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- W2055131981 abstract "The paper describes a semi-supervised approach to extracting multiword units that belong to a specific semantic class of entities. The approach uses a small set of seed words representing the target class, and calculates distributional similarity between the candidate and seed words. We adapt a well-known document ranking function, BM25, to the task of calculating similarity between vectors of context features representing seed words and candidate words, and perform a systematic comparison to a number of distributional similarity measures. We then introduce a method for ranking multiword units by the likelihood of belonging to the target semantic class. The task used for evaluation is extraction of restaurant dish names from the corpus of 157,865 restaurant reviews." @default.
- W2055131981 created "2016-06-24" @default.
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- W2055131981 date "2012-08-12" @default.
- W2055131981 modified "2023-10-10" @default.
- W2055131981 title "A semi-supervised approach to extracting multiword entity names from user reviews" @default.
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- W2055131981 doi "https://doi.org/10.1145/2379307.2379309" @default.
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