{"id":{"repo_id":"nus","oai_identifier":"oai:scholarbank.nus.edu.sg:10635/15159"},"canonical_url":"https://search.dev.ndltd.org/etd/nus/oai:scholarbank.nus.edu.sg:10635/15159","repository":{"repo_id":"nus","name":"National University of Singapore","base_url":"https://scholarbank.nus.edu.sg/oai/request"},"display":{"title":"Chinese word segmentation with a maximum entropy approach","abstract":"In this thesis, we present a maximum entropy approach to Chinese word segmentation. Besides using features derived from gold-standard word-segmented training data, we also used an external dictionary and additional training corpora of different segmentation standards to further improve segmentation accuracy. The selection of useful additional training data is modeled as example selection from noisy data. Using these techniques, our word segmenter achieved state-of-the-art accuracy. 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