  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 state-of-the-art results can be
obtained with much less data than hinted by Yuan et al. All code and trained
models are made freely available.
