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

Abstract

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.

References in corpus (1)

• Transition-Based Dependency Parsing with Stack Long Short-Term Memory

Cited by in corpus (1)

• Fixed-Size Ordinally Forgetting Encoding Based Word Sense Disambiguation