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Neural context embeddings for automatic discovery of word senses

Mikael Kågebäck (Institutionen för data- och informationsteknik, Datorteknik (Chalmers)) ; Fredrik Johansson (Institutionen för data- och informationsteknik (Chalmers)) ; Richard Johansson ; Devdatt Dubhashi (Institutionen för data- och informationsteknik (Chalmers))
Proceedings of the 1st Workshop on Vector Space Modeling for Natural Language Processing. Denver, United States p. 25-32. (2015)
[Konferensbidrag, refereegranskat]

Word sense induction (WSI) is the problem of automatically building an inventory of senses for a set of target words using only a text corpus. We introduce a new method for embedding word instances and their context, for use in WSI. The method, Instance-context embedding (ICE), leverages neural word embeddings, and the correlation statistics they capture, to compute high quality embeddings of word contexts. In WSI, these context embeddings are clustered to find the word senses present in the text. ICE is based on a novel method for combining word embeddings using continuous Skip-gram, based on both se- mantic and a temporal aspects of context words. ICE is evaluated both in a new system, and in an extension to a previous system for WSI. In both cases, we surpass previous state-of-the-art, on the WSI task of SemEval-2013, which highlights the generality of ICE. Our proposed system achieves a 33% relative improvement.

Nyckelord: språkteknologi, lexikal semantik, ordbetydelser, korpusar, distributionella metoder

Konferensens websida: http://naacl15vs.github.io/

Denna post skapades 2015-06-01.
CPL Pubid: 217864