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Recently, Yuan et al. (2016) have shown the effectiveness of using LongShort-Term Memory (LSTM) for performing Word Sense Disambiguation (WSD). Theirproposed technique outperformed the previous state-of-the-art with severalbenchmarks, but neither the training data nor the source code was released.This paper presents the results of a reproduction study of this technique usingonly openly available datasets (GigaWord, SemCore, OMSTI) and software(TensorFlow). From them, it emerged that state-of-the-art results can beobtained with much less data than hinted by Yuan et al. All code and trainedmodels are made freely available.

Word Sense Disambiguation with LSTM: Do We Really Need 100 Billion Words?