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• W60571609 abstract "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." @default.
• W60571609 created "2016-06-24" @default.
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• W60571609 date "2010-10-09" @default.
• W60571609 modified "2023-09-26" @default.
• W60571609 title "Measuring Distributional Similarity in Context" @default.
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