Neural context embeddings for automatic discovery of word senses
Paper in 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, Computer Science and Engineering (Chalmers), Computer Engineering (Chalmers)

Fredrik Johansson

Chalmers, Computer Science and Engineering (Chalmers)

Richard Johansson

University of Gothenburg

Devdatt Dubhashi

Chalmers, Computer Science and Engineering (Chalmers)

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

9781941643464 (ISBN)

Subject Categories

Language Technology (Computational Linguistics)

Computer and Information Science



More information

Latest update