Year: 2016
Type: conference-paper
Abstract: Context representations are central to various NLP tasks, such as word sense disambiguation, named entity recognition, coreference 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 pretrained models, suggesting they could be useful in a wide variety of NLP tasks.
DOI: 10.18653/v1/k16-1006
Authors: Oren Melamud, Jacob Goldberger, Ido Dagan
Institution: Bar-Ilan University
Language: English
Topic: Topic Modeling
Subfield: Artificial Intelligence
Field: Computer Science
Domain: Physical Sciences
Open Access status: gold
Award: G7805275943
OpenAlex ID: W2507974895