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Word Sense Disambiguation with LSTM: Do We Really Need 100 Billion Words?
[article]
2017
arXiv
pre-print
Recently, Yuan et al. (2016) have shown the effectiveness of using Long Short-Term Memory (LSTM) for performing Word Sense Disambiguation (WSD). Their proposed technique outperformed the previous state-of-the-art with several benchmarks, but neither the training data nor the source code was released. This paper presents the results of a reproduction study of this technique using only openly available datasets (GigaWord, SemCore, OMSTI) and software (TensorFlow). From them, it emerged that
arXiv:1712.03376v2
fatcat:ydhne4b2b5gjjcp2xvnfd4ch4m