In this paper, we analyze relationships between word pairs and evaluate their idiosyncratic properties in the applied context of authorship attribution. Specifically, on three literary corpora we optimize word pair features for information gain which reflect word similarity as measured by word embeddings. We analyze the quality of the most informative features in terms of word type relation (a comparison of different constellations of function and content words), similarity, and relatedness. Results point to the extraordinary role of function words within the authorship attribution task being extended to their pairwise relational patterns. Similarity of content words is likewise among the most informative features. From a cognitive perspective, we conclude that both relationship types reflect short distance connections in the human brain, which is highly indicative of an individual writing style.
@InProceedings{HOENEN18.349, author = {Armin Hoenen and Niko Schenk}, title = "{Knowing the Author by the Company His Words Keep}", booktitle = {Proceedings of the Eleventh International Conference on Language Resources and Evaluation (LREC 2018)}, year = {2018}, month = {May 7-12, 2018}, address = {Miyazaki, Japan}, editor = {Nicoletta Calzolari (Conference chair) and Khalid Choukri and Christopher Cieri and Thierry Declerck and Sara Goggi and Koiti Hasida and Hitoshi Isahara and Bente Maegaard and Joseph Mariani and Hélène Mazo and Asuncion Moreno and Jan Odijk and Stelios Piperidis and Takenobu Tokunaga}, publisher = {European Language Resources Association (ELRA)}, isbn = {979-10-95546-00-9}, language = {english} }
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