Semi-supervised Word Sense Disambiguation with Neural Models

Understand

Determining the intended sense of words in text - word sense disambiguation (WSD) - is a long standing problem in natural language processing.

• Recently, researchers have shown promising results using word vectors extracted from a neural network language model as features in WSD algorithms.
• However, a simple average or concatenation of word vectors for each word in a text loses the sequential and syntactic information of the text.
• In this paper, we study WSD with a sequence learning neural net, LSTM, to better capture the sequential and syntactic patterns of the text.