Werdy: Recognition and Disambiguation of Verbs and Verb Phrases with Syntactic and Semantic Pruning
Luciano Del Corro, Rainer Gemulla, Gerhard Weikum · 2014
Word-sense recognition and disambigua-tion (WERD) is the task of identifying word phrases and their senses in natural language text. Though it is well under-stood how to disambiguate noun phrases, this task is much less studied for verbs and verbal phrases. We present Werdy, a framework for WERD with particular focus on verbs and verbal phrases. Our framework first identifies multi-word ex-pressions based on the syntactic structure of the sentence; this allows us to recog-nize both contiguous and non-contiguous phrases. We then generate a list of can-didate senses for each word or phrase, us-ing novel syntactic and semantic pruning techniques. We also construct and lever-age a new resource of pairs of senses for verbs and their object arguments. Finally, we feed the so-obtained candidate senses into standard word-sense disambiguation (WSD) methods, and boost their precision and recall. Our experiments indicate that Werdy significantly increases the perfor-mance of existing WSD methods. 1