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• W2953318082 abstract "In this paper, we investigate whether an a priori disambiguation of word senses is strictly necessary or whether the meaning of a word in context can be disambiguated through composition alone. We evaluate the performance of off-the-shelf single-vector and multi-sense vector models on a benchmark phrase similarity task and a novel task for word-sense discrimination. We find that single-sense vector models perform as well or better than multi-sense vector models despite arguably less clean elementary representations. Our findings furthermore show that simple composition functions such as pointwise addition are able to recover sense specific information from a single-sense vector model remarkably well." @default.
• W2953318082 created "2019-06-27" @default.
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• W2953318082 date "2017-02-22" @default.
• W2953318082 modified "2023-09-27" @default.
• W2953318082 title "One Representation per Word - Does it make Sense for Composition?" @default.
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• W2953318082 hasPublicationYear "2017" @default.
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