Deep Dive into Word Sense Disambiguation with LSTM

M.N. Le, M.C. Postma, J. Urbani, P.T.J.M. Vossen

Research output: Chapter in Book / Report / Conference proceedingConference contributionAcademicpeer-review


LSTM-based language models have been shown effective in Word Sense Disambiguation
(WSD). In particular, the technique proposed by Yuan et al. (2016) returned state-of-the-art performance
in several benchmarks, but neither the training data nor the source code was released.
This paper presents the results of a reproduction study and analysis of this technique using only
openly available datasets (GigaWord, SemCor, OMSTI) and software (TensorFlow). Our study
showed that similar results can be obtained with much less data than hinted at by Yuan et al.
(2016). Detailed analyses shed light on the strengths and weaknesses of this method. First,
adding more unannotated training data is useful, but is subject to diminishing returns. Second,
the model can correctly identify both popular and unpopular meanings. Finally, the limited sense
coverage in the annotated datasets is a major limitation. All code and trained models are made
freely available.
Original languageEnglish
Title of host publicationProceedings of the International Conference on Computational Linguistics (COLING 2018)
PublisherInternational Conference on Computational Linguistics (COLING)
Number of pages12
ISBN (Print)9781948087506
Publication statusPublished - 2018
Event27th International Conference on Computational Linguistics COLING 2018 - Santa Fe, NM
Duration: 20 Aug 201826 Aug 2018
Conference number: 27


Conference27th International Conference on Computational Linguistics COLING 2018
Abbreviated titleCOLING 2018
CitySanta Fe, NM

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