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%.

本研究使用 NLP 技术中的 lemmatization 和 parsing，研究了词语和语法标记对于作者风格识别的效果，并在多种标记类型下进行了分类准确度的对比，结果显示虽然其性能较字词标记差，但不相差的程度不超过 15%。