiment. Indeed, the results we have obtained suggest that the style of an author can reveal itself through words distribution too.

Texte intégral

1 Introduction

1Stylometry, 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 certain stylistic idiosyncrasies (a “human stylome” (Van Halteren et al., 2005)) that define their style. Analysis based on stylometry are often used for Authorship Attribution (AA) tasks, since the main idea behind computationally supported AA is that by measuring some textual features, we can distinguish between texts written by different authors (Stamatatos, 2009).

2One of the less investigated stylistic feature 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’s more commonly used (as it happens, typically, in poetry).

3A 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.

4In 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.

5In the following, Section 2 gives a short insight on the state of the art of computational stylistic analysis, Section 3 describes the approach together with the corpus used to conduct our investigation and Section 4 discuss about results. Finally, Section 5 draws some conclusions and outlines some possible future works.

2 State of the Art

6The very first attempts to analyze the style of an author were based on simple lexical features such as sentence length counts and word length counts, since they can be applied to any language and any corpus with no additional requirements (Koppel and Schler, 2004; Stamatatos, 2006; Zhao and Zobel, 2005; Argamon et al., 2007). Similarly, character measures have been proven to be quite useful to quantify the writing style (Grieve, 2007; De Vel et al., 2001; Zheng et al., 2006). Basically, a text can be viewed as a mere sequence of characters, so that various measures can be defined (including alphabetic, digit, uppercase and lowercase characters count, etc.). A more elaborate text representation method is to employ syntactic information (Gamon, 2004; Stamatatos et al., 2000; Stamatatos et al., 2001; Hirst and Feiguina, 2007; Uzuner and Katz, 2005). The idea is that authors tend to use similar syntactic patterns unconsciously. Therefore, syntactic information is considered a more reliable authorial fingerprint in comparison to lexical information.

7More complicated tasks such as full syntactic parsing, semantic analysis, or pragmatic analysis cannot yet be handled adequately by current NLP technologies for unrestricted text. As a result, very few attempts have been made to exploit high-level features for stylometric purposes. Perhaps the most important method of exploiting semantic information so far was described in (Argamon et al., 2007). This work was based on the theory of Systemic Functional Grammar (SFG) (Halliday, 1994) and consisted on the definition of a set of functional features that associate certain words or phrases with semantic information.

8The previously described features are application independent since they can be extracted from any textual data. Beyond that, one can define application-specific measures in order to better represent the nuances of style in a given text domain (such as e-mail messages, or online forum messages) (Li et al., 2006; Teng et al., 2004).

9To the best of our knowledge, the application of DS to the analysis of literary texts has been documented in a rather small number of works (Buitelaar et al., 2014; Herbelot, 2015). In both these works, DS is used as a theoretical basis in order to verify some hypotheses on specific semantic characteristics of poetic works. In more details, in (Buitelaar et al., 2014) the authors investigated through DS the influence of Lord Byron’s work on Thomas Moore trying to find a shared vocabulary or specific formal textual characteristics. In (Herbelot, 2015) it is argued how distributionalism can support the notion that the meaning of poetry comes from the meaning of ordinary language and how distributional representations can model the link between ordinary and poetic language. However, the role of DS in the study of a style of an author was not the aim of these works.

3 Experimental Setup

10First, 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, while Section 3.2 describes the steps implemented in our experiment.

3.1 Data Set Construction

11In order to build the reference and test corpora, we started from texts pertaining to the work of six Italian writers working at the turn of the 20th century, namely, Luigi Capuana, Federico De Roberto, Luigi Pirandello, Italo Svevo, Federigo Tozzi and Giovanni Verga. We chose contiguous authors in chronological sense, whose texts are available in digital format (in fact we could not do a similar survey on the narrative of the 90s because it is still under copyrights). Indeed, we used texts freely available for download from the digital library of the Manunzio project, via the LiberLiber website1. Since they were encoded in various formats, such as .epub, .odt and .txt, our pre-processing consisted in converting them all in .txt format and getting rid of all xml tags, together with footnotes and editors’ notes and comments.

3.2 Experiment Description

12According to Rudman (1997), a striking problem in stylometry is due to the lack of homogeneity of the examined corpora, in particular to the improper selection or fragmentation of the texts, that might cause alterations in the writers’ style. In order to create balanced reference corpora, i.e. covering all the authors’ different stylistic and thematic phases, for each author, as shown in Figure 1, we built a reference corpus as the composition of the 70% of each single work (usually a novel). The same technique was used to create the test corpus by using the remaining 30% of each work. Typical AA approaches consist in analyzing known authors and assigning authorship to previously unseen text on the basis of various features. Train and test sets should then contain different texts. Contrary to the classical AA task, our train and test sets contain different parts of the same texts. Indeed, with this experiment, we wanted to understand if the semantics that an author bestows to a word, is peculiar to his writing. To prove this, we wanted to cover all the different stylistic and thematic phases an author can go through during his activity, hence the partition of all his texts in a reference and a test portion.

Figure 1: RWPref and RWPtest creation process for an author.

Figure 1: RWPref and RWPtest creation process for an author.
  • 2 Being the corpus relatively small and not having particular computability issues, we chose not to a (...)
  • 3 We performed different empiric setup of the window’s size and chose the one that showed more suitab (...)

13We then analyzed each reference and test corpora with a Part-of-Speech (PoS) tagger and a lemmatizer for Italian (Dell’Orletta et al., 2014). For every author, we built two lists of word pairs (with their lemma and PoS), one relative to the tagged reference corpus (reference pairs) and the other to the tagged test set (test pairs), where each word was paired with all the other words with the same PoS. We also filtered the pairs to leave only nouns, adjectives and verbs. Starting from the tagged corpora, we built two words-by-words matrixes2 of co-occurrence counts (co-occurrence matrixes) for each author, using a context window of 43. The chosen DS model (Baroni and Lenci, 2010) was applied to each matrix to calculate the cosine between the vectors representing the two words of each pair. This allowed us to evaluate the semantic relatedness between the words by assessing their proximity in the distributional space as represented by the cosine value: the more this value tends to 1, the more the two words of the pair are considered to be related. We then obtained two related word pair (RWP) lists for each author A: RWPrefA and RWPtestA. Figure 1 shows the process described above.

14Since 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 Table 1) 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 Table 1 would be removed as probable multiword (PM column in Table 1): “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”).

Table 1: An example of co-occurring RWs from Pirandello’s test list: the first two pairs would be removed.




































  • 4 At the following url we have uploaded an archive containing all the data we have used and processed (...)

15Finally, 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 threshold4. We chose to introduce the parameter θ for two reasons: i) to avoid the classification algorithm to be disturbed by noisy (i.e. not significative) pairs which would not hold any relevant stylistic cue, and ii) 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.

16For 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. Table 2 shows the classification results for θ = 5%.

Table 2: Classification results, obtained via the nearest-cosine method for θ = 5%.


De Roberto












De Roberto



































4 Interpreting the Results

17As summarized in Table 3, a correct classification of all RWPs in RWPtest lists has been obtained with a θ value of 5%.

18To help in interpreting the failure of the algorithm in classifying Tozzi’s test list for θ values lower than 5% (as shown in Table 3) we calculated the cardinality of the RWPtest lists for each author with the change in θ value (Tables 4).

19It is possible to observe how the choice of θ influences the correct classification of Tozzi’s test list. Indeed, the use of a θ value below 5% has the effect of remarkably reducing an already small test list (RWPtextTozzi) as shown in Table 4. It is apparent that increasing the value of θ and consequently the number of significant RW pairs that are analysed, the system is able to correctly classify RWP Table 3).

Table 3: Results of the classification. Classification errors are highlighted.










De Roberto

De Roberto

De Roberto

De Roberto

De Roberto





















Table 4: Cardinality of RWPtest for each author and for each θ value.





#RWPtest Capuana





#RWPtest De Roberto





#RWPtest Pirandello





#RWPtest Svevo





#RWPtest Tozzi





#RWPtest Verga





5 Conclusion and Next Steps

20In 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 a first experiment on six different Italian authors. The 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. Of course, it is not our intent, with this paper, to define new methods for enhancing state-of-the-art authorship attribution algorithms. Our research will focus, in the next steps, in 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 analysing their distributional variability. Furthermore, it could be interesting to use a different distributional measure, than the cosine, to test our hypothesis.


Shlomo Argamon, Casey Whitelaw, Paul Chase, Sobhan Raj Hota, Navendu Garg, and Shlomo Levitan. 2007. Stylistic text classification using functional lexical features. Journal of the American Society for Information Science and Technology, 58(6):802–822, April.

Marco Baroni and Alessandro Lenci. 2010. Distributional memory: A general framework for corpus-based semantics. Computational Linguistics, 36(4):673–721.

Paul Buitelaar, Nitish Aggarwal, and Justin Tonra. 2014. Using distributional semantics to trace influence and imitation in romantic orientalist poetry. In AHA!-Workshop 2014 on Information Discovery in Text. ACL.

Olivier De Vel, Alison Anderson, Malcolm Corney, and George Mohay. 2001. Mining e-mail content for author identification forensics. ACM Sigmod Record, 30(4):55–64.

Felice Dell’Orletta, Giulia Venturi, Andrea Cimino, and Simonetta Montemagni. 2014. T2kˆ2: a system for automatically extracting and organizing knowledge from texts. In LREC, pages 2062–2070.

Michael Gamon. 2004. Linguistic correlates of style: authorship classification with deep linguistic analysis features. In Proceedings of the 20th international conference on Computational Linguistics, page 611. Association for Computational Linguistics.

Jack Grieve. 2007. Quantitative Authorship Attribution: An Evaluation of Techniques. Literary and Linguistic Computing, 22(3):251–270, May.

Michael AK Halliday. 1994. Functional grammar. London: Edward Arnold.

Aurélie Herbelot. 2015. The semantics of poetry: A distributional reading. Digital Scholarship in the Humanities, 30(4):516–531.

Graeme Hirst and Olga Feiguina. 2007. Bigrams of Syntactic Labels for Authorship Discrimination of Short Texts. Literary and Linguistic Computing, 22(4):405–417, September.

Moshe Koppel and Jonathan Schler. 2004. Authorship verification as a one-class classification problem. In Proceedings of the twenty-first international conference on Machine learning, page 62. ACM.

Germán Kruszewski and Marco Baroni. 2014. Dead parrots make bad pets: Exploring modifier effects in noun phrases. Lexical and Computational Semantics (* SEM 2014), page 171.

Alessandro Lenci. 2008. Distributional semantics in linguistic and cognitive research. Italian journal of linguistics, 20(1):1–31.

Jiexun Li, Rong Zheng, and Hsinchun Chen. 2006. From fingerprint to writeprint. Communications of the ACM, 49(4):76–82.

George A Miller and Walter G Charles. 1991. Contextual correlates of semantic similarity. Language and cognitive processes, 6(1):1–28.

Joseph Rudman. 1997. The state of authorship attribution studies: Some problems and solutions. Computers and the Humanities, 31(4):351–365.

Efstathios Stamatatos, Nikos Fakotakis, and George Kokkinakis. 2000. Automatic text categorization in terms of genre and author. Computational linguistics, 26(4):471–495.

Efstathios Stamatatos, Nikos Fakotakis, and Georgios Kokkinakis. 2001. Computer-based authorship attribution without lexical measures. Computers and the Humanities, 35(2):193–214.

Efstathios Stamatatos. 2006. Authorship attribution based on feature set subspacing ensembles. International Journal on Artificial Intelligence Tools, 15(05):823–838.

Efstathios Stamatatos. 2009. A survey of modern authorship attribution methods. J. Am. Soc. Inf. Sci. Technol., 60(3):538–556, March.

Gui-Fa Teng, Mao-Sheng Lai, Jian-Bin Ma, and Ying Li. 2004. E-mail authorship mining based on svm for computer forensic. In Machine Learning and Cybernetics, 2004. Proceedings of 2004 International Conference on, volume 2, pages 1204–1207. IEEE.

Özlem Uzuner and Boris Katz. 2005. A comparative study of language models for book and author recognition. In Natural Language Processing–IJCNLP 2005, pages 969–980. Springer.

Hans Van Halteren, Harald Baayen, Fiona Tweedie, Marco Haverkort, and Anneke Neijt. 2005. New machine learning methods demonstrate the existence of a human stylome. Journal of Quantitative Linguistics, 12(1):65–77.

Ying Zhao and Justin Zobel. 2005. Effective and scalable authorship attribution using function words. In Information Retrieval Technology, pages 174–189. Springer.

Rong Zheng, Jiexun Li, Hsinchun Chen, and Zan Huang. 2006. A framework for authorship identification of online messages: Writing-style features and classification techniques. Journal of the American Society for Information Science and Technology, 57(3):378–393, February.



2 Being the corpus relatively small and not having particular computability issues, we chose not to apply decomposition techniques to reduce the size of the matrixes (and thus not losing any information).

3 We performed different empiric setup of the window’s size and chose the one that showed more suitable results, according to what is stated by Kruszewski and Baroni (2014).

4 At the following url we have uploaded an archive containing all the data we have used and processed for our experiment:

Table des illustrations

Titre Figure 1: RWPref and RWPtest creation process for an author.
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