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SUMMARY:Senses can help vector space models of lexical substitution - Mari
 anna Apidianaki (LIMSI)
DTSTART:20180111T110000Z
DTEND:20180111T120000Z
UID:TALK97600@talks.cam.ac.uk
CONTACT:Dimitri Kartsaklis
DESCRIPTION:The role of senses in NLP applications has been questioned due
  to the high performance of vector space models in semantic tasks. These m
 odels deliver state-of-the-art performance without explicitly accounting f
 or senses which have even been shown to be harmful for some tasks. In this
  talk\, I will show how sense representations tailored to the task can imp
 rove the results of vector-based lexical substitution models. I will discu
 ss two aspects related to paraphrase substitution\, namely their clusterab
 ility into senses and their substitutability in context. Finally\, I will 
 present preliminary results on core sense detection through a multi-view a
 pproach to paraphrase semantic analysis.
LOCATION:Boardroom\, Faculty of English\, West Road
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