Neural context embeddings for automatic discovery of word senses
Paper i proceeding, 2015

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.

distributionella metoder


lexikal semantik




Mikael Kågebäck

Chalmers, Data- och informationsteknik, Datorteknik

Fredrik Johansson

Chalmers, Data- och informationsteknik

Richard Johansson

Göteborgs universitet

Devdatt Dubhashi

Chalmers, Data- och informationsteknik

Proceedings of the 1st Workshop on Vector Space Modeling for Natural Language Processing. Denver, United States



Språkteknologi (språkvetenskaplig databehandling)

Data- och informationsvetenskap