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• W2135341569 abstract "We introduce tiered clustering, a mixture model capable of accounting for varying degrees of shared (context-independent) feature structure, and demonstrate its applicability to inferring distributed representations of word meaning. Common tasks in lexical semantics such as word relatedness or selectional preference can benefit from modeling such structure: Polysemous word usage is often governed by some common background metaphoric usage (e.g. the senses of line or run), and likewise modeling the selectional preference of verbs relies on identifying commonalities shared by their typical arguments. Tiered clustering can also be viewed as a form of soft feature selection, where features that do not contribute meaningfully to the clustering can be excluded. We demonstrate the applicability of tiered clustering, highlighting particular cases where modeling shared structure is beneficial and where it can be detrimental." @default.
• W2135341569 created "2016-06-24" @default.
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• W2135341569 date "2010-10-09" @default.
• W2135341569 modified "2023-09-26" @default.
• W2135341569 title "A Mixture Model with Sharing for Lexical Semantics" @default.
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