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NLP Reading Group: Measuring Distributional Similarity in Context

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If you have a question about this talk, please contact Marek Rei.

This week Marek will be talking about:

Measuring Distributional Similarity in Context. Georgiana Dinu & Mirella Lapata. EMNLP 2010


The computation of meaning similarity as operationalized by vector-based models has found widespread use in many tasks ranging from the acquisition of synonyms and paraphrases to word sense disambiguation and textual entailment. Vector-based models are typically directed at representing words in isolation and thus best suited for measuring similarity out of context. In his paper we propose a probabilistic framework for measuring similarity in context. Central to our approach is the intuition that word meaning is represented as a probability distribution over a set of latent senses and is modulated by context. Experimental results on lexical substitution and word similarity show that our algorithm outperforms previously proposed models.

This talk is part of the Natural Language Processing Reading Group series.

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