Chinese Word Segmentation and Unknown Word Extraction by Mining Maximized Substring
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- Shen Mo
- Graduate School of Informatics, Kyoto University
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- Kawahara Daisuke
- Graduate School of Informatics, Kyoto University
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- Kurohashi Sadao
- Graduate School of Informatics, Kyoto University
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抄録
<p>Chinese word segmentation is an initial and important step in Chinese language processing. Recent advances in machine learning techniques have boosted the performance of Chinese word segmentation systems, yet the identification of out-of-vocabulary words is still a major problem in this field of study. Recent research has attempted to address this problem by exploiting characteristics of frequent substrings in unlabeled data. We propose a simple yet effective approach for extracting a specific type of frequent substrings, called maximized substrings, which provide good estimations of unknown word boundaries. In the task of Chinese word segmentation, we use these substrings which are extracted from large scale unlabeled data to improve the segmentation accuracy. The effectiveness of this approach is demonstrated through experiments using various data sets from different domains. In the task of unknown word extraction, we apply post-processing techniques that effectively reduce the noise in the extracted substrings. We demonstrate the effectiveness and efficiency of our approach by comparing the results with a widely applied Chinese word recognition method in a previous study. </p>
収録刊行物
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- 自然言語処理
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自然言語処理 23 (3), 235-266, 2016
一般社団法人 言語処理学会
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詳細情報 詳細情報について
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- CRID
- 1390282679453256960
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- NII論文ID
- 130005411025
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- NII書誌ID
- AN10472659
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- ISSN
- 21858314
- 13407619
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- NDL書誌ID
- 027492546
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- 本文言語コード
- en
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- データソース種別
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- JaLC
- NDL
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- CiNii Articles
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- 使用不可