Bayesian Word Sense Discrimination
نویسندگان
چکیده
منابع مشابه
One Sense Per Discourse
It is well-known that there are polysemous words like sentence whose ‘‘meaning’’ or ‘‘sense’’ depends on the context of use. We have recently reported on two new word-sense disambiguation systems, one trained on bilingual material (the Canadian Hansards) and the other trained on monolingual material (Roget’s Thesaurus and Grolier’s Encyclopedia). As this work was nearing completion, we observed...
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The problem of feature selection in Word Sense Discrimination (a subtask of Word Sense Disambiguation) is crucial for the accuracy of results. The paper proposes as a new feature the length of words [1]. Some combination between this feature and other features usually used are studied
متن کاملOne Sense per Discourse 2. Our Previous Work on Word-sense Disambiguation 2.1. Data Deprivation
It is well-known that there are polysemous words like sentence whose "meaning" or "sense" depends on the context of use. We have recently reported on two new word-sense disambiguation systems, one trained on bilingual material (the Canadian Hansards) and the other trained on monolingual material (Roget's Thesaurus and Grolier's Encyclopedia). As this work was nearing completion, we observed a v...
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This paper describes adaptations of unsupervised word sense discrimination techniques to the problem of name discrimination. These methods cluster the contexts containing an ambiguous name, such that each cluster refers to a unique underlying person or place. We also present new techniques to assign meaningful labels to the discovered clusters.
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Word sense ambiguity has been identified as a cause of poor precision in information retrieval (IR) systems. Word sense disambiguation and discrimination methods have been defined to help systems choose which documents should be retrieved in relation to an ambiguous query. However, the only approaches that show a genuine benefit for word sense discrimination or disambiguation in IR are generall...
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تاریخ انتشار 2007