Capturing Expression Using Linguistic Information

Book One

Volume

Proceedings of the AAAI Conference on Artificial Intelligence, 20

Natural Language Processing and Speech Recognition

Recognizing similarities between literary works for copyright infringement detection requires evaluating similarity in the expression of content. Copyright law protects expression of content; similarities in content alone are not enough to indicate infringement. Expression refers to the way people convey particular information; it captures both the information and the manner of its presentation. In this paper, we present a novel set of linguistically informed features that provide a computational definition of expression and that enable accurate recognition of individual titles and their paraphrases more than 80% of the time. In comparison, baseline features, e.g., tfidf-weighted keywords, function words, etc., give an accuracy of at most 53%. Our computational definition of expression uses linguistic features that are extracted from POS-tagged text using context-free grammars, without incurring the computational cost of full parsers. The results indicate that informative linguistic features do not have to be computationally prohibitively expensive to extract.