Capturing Expression Using Linguistic Information

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.