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Title: Word sense disambiguation for all words without hard labor
Authors: Zhong, Z.
Ng, H.T. 
Issue Date: 2009
Citation: Zhong, Z.,Ng, H.T. (2009). Word sense disambiguation for all words without hard labor. IJCAI International Joint Conference on Artificial Intelligence : 1616-1621. ScholarBank@NUS Repository.
Abstract: While the most accurate word sense disambiguation systems are built using supervised learning from sense-tagged data, scaling them up to all words of a language has proved elusive, since preparing a sense-tagged corpus for all words of a language is time-consuming and human labor intensive. In this paper, we propose and implement a completely automatic approach to scale up word sense disambiguation to all words of English. Our approach relies on English-Chinese parallel corpora, English-Chinese bilingual dictionaries, and automatic methods of finding synonyms of Chinese words. No additional human sense annotations or word translations are needed. We conducted a large-scale empirical evaluation on more than 29,000 noun tokens in English texts annotated in OntoNotes 2.0, based on its coarse-grained sense inventory. The evaluation results show that our approach is able to achieve high accuracy, outperforming the first-sense baseline and coming close to a prior reported approach that requires manual human efforts to provide Chinese translations of English senses.
Source Title: IJCAI International Joint Conference on Artificial Intelligence
ISBN: 9781577354260
ISSN: 10450823
Appears in Collections:Staff Publications

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