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題名 Automatic Extraction of New Words based on Google News Corpora for Supporting Lexicon-based Chinese Word Segmentation Systems,
作者 Hong, Chin-Ming
Chen, Chih-Ming
Chiu, Chao-Yang
陳志銘
貢獻者 政大圖檔所
關鍵詞 Natural language processing; New word extraction; Chinese word segmentation; Information retrieval
日期 2009-03
上傳時間 15-四月-2013 09:36:59 (UTC+8)
摘要 Chinese word segmentation is an essential step in a processing of Chinese natural language because it is beneficial to the Chinese text mining and information retrieval. Currently, the lexicon-based Chinese word segmentation scheme is widely adopted, which can correctly identify Chinese sentences as distinct words from Chinese language texts in real-word applications. However, the word identification ability of the lexicon-based scheme is highly dependent with a well prepared lexicon with sufficient amount of lexical entries which covers all of the Chinese words. In particular, this scheme cannot perform Chinese word segmentation process well for highly changeable texts with time, such as newspaper articles and web documents. This is because highly changeable documents often contain many new words that cannot be identified by a lexicon-based Chinese word segmentation system with a constant lexicon. Moreover, to maintain a lexicon by manpower is an inefficient and time-consuming job. Therefore, this study proposes a novel statistics-based scheme for extraction of new words based on the categorized corpora of Google News retrieved automatically from the Google News site to promote the word identification ability for lexicon-based Chinese word segmentation systems. Since corpora of news almost contain all words used in daily life, to extract news words from corpora of news and to incrementally add them into lexicon for lexicon-based Chinese word segmentation systems provide benefits in terms of automatically constructing a professional lexicon and enhancing word identification capability. Compared to another proposed scheme of new word extraction, the experimental results indicated that the proposed extraction scheme of new words not only more correctly retrieves new words from the categorized corpora of Google News, but also obtains larger amount of new words. Moreover, the proposed scheme of new word extraction has been applied to automatically expand the lexicon of the Chinese word segmentation system ECScanner (A Chinese Lexicon Scanner with Lexicon Extension). Currently, the ECScanner has been published on the Web to provide Chinese word segmentation service based on Web service. Experimental results also confirmed that ECScanner is superior to CKIP (Chinese knowledge information processing) in identifying meaningful Chinese words.
關聯 Expert Systems with Applications, 36(2), 3641-3651. (SCIE,EI)
資料類型 article
DOI http://dx.doi.org/http://dx.doi.org/10.1016/j.eswa.2008.02.013
dc.contributor 政大圖檔所en
dc.creator (作者) Hong, Chin-Mingen
dc.creator (作者) Chen, Chih-Mingen
dc.creator (作者) Chiu, Chao-Yangen
dc.creator (作者) 陳志銘zh_TW
dc.date (日期) 2009-03-
dc.date.accessioned 15-四月-2013 09:36:59 (UTC+8)-
dc.date.available 15-四月-2013 09:36:59 (UTC+8)-
dc.date.issued (上傳時間) 15-四月-2013 09:36:59 (UTC+8)-
dc.identifier.uri (URI) http://nccur.lib.nccu.edu.tw/handle/140.119/57626-
dc.description.abstract (摘要) Chinese word segmentation is an essential step in a processing of Chinese natural language because it is beneficial to the Chinese text mining and information retrieval. Currently, the lexicon-based Chinese word segmentation scheme is widely adopted, which can correctly identify Chinese sentences as distinct words from Chinese language texts in real-word applications. However, the word identification ability of the lexicon-based scheme is highly dependent with a well prepared lexicon with sufficient amount of lexical entries which covers all of the Chinese words. In particular, this scheme cannot perform Chinese word segmentation process well for highly changeable texts with time, such as newspaper articles and web documents. This is because highly changeable documents often contain many new words that cannot be identified by a lexicon-based Chinese word segmentation system with a constant lexicon. Moreover, to maintain a lexicon by manpower is an inefficient and time-consuming job. Therefore, this study proposes a novel statistics-based scheme for extraction of new words based on the categorized corpora of Google News retrieved automatically from the Google News site to promote the word identification ability for lexicon-based Chinese word segmentation systems. Since corpora of news almost contain all words used in daily life, to extract news words from corpora of news and to incrementally add them into lexicon for lexicon-based Chinese word segmentation systems provide benefits in terms of automatically constructing a professional lexicon and enhancing word identification capability. Compared to another proposed scheme of new word extraction, the experimental results indicated that the proposed extraction scheme of new words not only more correctly retrieves new words from the categorized corpora of Google News, but also obtains larger amount of new words. Moreover, the proposed scheme of new word extraction has been applied to automatically expand the lexicon of the Chinese word segmentation system ECScanner (A Chinese Lexicon Scanner with Lexicon Extension). Currently, the ECScanner has been published on the Web to provide Chinese word segmentation service based on Web service. Experimental results also confirmed that ECScanner is superior to CKIP (Chinese knowledge information processing) in identifying meaningful Chinese words.en
dc.format.extent 1511708 bytes-
dc.format.mimetype application/pdf-
dc.language zh_TWen
dc.language.iso en_US-
dc.relation (關聯) Expert Systems with Applications, 36(2), 3641-3651. (SCIE,EI)en
dc.subject (關鍵詞) Natural language processing; New word extraction; Chinese word segmentation; Information retrievalen
dc.title (題名) Automatic Extraction of New Words based on Google News Corpora for Supporting Lexicon-based Chinese Word Segmentation Systems,en
dc.type (資料類型) articleen
dc.identifier.doi (DOI) 10.1016/j.eswa.2008.02.013en_US
dc.doi.uri (DOI) http://dx.doi.org/http://dx.doi.org/10.1016/j.eswa.2008.02.013en_US