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Word Sense Disambiguation using a Bidirectional LSTM

Mikael Kågebäck and Hans Salomonsson, (Coling 2016 Workshop on Cognitive Aspects of the Lexicon (CogALex-V))
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In this paper we present a clean, yet effective, model for word sense disambiguation. Our approach leverage a bidirectional long short-term memory network which is shared between all words. This enables the model to share statistical strength and to scale well with vocabulary size. The model is trained end-to-end, directly from the raw text to sense labels, and makes effective use of word order. We evaluate our approach on two standard datasets, using identical hyperparameter settings, which are in turn tuned on a third set of held out data. We employ no external resources (e.g. knowledge graphs, part-of-speech tagging, etc), language specific features, or hand crafted rules, but still achieve statistically equivalent results to the best state-of-the-art systems, that employ no such limitations.

BibTex

@inproceedings{kaageback2016word,
	title={Word Sense Disambiguation using a Bidirectional LSTM},
	author={K{\aa}geb{\"a}ck, Mikael and Salomonsson, Hans},
	booktitle={5th Workshop on Cognitive Aspects of the Lexicon (CogALex)},
	organization={Association for Computational Linguistics},
	year={2016},
}

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