Word Meaning in Context: A Simple and Effective Vector Model

Abstract

We present a model that represents word meaning in context by vectors which are modified according to the words in the target’s syntactic context. Contextualization of a vector is realized by reweighting its components, based on distributional information about the context words. Evaluation on a paraphrase ranking task derived from the SemEval 2007 Lexical Substitution Task shows that our model outperforms all previous models on this task. We show that our model supports a wider range of applications by evaluating it on a word sense disambiguation task. Results show that our model achieves state-of-the-art performance.

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@inproceedings{Thater2011WordMI, title={Word Meaning in Context: A Simple and Effective Vector Model}, author={Stefan Thater and Hagen F{\"{u}rstenau and Manfred Pinkal}, booktitle={IJCNLP}, year={2011} }