Stylistic Fingerprints, POS-tags, and Inflected Languages: A Case Study in Polish

ABSTRACT

In stylometric investigations, frequencies of the most frequent words (MFWs) and character n-grams outperform other style-markers, even if their performance varies significantly across languages. In inflected languages, word endings play a prominent role, and hence different word forms cannot be recognized using generic text tokenization. Countless inflected word forms make frequencies sparse, making most statistical procedures complicated. Presumably, applying one of the NLP techniques, such as lemmatization and/or parsing, might increase the performance of classification. The aim of this paper is to examine the usefulness of grammatical features (as assessed via POS-tag n-grams) and lemmatized forms in recognizing authorial profiles, in order to address the underlying issue of the degree of freedom of choice within lexis and grammar. Using a corpus of Polish novels, we performed a series of supervised authorship attribution benchmarks, in order to compare the classification accuracy for different types of lexical and syntactic style-markers. Even if the performance of POS-tags as well as lemmatized forms was notoriously worse than that of lexical markers, the difference was not substantial and never exceeded ca. 15%.

Disclosure Statement

No potential conflict of interest was reported by the author(s).

Notes

1. Our corpus contains literary sources only. An interesting question – far beyond the scope of this study – is the extent to which the fact that a writer seeks originality makes the fingerprint clearer compared to non-fiction literature.

2. The full set of tables for particular datasets, classifiers, feature types, and their n-grams, can be found in our GitHub repository.

Additional information

Funding

This research was conducted as a result of the project “Large-Scale Text Analysis and Methodological Foundations of Computational Stylistics” [2017/26/E/HS2/01019] supported by Poland’s National Science Centre.