context2vec: Learning Generic Context Embedding with Bidirectional LSTM

Oren Melamud1, Jacob Goldberger2, Ido Dagan2
1Bar Ilan University, 2Bar-Ilan University


Context representations are central to various NLP tasks, such as word sense disambiguation, named entity recognition, co-reference resolution, and many more. In this work we present a neural model for efficiently learning a generic context embedding function from large corpora, using bidirectional LSTM. With a very simple application of our context representations, we manage to surpass or nearly reach state-of-the-art results on sentence completion, lexical substitution and word sense disambiguation tasks, while substantially outperforming the popular context representation of averaged word embeddings. We release our code and pre-trained models, suggesting they could be useful in a wide variety of NLP tasks.