The Lexical Substitution task involves selecting and ranking lexical paraphrases for a target word in a given sentential context. We present PIC, a simple measure for estimating the appropriateness of substitutes in a given context. PIC outperforms another simple, comparable model proposed in recent work, especially when selecting substitutes from the entire vocabulary. Analysis shows that PIC improves over baselines by incorporating frequency biases into predictions.

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In Proceedings of the 15th Annual Conference of the North American Chapter of the Association for Computational Linguistics (NAACL-16), pp. 1121-1126, San Diego, California 2016.

@inproceedings{roller:naacl16, title={PIC a Different Word: A Simple Model for Lexical Substitution in Context}, author={Stephen Roller and Katrin Erk}, booktitle={Proceedings of the 15th Annual Conference of the North American Chapter of the Association for Computational Linguistics (NAACL-16)}, address={San Diego, California}, pages={1121-1126}, url="http://www.cs.utexas.edu/users/ai-labpub-view.php?PubID=127594", year={2016} }