Skip to main content


	title        = {Enriching Word-sense Embeddings with Translational Context},
	abstract     = {Vector-space models derived from corpora are an effective way to learn a representation of word meaning directly from data, and these models have many uses in practical applications. A  number  of  unsupervised  approaches  have  been  proposed to  automatically  learn  representations of word senses
directly  from  corpora,  but since  these  methods  use  no  information
but the words themselves, they sometimes miss distinctions that could be possible to make if more information were available.

In this paper, we present a general framework that we call context enrichment that incorporates  external  information  during the  training  of  multi-sense  vector-space models.   Our  approach  is  agnostic  as  to which external signal is used to enrich the context, but in this work we consider the use of translations
as the source of enrichment. We evaluated the models trained using the translation-enriched context using
several similarity benchmarks and a word analogy test set. In all our evaluations, the enriched  model  outperformed  the  purely word-based baseline soundly.
	booktitle    = {Proceedings of Recent Advances in Natural Language Processing / edited by Galia Angelova, Kalina Bontcheva, Ruslan Mitkov. International Conference, Hissar, Bulgaria 7–9 September, 2015},
	author       = {Ghanimifard, Mehdi and Johansson, Richard},
	year         = {2015},
	pages        = {208--215},