Alicia Krebs

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Distributional semantic models can predict many linguistic phenomena, including word similarity, lexical ambiguity, and semantic priming, or even to pass TOEFL synonymy and analogy tests (Landauer and Dumais, 1997; Griffiths et al., 2007; Turney and Pantel, 2010). But what does it take to create a competitive distributional model? Levy et al. (2015) argue(More)
If lexical similarity is not enough to reliably assess how word vectors would perform on various specific tasks, we need other ways of evaluating semantic representations. We propose a new task, which consists in extracting semantic differences using distributional models: given two words, what is the difference between their meanings? We present two proof(More)
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