{ "paper_id": "O13-3002", "header": { "generated_with": "S2ORC 1.0.0", "date_generated": "2023-01-19T08:03:29.536784Z" }, "title": "Cross-Strait Lexical Differences: A Comparative Study based on Chinese Gigaword Corpus", "authors": [ { "first": "Jia-Fei", "middle": [], "last": "Hong", "suffix": "", "affiliation": { "laboratory": "", "institution": "\u570b\uf9f7\u81fa\u7063\u5e2b\u7bc4\u5927\u5b78 National Taiwan Normal University", "location": {} }, "email": "jiafeihong@gmail.com" }, { "first": "Chu-Ren", "middle": [], "last": "Huang", "suffix": "", "affiliation": {}, "email": "churenhuang@gmail.com" } ], "year": "", "venue": null, "identifiers": {}, "abstract": "Studies of cross-strait lexical differences in the use of Mandarin Chinese reveal that a divergence has become increasingly evident. This divergence is apparent in phonological, semantic, and pragmatic analyses and has become an obstacle to knowledge-sharing and information exchange. Given the wide range of divergences, it seems that Chinese character forms offer the most reliable regular mapping between cross-strait usage contrasts. In this study, we take general cross-strait lexical wordforms to discovery of cross-strait lexical differences and explore their contrasts and variations. Based on Hong and Huang (2006), we discuss the same conceptual words between cross-strait usages by WordNet, Chinese Concept Dictionary (CCD) and Chinese Wordnet (CWN). In this study, we take all words which appear in CCD and CWN to check their lexical contrasts of traditional Chinese character data and simplified Chinese character data in Gigaword Corpus, explore their appearances and distributions, and compare and demonstrate them via Google website.", "pdf_parse": { "paper_id": "O13-3002", "_pdf_hash": "", "abstract": [ { "text": "Studies of cross-strait lexical differences in the use of Mandarin Chinese reveal that a divergence has become increasingly evident. This divergence is apparent in phonological, semantic, and pragmatic analyses and has become an obstacle to knowledge-sharing and information exchange. Given the wide range of divergences, it seems that Chinese character forms offer the most reliable regular mapping between cross-strait usage contrasts. In this study, we take general cross-strait lexical wordforms to discovery of cross-strait lexical differences and explore their contrasts and variations. Based on Hong and Huang (2006), we discuss the same conceptual words between cross-strait usages by WordNet, Chinese Concept Dictionary (CCD) and Chinese Wordnet (CWN). In this study, we take all words which appear in CCD and CWN to check their lexical contrasts of traditional Chinese character data and simplified Chinese character data in Gigaword Corpus, explore their appearances and distributions, and compare and demonstrate them via Google website.", "cite_spans": [], "ref_spans": [], "eq_spans": [], "section": "Abstract", "sec_num": null } ], "body_text": [ { "text": "WordNet \u662f\u4e00\u500b\u96fb\u5b50\u8a5e\u5f59\u5eab\u7684\u8cc7\uf9be\u5eab\uff0c\u662f\u91cd\u8981\u8a9e\uf9be\uf92d\u6e90\u7684\u5176\u4e2d\u4e00\u500b\u8a9e\uf9be\u8cc7\uf9be\u5eab\uff0cWordNet \u7684\u8a2d\u8a08\uf9b3\u611f\u6e90\u81ea\u65bc\u8fd1\u4ee3\u5fc3\uf9e4\u8a9e\u8a00\u5b78\u548c\u4eba\uf9d0\u8a5e\u5f59\u8a18\u61b6\u7684\u8a08\u7b97\uf9e4\uf941\uff0c\u63d0\u4f9b\u7814\u7a76\u8005\u5728\u8a08\u7b97\u8a9e\u8a00 \u6d2a\u5609\u99a1\u3001\u9ec3\u5c45\u4ec1 \u5b78\uff0c\u6587\u672c\u5206\u6790\u548c\u8a31\u8a31\u591a\u591a\u76f8\u95dc\u7684\u7814\u7a76 (Miller et al., 1993; Fellaum, 1998) ", "cite_spans": [ { "start": 111, "end": 132, "text": "(Miller et al., 1993;", "ref_id": "BIBREF6" }, { "start": 133, "end": 147, "text": "Fellaum, 1998)", "ref_id": null } ], "ref_spans": [], "eq_spans": [], "section": "WordNet\u548c\u4e2d\u6587\u8a5e\u7db2(CWN)", "sec_num": "3." } ], "back_matter": [], "bib_entries": { "BIBREF1": { "ref_id": "b1", "title": "WordNet: An Electronic Lexical Database", "authors": [ { "first": "C", "middle": [], "last": "Fellbaum", "suffix": "" } ], "year": 1998, "venue": "", "volume": "", "issue": "", "pages": "", "other_ids": {}, "num": null, "urls": [], "raw_text": "Fellbaum, C. (1998). WordNet: An Electronic Lexical Database. Cambridge: MIT Press.", "links": null }, "BIBREF2": { "ref_id": "b2", "title": "WordNet Based Comparison of Language Variation -A study based on CCD and CWN. Presented at Global WordNet (GWC-06)", "authors": [ { "first": "J.-F", "middle": [], "last": "Hong", "suffix": "" }, { "first": "C.-R", "middle": [], "last": "Huang", "suffix": "" } ], "year": 2006, "venue": "", "volume": "", "issue": "", "pages": "61--68", "other_ids": {}, "num": null, "urls": [], "raw_text": "Hong, J.-F., & Huang, C.-R. (2006). WordNet Based Comparison of Language Variation -A study based on CCD and CWN. Presented at Global WordNet (GWC-06). 61-68. January 22-26. Jeju Island, Korea.", "links": null }, "BIBREF3": { "ref_id": "b3", "title": "Sinica BOW: A bilingual ontological wordnet", "authors": [ { "first": "C.-R", "middle": [], "last": "Huang", "suffix": "" }, { "first": "R.-Y", "middle": [], "last": "Chang", "suffix": "" }, { "first": "S", "middle": [], "last": "Li", "suffix": "" } ], "year": 2010, "venue": "Eds. Ontology and the Lexicon. Cambridge Studies in Natural Language Processing", "volume": "", "issue": "", "pages": "", "other_ids": {}, "num": null, "urls": [], "raw_text": "Huang, C.-R., Chang, R.-Y., & Li, S.-b. (2010). Sinica BOW: A bilingual ontological wordnet. In: Chu-Ren Huang et al. Eds. Ontology and the Lexicon. Cambridge Studies in Natural Language Processing. Cambridge: Cambridge University Press.", "links": null }, "BIBREF4": { "ref_id": "b4", "title": "Cross-lingual Portability of Semantic relations: Bootstrapping Chinese WordNet with English WordNet Relations. Languages and Linguistics", "authors": [ { "first": "C.-R", "middle": [], "last": "Huang", "suffix": "" }, { "first": "E", "middle": [ "I J" ], "last": "Tseng", "suffix": "" }, { "first": "D", "middle": [ "B S" ], "last": "Tsai", "suffix": "" }, { "first": "B", "middle": [], "last": "Murphy", "suffix": "" } ], "year": 2003, "venue": "", "volume": "4", "issue": "", "pages": "509--532", "other_ids": {}, "num": null, "urls": [], "raw_text": "Huang, C.-R., Tseng, E. I. J., Tsai, D. B. S., & Murphy, B. (2003). Cross-lingual Portability of Semantic relations: Bootstrapping Chinese WordNet with English WordNet Relations. Languages and Linguistics. 4(3), 509-532.", "links": null }, "BIBREF5": { "ref_id": "b5", "title": "Chinese Word Sketches. ASIALEX 2005: Words in Asian Cultural Context. June 1-3. Singapore. Lexical Data Consortium. 2005. 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Chinese Gigaword Corpus 2.5.: http://www.ldc.upenn.edu/Catalog/CatalogEntry.jsp?catalogId=LDC2005T14.", "links": null }, "BIBREF6": { "ref_id": "b6", "title": "Introduction to WordNet: An On-line Lexical Database", "authors": [ { "first": "G", "middle": [ "A" ], "last": "Miller", "suffix": "" }, { "first": "R", "middle": [], "last": "Beckwith", "suffix": "" }, { "first": "C", "middle": [], "last": "Fellbaum", "suffix": "" }, { "first": "D", "middle": [], "last": "Gross", "suffix": "" }, { "first": "K", "middle": [], "last": "Miller", "suffix": "" } ], "year": 1993, "venue": "Proceedings of the fifteenth International Joint Conference on Artificial Intelligence", "volume": "", "issue": "", "pages": "", "other_ids": {}, "num": null, "urls": [], "raw_text": "Miller, G. A., Beckwith, R., Fellbaum, C., Gross, D., & Miller, K. (1993). Introduction to WordNet: An On-line Lexical Database. In Proceedings of the fifteenth International Joint Conference on Artificial Intelligence.", "links": null }, "BIBREF8": { "ref_id": "b8", "title": "\u4e8e\u6c5f\u751f\u3001\uf9c7\u63da\u3001\u4fde\u58eb\u6c76(2003)\u3002\u4e2d\u6587\u6982\uf9a3\u8a5e\u5178\u898f\u683c\uf96f\u660e\u3002Journal of Chinese language and Computing", "authors": [], "year": null, "venue": "", "volume": "13", "issue": "", "pages": "177--194", "other_ids": {}, "num": null, "urls": [], "raw_text": "\u4e8e\u6c5f\u751f\u3001\uf9c7\u63da\u3001\u4fde\u58eb\u6c76(2003)\u3002\u4e2d\u6587\u6982\uf9a3\u8a5e\u5178\u898f\u683c\uf96f\u660e\u3002Journal of Chinese language and Computing, 13(2), 177-194\u3002", "links": null }, "BIBREF10": { "ref_id": "b10", "title": "\u3002\u8a9e\uf9be\u5eab\u70ba\u672c\u7684\uf978\u5cb8\u5c0d\u61c9\u8a5e\u5f59\u767c\u6398(A Corpus-Based Approach to the Discovery of Cross-Strait Lexical Contrasts)", "authors": [ { "first": "", "middle": [], "last": "\u6d2a\u5609\u99a1\u3001\u9ec3\u5c45\u4ec1", "suffix": "" } ], "year": 1999, "venue": "Taipei, Nankang: Institute of Linguistics", "volume": "9", "issue": "", "pages": "221--238", "other_ids": {}, "num": null, "urls": [], "raw_text": "\u6d2a\u5609\u99a1\u3001\u9ec3\u5c45\u4ec1(2008)\u3002\u8a9e\uf9be\u5eab\u70ba\u672c\u7684\uf978\u5cb8\u5c0d\u61c9\u8a5e\u5f59\u767c\u6398(A Corpus-Based Approach to the Discovery of Cross-Strait Lexical Contrasts). 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Taipei, Nankang: Institute of Linguistics, Academia Sinica\u3002 \u8a31\u6590\u7d62(1999)\u3002\u53f0\u7063\u7576\u4ee3\u570b\u8a9e\u65b0\u8a5e\u63a2\u5fae\u3002\u570b\uf9f7\u53f0\u7063\u5e2b\u7bc4\u5927\u5b78\u83ef\u8207\u6587\u6559\u5b78\u7814\u7a76\u6240\u78a9\u58eb\uf941\u6587\uff0c \u53f0\uf963\u3002", "links": null }, "BIBREF13": { "ref_id": "b13", "title": "\u6234\u51f1\u5cf0(1996)\u3002\u5f9e\u8a9e\u8a00\u5b78\u7684\u89c0\u9ede\u63a2\u8a0e\u53f0\u7063\u8207\uf963\u4eac\u570b\u8a9e\u9593\u4e4b\u5dee\uf962[A Linguistic Study of Taiwan and Beijing Mandarin", "authors": [], "year": null, "venue": "", "volume": "", "issue": "", "pages": "", "other_ids": {}, "num": null, "urls": [], "raw_text": "\u6234\u51f1\u5cf0(1996)\u3002\u5f9e\u8a9e\u8a00\u5b78\u7684\u89c0\u9ede\u63a2\u8a0e\u53f0\u7063\u8207\uf963\u4eac\u570b\u8a9e\u9593\u4e4b\u5dee\uf962[A Linguistic Study of Taiwan and Beijing 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\u641c\u5c0b\u7db2\u9801\u7684\u8cc7\uf9be\u9032\ufa08\uf978\u5cb8\u4eba\u6c11\u4f7f\u7528\u8a5e\u5f59\u7684\u5c0d\u6bd4\u8207 \u5dee\uf962\u5206\u6790\uff0c\u5728\u6b64\uff0c\u767c\u73fe\u5177\u6709\u5b78\u8853\u6027\u8cea\u7684\u8a9e\uf9be\u5eab\uff0c\u5982\u672c\u6587\u6240\u4f7f\u7528\u7684 Gigaword Corpus \u5728\u4f5c \u70ba\uf978\u5cb8\u8a5e\u5f59\u5c0d\u6bd4\u7814\u7a76\u6216\u4e16\u754c\u83ef\u8a9e\u5c0d\u6bd4\u7814\u7a76\u6642\uff0c\u5176\u7814\u7a76\u6210\u679c\u8207\u5b78\u8853\u50f9\u503c\u662f\u6bd4 Google \u6240\u63d0\u4f9b \u7684\u8cc7\uf9be\u9ad8\u5f88\u591a\u7684\u3002", "num": null, "content": "
26 30\u4ee5\u4e2d\u6587\u5341\u5104\u8a5e\u8a9e\uf9be\u5eab\u70ba\u57fa\u790e\u4e4b\uf978\u5cb8\u8a5e\u5f59\u5c0d\u6bd4\u7814\u7a76 \u4ee5\u4e2d\u6587\u5341\u5104\u8a5e\u8a9e\uf9be\u5eab\u70ba\u57fa\u790e\u4e4b\uf978\u5cb8\u8a5e\u5f59\u5c0d\u6bd4\u7814\u7a76 \u4ee5\u4e2d\u6587\u5341\u5104\u8a5e\u8a9e\uf9be\u5eab\u70ba\u57fa\u790e\u4e4b\uf978\u5cb8\u8a5e\u5f59\u5c0d\u6bd4\u7814\u7a76 \u4ee5\u4e2d\u6587\u5341\u5104\u8a5e\u8a9e\uf9be\u5eab\u70ba\u57fa\u790e\u4e4b\uf978\u5cb8\u8a5e\u5f59\u5c0d\u6bd4\u7814\u7a76 \u4ee5\u4e2d\u6587\u5341\u5104\u8a5e\u8a9e\uf9be\u5eab\u70ba\u57fa\u790e\u4e4b\uf978\u5cb8\u8a5e\u5f59\u5c0d\u6bd4\u7814\u7a7623 \u6d2a\u5609\u99a1\u3001\u9ec3\u5c45\u4ec1 25 \u6d2a\u5609\u99a1\u3001\u9ec3\u5c45\u4ec1 27 \u6d2a\u5609\u99a1\u3001\u9ec3\u5c45\u4ec1 29 \u6d2a\u5609\u99a1\u3001\u9ec3\u5c45\u4ec1 31 \u6d2a\u5609\u99a1\u3001\u9ec3\u5c45\u4ec1
\u3002\u5728 WordNet \u4e2d\uff0c \u540d\u8a5e\u3001\u52d5\u8a5e\u3001\u5f62\u5bb9\u8a5e\u3001\u526f\u8a5e\uff0c\u9019\u56db\u500b\uf967\u540c\u7684\u8a5e\uf9d0\uff0c\u5206\u5225\u8a2d\u8a08\u3001\u7d44\u5408\u6210\u540c\u7fa9\u8a5e\u96c6(synsets) \u7684\u683c\u5f0f\uff0c\u5448\u73fe\u51fa\u6700\u57fa\u672c\u7684\u8a5e\u5f59\u6982\uf9a3\uff0c\u5728\u9019\u7576\u4e2d\uff0c\u4ee5\uf967\u540c\u7684\u8a9e\u7fa9\u95dc\u4fc2\uf99a\u7d50\u5404\u7a2e\uf967\u540c\u7684\u540c\u7fa9 \u8a5e\u96c6\uff0c\uf905\u6210\uf9ba WordNet \u7684\u6574\u500b\u67b6\u69cb\uff0c\u4e5f\u5448\u73fe\uf9ba WordNet \u6574\u500b\u5168\u8c8c\u3002 \u81ea\u5f9e Miller \u7b49\u4eba (1993)\u3001Fellaum (1998) \u767c\u5c55 WordNet \u4ee5\uf92d\uff0cWordNet \u5c31\u6301\u7e8c\uf967\u65b7 \u5730\uf901\u65b0\u7248\u672c\uff0c\u76ee\u524d\u6700\u65b0\u7684\u7248\u672c\u662f WordNet 3.0 \u7248\uff0c\u9019\u4e9b\u7248\u672c\u9593\u7684\u5dee\uf962\uff0c\u5305\u62ec\uf9ba\u540c\u7fa9\u8a5e\u96c6 \u7684\uf97e\u548c\u4ed6\u5011\u7684\u8a5e\u5f59\u5b9a\u7fa9\u3002\u7136\u800c\uff0c\u5c0d\u65bc\u62ff WordNet \uf92d\u505a\u7814\u7a76\u8a9e\uf9be\u7684\u5b78\u8005\uff0c\u591a\uf969\u9084\u662f\u4ee5 WordNet 1.6 \u7248\u70ba\u6700\u591a\uff0c\u56e0\u70ba\u9019\u500b\u7248\u672c\u662f\u76ee\u524d\u6700\u591a\u8a08\u7b97\u8a9e\u8a00\u5b78\u5b78\u8005\u4f7f\u7528\u7684\u3002\u5728 WordNet 1.6 \u7248\uf9e8\uff0c\u6709\u5c07\u8fd1 100,000 \u7684\u540c\u7fa9\u8a5e\u96c6\u3002 \u6211\u5011\u77e5\u9053\uff0c\u96d9\u8a9e\uf9b4\u57df\u5206\uf9d0\uff0c\u53ef\u4ee5\u589e\u52a0\u6211\u5011\u5404\u7a2e\uf9b4\u57df\u8a5e\u5f59\u5eab\u7684\u767c\u5c55\uff0c\u540c\u6a23\u7684\uff0c\u5728\u4e0a\u4e00 \u6bb5\u7684\u5167\u5bb9\uff0c\u6211\u5011\u4e5f\u63d0\u5230\u95dc\u65bc\u4ee5 WordNet \u70ba\u57fa\u790e\uff0c\u767c\u5c55\u51fa\u7e41\u9ad4\u4e2d\u6587\u7cfb\u7d71(Chinese Wordnet, CWN)\u8207\u7c21\u9ad4\u4e2d\u6587\u7cfb\u7d71(Chinese Concept Dictionary, CCD)\u7684\u5c0d\u8b6f\uff0c\u6211\u5011\u4f7f\u7528\u96d9\u8a9e\u8a5e\u7db2\uff0c\u4f5c \u70ba\u8a5e\u5f59\u77e5\uf9fc\u8cc7\uf9be\u5eab\uf92d\u5be6\u73fe\u3001\u652f\u6301\u6211\u5011\u5728\u8a5e\u5f59\u6982\uf9a3\u4e0a\u7684\u7814\u7a76\u3002 \u5728\u4e2d\u82f1\u96d9\u8a9e\u8a5e\u7db2\u4e2d\uff0c\u6bcf\u4e00\u500b\u82f1\u6587\u7684\u540c\u7fa9\u8a5e\u96c6\uff0c\u6211\u5011\u90fd\u6703\u7d66\u4e88\u4e09\u500b\u6700\u9069\u5408\u4e14\u5c0d\u7b49\u4e2d\u6587 \u7ffb\u8b6f\uff0c\u800c\u9019\u4e9b\u7ffb\u8b6f\uff0c\u5982\u679c\uf967\u5c6c\u65bc\u771f\u6b63\u7684\u540c\u7fa9\u8a5e\uff0c\u6211\u5011\u4e5f\u6703\u6a19\u8a3b\u4ed6\u5011\u7684\u8a9e\u7fa9\u95dc\u4fc2(Huang, Tseng, Tsai & Murphy, 2003)\uff0c\u53c8\u9019\u4e9b\u96d9\u8a9e\u8a5e\u7db2\uff0c\u4e5f\u5728\u4e2d\u7814\u9662\u8a9e\u8a00\u6240\u8a5e\u7db2\u5c0f\u7d44\u5718\u968a\u7684\u767c\u5c55\uff0c \u5c07\u6bcf\u4e00\u500b\u540c\u7fa9\u8a5e\u96c6\u90fd\u8207 SUMO \u6982\uf9a3\u7bc0\u9ede\uf99a\u7d50\uff0c\u9032\u800c\u958b\u767c\u51fa Academia Sinica Bilingual Ontological Wordnet (Sinica BOW) (Huang, Chang & Li, 2010)\u3002\u7576\u6211\u5011\u7121\u6cd5\u76f4\u63a5\u53d6\u5f97\u4e2d\u82f1 \u76f8\u5c0d\u61c9\u7684\u8a5e\u5f59\uff0c\u6211\u5011\u5728\u96d9\u8a9e\u8a5e\u7db2\u7684\u8cc7\uf9be\u5eab\uf9e8\uff0c\u53ef\u4ee5\uf9dd\u7528\u9019\u4e9b\u8a9e\u7fa9\u95dc\u4fc2\uff0c\u9032\u800c\u767c\u5c55\u4e26\u9810\u6e2c \uf9b4\u57df\u5206\uf9d0\u3002 4. WordNet\u548c\u4e2d\u6587\u6982\uf9a3\u8fad\u5178(CCD) CCD\uff0c\u4e2d\u6587\u6982\uf9a3\u8fad\u5178(Chinese Concept Dictionary)\uff0c\u662f\u4e00\u500b\u4e2d\u82f1\u96d9\u8a9e\u7684\u8a5e\u7db2\uff0c\u6574\u500b\u67b6\u69cb\u767c \u5c55\u4e5f\u662f\uf92d\u81ea\u65bc WordNet (\u4e8e\u6c5f\u751f\u8207\u4fde\u58eb\u6c76\uff0c2004\uff1b\u4e8e\u6c5f\u751f\u3001\uf9c7\u63da\u8207\u4fde\u58eb\u6c76\uff0c2003\uff1b\uf9c7\u63da\u3001 \u4fde\u58eb\u6c76\u8207\u4e8e\u6c5f\u751f\uff0c2003)\u3002\u5728 CCD \u7684\u767c\u5c55\u624b\u518a\uf9e8\u8a18\u8f09\uff0c\u7814\u7a76\u5718\u968a\u63cf\u8ff0\u9019\u4e9b\u8a5e\u7fa9\u7684\u9996\u8981\u689d \u4ef6\uff0c\u662f\uf967\u53ef\u4ee5\u7834\u58de\u539f\u672c WordNet \u5c0d\u65bc\u540c\u7fa9\u8a5e\u96c6\u5b9a\u7fa9\u6982\uf9a3\u8207\u5176\u8a9e\u7fa9\u95dc\u4fc2\u7684\u67b6\u69cb\u3002\u53e6\u4e00\u65b9\u9762\uff0c CCD \u7684\u7814\u7a76\u5718\u968a\u8003\uf97e\u5230\u53ef\u4ee5\u5b58\u5728\u8a31\u591a\u5728\u4e2d\u6587\u8207\u82f1\u6587\u7684\uf967\u540c\u63cf\u8ff0\u67b6\u69cb\uff0c\u6240\u4ee5\uff0c\u4ed6\u5011\uf967\u6b62\u8868 \u73fe\u5c0d\u4e2d\u6587\u8a5e\u5f59\u5167\u6db5\u7684\u8868\u9054\uff0c\u4e5f\u767c\u5c55\uf9ba\u4e2d\u6587\u8a5e\u5f59\u8a9e\u7fa9\u8207\u6982\uf9a3\u7684\u95dc\u4fc2\u6027\uff0c\u4ee5\uf9dd\u65bc\u5f37\u8abf\u4e2d\u6587\u7684 \u7279\u8cea\u3002 CCD \u7684\u7814\u7a76\u5718\u968a\u5c08\u6ce8\u5728\u6574\u500b CCD \u7684\u67b6\u69cb\uff0c\u63d0\u51fa\u540c\u4e00\u6982\uf9a3\u7684\u540c\u7fa9\u8a5e\u96c6\u7684\u5b9a\u7fa9\uff0c\u5176\u6240 \u5448\u73fe\u7684\u6982\uf9a3\u3001\u5b9a\u7fa9\u548c\u6982\uf9a3\u7db2\u7684\u4e0a\u4e0b\u4f4d\u8a9e\u7fa9\u95dc\u4fc2\uff0c\u6bcf\u4e00\u500b\u540c\u7fa9\u8a5e\u96c6\u90fd\u6709\u5176\u57fa\u672c\u95dc\u4fc2\uff0c\u5f7c\u6b64 \u4e4b\u9593\u4ea6\u6709\u8a9e\u7fa9\u95dc\u4fc2\u7684\u5b58\u5728\u3002\u81f3\u65bc CCD \u7684\uf913\u8f2f\u63a8\u6f14\u539f\u5247\u5728\u8a9e\u7fa9\u7db2\u4e0a\u7684\u5448\u73fe\uff0c\u662f\u904b\u7528\u5230\uf969\u5b78 \u7684\u5f62\u5f0f\u800c\uf92d\u7684\uff0c\u662f\u53ef\u4ee5\u5e6b\u52a9\u7814\u7a76\u8005\u5728\u4e2d\u6587\u8a9e\u7fa9\u5206\u6790\u4e0a\u7684\u4f7f\u7528\u3002 \u81ea\u5f9e 2000/09 \u958b\u59cb\uff0c\uf963\u4eac\u5927\u5b78\u8a08\u7b97\u8a9e\u8a00\u5b78\u7814\u7a76\u6240\u5c31\u5df2\u7d93\u958b\u59cb\u8457\u624b\u4ee5 WordNet \u70ba\u57fa \u6e96\uff0c\u7814\u7a76 CCD\uff0c\u4e26\u5efa\uf9f7\u4e00\u500b\u4e2d\u82f1\u96d9\u8a9e\u7684\u8a5e\u7db2\uff0c\u4e00\u500b\u53ef\u4ee5\u63d0\u4f9b\u5404\u7a2e\uf967\u540c\u7814\u7a76\u7684\u8a5e\u7db2\uff0c\u5982\u6a5f \u5668\u7ffb\u8b6f(MT)\uff0c\u8a0a\u606f\u64f7\u53d6(IE)\u2026\u7b49\u7b49\u3002 \u57fa\u65bc WordNet \u82f1\u6587\u6982\uf9a3\u8207 CCD \u4e2d\u6587\u6982\uf9a3\u662f\u5c6c\u65bc\uf978\u500b\uf967\u540c\u77e5\uf9fc\u80cc\u666f\uff0c\u4e5f\u56e0\u6b64 CCD \u4e2d\uff0c\u4ed6\u5011\uf978\u8005\u9593\u7684\u76f8\u4e92\u95dc\u4fc2\u8207\u6982\uf9a3\uff0c\u662f\u975e\u5e38\u8907\u96dc\u3001\u7e41\u7463\u7684\u3002CCD \u5305\u62ec\uf9ba\u5927\uf97e\u4e14\u7e41\u96dc\u7684\u6210 \u5c0d\u3001\u6210\u7d44\u7684\u5c0f\u7db2\u7d61\uff0c\u5927\u81f4\u4e0a\uff0c\u5dee\uf967\u591a\u6709 10 5 \u7684\u6982\uf9a3\u7bc0\u9ede\u548c 10 6 \u7684\u6210\u7d44\u5c0f\u7db2\u7d61\u7684\u6982\uf9a3\u95dc\u4fc2\uff0c \u4ed6\u5011\u7684\u95dc\u4fc2\uff0c\u5448\u73fe\u5982\u4e0b\u5716\uff1a \u5716 1. WordNet \u5c0f\u7db2\u7d61\u4e2d\u8907\u96dc\u7684\u95dc\u4fc2\u7d50\u69cb 5. \u6587\u737b\u63a2\u8a0e \u5c0d\u65bc\uf978\u5cb8\u8a5e\u5f59\u5c0d\u6bd4\u7684\u63a2\u8a0e\uff0c\u904e\u53bb\u7684\u7814\u7a76\uff0c\u591a\u534a\u8457\u91cd\u5728\u8868\u9762\u8a9e\u8a00\u7279\u5fb5\u7684\u5340\u5225\u3002\u5982\uf99c\u8209\u8a9e\u97f3 \u65b9\u9762\u3001\u8a5e\u5f59\u65b9\u9762\u7684\u5c0d\u6bd4(\u5357\u4eac\u8a9e\u8a00\u6587\u5b57\u7db2\uff0c2004)\uff1b\u6216\u4ee5\u8a9e\u97f3\u3001\u8a5e\u5f59\u3001\u8a9e\u6cd5\u53ca\u8868\u9054\u65b9\u5f0f\u7b49 \u65b9\u9762\uf92d\u5206\u6790\u8a9e\u8a00\u5dee\uf962\u7684\u73fe\u8c61 (\u5982\uff1a\u738b\u9435\u6606\u8207\uf9e1\ufa08\u5065\uff0c1996\uff1b\u59da\u69ae\u677e\uff0c1997\uff1b\u8a31\u6590\u7d62\uff0c1999\uff1b \u6234\u51f1\u5cf0\uff0c1996)\u3002 \u8fd1\uf98e\uf92d\uff0c\u5c0d\u65bc\uf978\u5cb8\u8a5e\u5f59\u5c0d\u6bd4\u7684\u7814\u7a76\uff0c\u6bd4\u8f03\u65b0\u7684\u7814\u7a76\u65b9\u6cd5\uff0c\u662f\u4ee5 WordNet \u70ba\u57fa\u790e\uff0c\u53d6 \uf978\u5cb8\u8a9e\uf9be\u5eab\u8cc7\uf9be\u4f5c\u6bd4\u8f03\uff0c\u9032\u800c\u5206\u6790\uf978\u5cb8\u8a5e\u5f59\u7684\u5c0d\u6bd4(\u5982\uff1aHong & Huang, 2006)\uff1b\u6216\u4ee5 Chinese Gigaword Corpus (2005)\u70ba\u57fa\u790e\uff0c\u63a2\uf96a\uf978\u5cb8\u5c0d\u65bc\u6f22\u8a9e\u8a5e\u5f59\u5728\u4f7f\u7528\u4e0a\u7684\u5dee\uf962\u73fe\u8c61\uff0c\uf9b5 \u5982\uff1a\u76f8\u95dc\u5171\u73fe\u8a5e\u5f59(collocation)\u7684\u5dee\uf962\u3001\u53f0\u7063\u6216\u5927\uf9d3\u7368\u7528\u7684\u5dee\uf962\u3001\u7279\u5b9a\u8a9e\u5883\u4e0b\u7684\u7279\u6b8a\u7528\u6cd5 \u7684\u5dee\uf962\u3001\u8a9e\u8a00\u4f7f\u7528\u7fd2\u6163\u7684\u5dee\uf962\u7b49\u7b49(\u5982\uff1a\u6d2a\u5609\u99a1\u8207\u9ec3\u5c45\u4ec1\uff0c2008)\u3002 6. \u7814\u7a76\u65b9\u6cd5 \u672c\u7814\u7a76\u4ee5\u82f1\u6587\u7684 WordNet\u3001\u7e41\u9ad4\u4e2d\u6587\u7cfb\u7d71\u7684\u4e2d\u6587\u8a5e\u7db2(CWN)\u3001\u4ee5\u53ca\u7c21\u9ad4\u4e2d\u6587\u7cfb\u7d71\u7684\u4e2d\u6587 \u6982\uf9a3\u8fad\u5178(CCD)\u7b49\u4e09\u5927\u8cc7\uf9be\u5eab\u70ba\u4e3b\uff0c\u5c0d\u65bc\u7e41\u9ad4\u4e2d\u6587\u7cfb\u7d71\u7684\u82f1\u4e2d\u5c0d\u8b6f\u8207\u7c21\u9ad4\u4e2d\u6587\u7cfb\u7d71\u7684\u82f1 \u4e2d\u5c0d\u8b6f\uff0c\u6211\u5011\u5148\u9032\ufa08\u6bd4\u5c0d\uff0c\u8a66\u5716\u5728\u6bd4\u5c0d\u4e2d\uff0c\u5c0b\u627e\u51fa\uf978\u8005\u4e4b\u9593\u7684\u5dee\u5225\u8207\u4f7f\u7528\u5206\u4f48\u3002 \u76f8\u540c\u7684\u6982\uf9a3\uff0c\u672c\u6b78\u5c6c\u65bc\u4e00\u500b\u540c\u7fa9\u8a5e\u96c6\uff0c\u4f46\u56e0\uf978\u5cb8\u5728\u8a5e\u5f59\u4f7f\u7528\u4e0a\u7684\u5dee\uf962\uff0c\u800c\u6709\u6240\uf967\u540c\uff0c \u5118\u7ba1\u5982\u6b64\uff0c\u4ecd\u820a\u6709\u4e00\u4e9b\uf978\u5cb8\u4f7f\u7528\u76f8\u540c\u7684\u8a5e\u5f59\uf92d\u8868\u9054\u76f8\u540c\u7684\u6982\uf9a3\u8a9e\u7fa9\u3002\u672c\u6587\u4e2d\u5c07\u5f9e\u7e41\u9ad4\u4e2d \u6587\u7cfb\u7d71\u8207\u7c21\u9ad4\u4e2d\u6587\u7cfb\u7d71\u7684\u82f1\u4e2d\u5c0d\u8b6f\u8cc7\uf9be\uf9e8\uff0c\u96c6\u4e2d\u63a2\u7a76\u540c\u4e00\u500b\u540c\u7fa9\u8a5e\u96c6\uff0c\u5728\uf978\u5cb8\u4f7f\u7528\u7684\u8a5e \u5f59\u662f\u5b8c\u5168\u76f8\u540c\u3001\u5b8c\u5168\uf967\u540c\u7684\uf9fa\u6cc1\u3002\u7136\u5f8c\uff0c\u518d\u5c07\u9019\u4e9b\u5b8c\u5168\u76f8\u540c\u3001\u5b8c\u5168\uf967\u540c\u7684\u8a5e\u5f59\uff0c\u4ee5 Gigaword Corpus \u70ba\u57fa\u790e\uff0c\u5206\u6790\u9019\u4e9b\u8a5e\u5f59\u5728\u9019\u500b\u8a9e\uf9be\u5eab\uf9e8\uff0c\u6240\u5448\u73fe\u51fa\uf978\u5cb8\u4f7f\u7528\u7684\uf9fa\u6cc1\u3002 \u63a5\u8457\uff0c\u672c\u6587\u518d\u4ee5\u8a9e\uf9be\u5eab\u70ba\u7814\u7a76\u51fa\u767c\u9ede\uff0c\u662f\u4ee5\u7d04\u5341\u56db\u5104\u5b57\u7684 Chinese Gigaword Corpus \u70ba\u4e3b\u8981\u8a9e\uf9be\uf92d\u6e90\uff0c\u4ee5\u4e2d\u6587\u8a5e\u5f59\u901f\u63cf\u70ba\u641c\u5c0b\u8a9e\uf9be\u5de5\u5177 Chinese Gigaword Corpus (2005)\u3001 Chinese Word Sketch Engine\u3001Kilgarriff et al. (2005)\u3002Chinese Gigaword Corpus \u5305\u542b\uf9ba\u5206 \u5225\uf92d\u81ea\u5927\uf9d3\u3001\u81fa\u7063\u3001\u65b0\u52a0\u5761\u7684\u5927\uf97e\u8a9e\uf9be\uff0c\u5305\u62ec\u7d04 5 \u5104\u5b57\u65b0\u83ef\u793e\u8cc7\uf9be(XIN)\u3001\u7d04 8 \u5104\u5b57\u4e2d\u592e \u793e\u8cc7\uf9be(CNA)\uff0c\u53ca\u7d04 3 \u5343\u842c\u5b57\u65b0\u52a0\u5761\uf997\u5408\u65e9\u5831\u8cc7\uf9be(Zaobao)\u3002\u672c\u7814\u7a76\uff0c\u50c5\u5c31\u5927\uf9d3\u65b0\u83ef\u793e\u8cc7 \uf9be\u8207\u81fa\u7063\u4e2d\u592e\u793e\u8cc7\uf9be\u9032\ufa08\u6bd4\u5c0d\uff0c\u56e0\u6b64\uff0c\u672c\u6587\u7814\u7a76\u53ef\u4ee5\u63d0\u4f9b\uf978\u5cb8\u8a5e\u5f59\u5dee\uf962\u7684\u5927\uf97e\u8a5e\u5f59\u8b49\u64da\u3002 \u6700\u5f8c\uff0c\u672c\u6587\u4ea6\u8996 Google \u70ba\u4e00\u500b\u64c1\u6709\u5927\uf97e\u7e41\u9ad4\u4e2d\u6587\u3001\u7c21\u9ad4\u4e2d\u6587\u7684\u8a9e\uf9be\u5eab\uff0c\u8a66\u5716\u6839\u64da Google \u6240\u641c\u5c0b\u5230\u7684\u7e41\u9ad4\u4e2d\u6587\u7db2\u9801\u8207\u7c21\u9ad4\u4e2d\u6587\u7db2\u9801\u7684\u8cc7\uf9be\uff0c\u9032\ufa08\u4e26\u9a57\u8b49\uf978\u5cb8\u5728\u8a5e\u5f59\u4f7f\u7528\u5dee \uf962\u4e0a\u7684\u5be6\u969b\u4f7f\u7528\u8b49\u64da\u3002 \u70ba\uf9ba\u53ef\u4ee5\u6bd4\u8f03 Chinese Gigaword Corpus \u7684\u7e41\u9ad4\u4e2d\u6587\u8207\u7c21\u9ad4\u4e2d\u6587\uff0c\u53ca\uf978\u8005\u7684\u4f7f\u7528\u5dee\uf962 \u6027\uff0c\u6211\u5011\u63a1\u7528\u4e2d\u6587\u8a5e\u5f59\u901f\u63cf\u7cfb\u7d71(Chinese Word Sketch)\u9032\ufa08\u6aa2\u9a57\u3002\u4e2d\u6587\u8a5e\u5f59\u901f\u63cf\u7cfb\u7d71\uf9e8\uff0c \u6709\u56db\u5927\u641c\u5c0b\u529f\u80fd\uff0c\u5206\u5225\u70ba\uff1aconcordance\u3001word sketch\u3001Thesaurus\u3001Sketch-Diff\uff0c\u5176\u4e2d \u300cSketch-Diff\u300d\u9019\u500b\u529f\u80fd\u5c31\u662f\u6bd4\u8f03\u8a5e\u5f59\u5dee\uf962\u7684\u5de5\u5177\uff0c\u53ef\u4ee5\u770b\u51fa\uf978\u5cb8\u5c0d\u65bc\u540c\u4e00\u6982\uf9a3\u800c\u4f7f\u7528 \uf967\u540c\u8a5e\u5f59\u7684\u5be6\u969b\uf9fa\u6cc1\u8207\u5206\u4f48\uff0c\u4e5f\u53ef\u4ee5\u770b\u51fa\u540c\u4e00\u8a9e\u7fa9\u8a5e\u5f59\u5728\uf978\u5cb8\u7684\u5be6\u969b\u8a9e\uf9be\u4e2d\uff0c\u6240\u5448\u73fe\u7684 \u76f8\u540c\u9ede\u8207\u5dee\uf962\u6027\u3002\u6211\u5011\u4e3b\u8981\uf9dd\u7528\u4e2d\u6587\u8a5e\u5f59\u901f\u63cf\u4e2d\u8a5e\u5f59\u901f\u63cf\u5dee\uf962(word sketch difference)\u7684 \u529f\u80fd\u3002\u8a5e\u5f59\u901f\u63cf\u5dee\uf962\u7684\u5be6\u969b\u64cd\u4f5c\u4ecb\u9762\uff0c\u5982\u5716 2\uff1a \u5716 2. \u4e2d\u6587\u8a5e\u5f59\u901f\u63cf\u7cfb\u7d71\u7684\u8a5e\u5f59\u63cf\u7d20\u5c0d\u6bd4 \u5728\u6b64\u529f\u80fd\u4e0b\uff0c\u6211\u5011\u5c07\u5df2\u7d93\u6bd4\u5c0d\u904e CCD \u8207 CWN \u5c0d\u61c9\uf967\u540c\u5c0d\u8b6f\u7684\u8a5e\u5f59\uff0c\u9032\u4e00\u6b65\u63a2\u7a76\uf978 \u8a5e\u5f59\u7684\u4f7f\u7528\uf9fa\u6cc1\u8207\u5206\u4f48\u3002\u5728\u672c\u6587\u4e2d\uff0c\u4e3b\u8981\u662f\u4ee5\u6bd4\u5c0d\uf978\u5cb8\u8a5e\u5f59\u8a5e\u983b\u70ba\u4e3b\uff0c\u5018\uf974\u5728 CCD \u8207 CWN \u7684\u5c0d\u61c9\u4e2d\uff0c\u78ba\u5be6\u662f\u76f8\u540c\u8a9e\u7fa9\uff0c\u537b\u5728\uf978\u5cb8\u4f7f\u7528\u5b8c\u5168\u76f8\u540c\u6216\u5b8c\u5168\uf967\u540c\u7684\u8a5e\u5f59\uff0c\u90a3\u9ebc\u5176\u5404 \u81ea\u4f7f\u7528\u7684\u8a5e\u5f59\uff0c\u5728 Gigaword Corpus \uf9e8\u7e41\u9ad4\u8a9e\uf9be\u8207\u7c21\u9ad4\u8a9e\uf9be\u4ea4\u53c9\u6bd4\u5c0d\u5f8c\u6240\u5f97\u7684\u8a5e\u983b\uff0c\u4e5f \u61c9\u7576\u6703\u6709\u8fd1\u4f3c\u7684\u5206\u4f48\u73fe\u8c61\uff0c\u85c9\u6b64\uf969\u64da\uff0c\uf967\u4f46\u53ef\u4ee5\u8b49\u660e CCD \u548c CWN \u5728\u82f1\u4e2d\u5c0d\u8b6f\u4e0a\uff0c\u7e41\u9ad4 \u4e2d\u6587\u7cfb\u7d71\u8207\u7c21\u9ad4\u4e2d\u6587\u7cfb\u7d71\uff0c\u662f\u6709\u5dee\u5225\u7684\uff0c\u4e5f\u53ef\u4ee5\u8b49\u660e\uff0c\u78ba\u5be6\u6709\uf978\u5cb8\u4f7f\u7528\uf967\u540c\u8a5e\u5f59\uf92d\u8868\u9054 \u76f8\u540c\u6982\uf9a3\u8a9e\u7fa9\u7684\u7528\u6cd5\uff0c\u9032\u800c\uf9ba\u89e3\uf978\u5cb8\u8a5e\u5f59\u7684\u5be6\u969b\u73fe\u8c61\uff0c\u4ee5\u9032\ufa08\u672c\u7814\u7a76\u7684\u5206\u6790\u3002 7. CCD\u8207CWN\u8a9e\uf9be\u5206\u6790 \u7e41\u9ad4\u4e2d\u6587\u7cfb\u7d71\u7684\u82f1\u4e2d\u5c0d\u8b6f(CWN)\u8207\u7c21\u9ad4\u4e2d\u6587\u7cfb\u7d71\u7684\u82f1\u4e2d\u5c0d\u8b6f(CCD)\uff0c\u4f9d\uf967\u540c\u8a5e\uf9d0\uff0c\u5340\u5206 \u6210\uff1a\u540d\u8a5e\u3001\u52d5\u8a5e\u3001\u5f62\u5bb9\u8a5e\u548c\u526f\u8a5e\u56db\u5927\uf9d0\uf92d\u9032\ufa08\u5c0d\u6bd4\uff0c\u4ee5 WordNet \u70ba\u4e3b\uff0c\u6aa2\u6e2c\u5728\u540c\u4e00\u500b\u540c \u7fa9\u8a5e\u96c6\u4e2d(Synset)\uff0c\u7e41\u9ad4\u4e2d\u6587\u7cfb\u7d71\u7684\u5c0d\u8b6f\u8a5e\u5f59\u548c\u7c21\u9ad4\u4e2d\u6587\u7cfb\u7d71\u7684\u5c0d\u8b6f\u8a5e\u5f59\uff0c\u7136\u5f8c\u518d\u9032\ufa08\u6bd4 \u5c0d\u3002 \u5728\u56db\u5927\u8a5e\uf9d0\u4e2d\uff0c\u6211\u5011\u53ef\u4ee5\u6e05\u695a\u5f97\u77e5\uff0c\u5728\u540c\u4e00\u500b\u540c\u7fa9\u8a5e\u96c6\u4e2d(Synset)\uff0c\u7e41\u9ad4\u4e2d\u6587\u7cfb\u7d71\uff0c \u53ef\u80fd\u6709\u591a\u500b\u76f8\u5c0d\u61c9\u7684\u5c0d\u8b6f\u8a5e\u5f59\uff0c\u540c\u6a23\u5730\uff0c\u7c21\u9ad4\u4e2d\u6587\u7cfb\u7d71\u4e5f\u53ef\u80fd\u6709\u500b\u76f8\u5c0d\u61c9\u7684\u5c0d\u8b6f\u8a5e\u5f59\u3002 \u5728\u9019\u4e9b\u5c0d\u8b6f\u8a5e\u5f59\uf9e8\uff0c\u53c8\u6709\u53ef\u80fd\u662f\uf978\u908a\u4f7f\u7528\u7684\u5c0d\u8b6f\u8a5e\u5f59\u5b8c\u5168\u4e00\u6a23\uff0c\u7a31\u4e4b\u300c\u5b8c\u5168\u76f8\u540c\u300d\uff1b\u5982 \u679c\uff0c\uf978\u908a\u4f7f\u7528\u7684\u5c0d\u8b6f\u8a5e\u5f59\uff0c\u6c92\u6709\u4e00\u500b\u76f8\u540c\u7684\uff0c\u7a31\u4e4b\u300c\u5b8c\u5168\uf967\u540c\u300d\uff0c\u4e5f\u5c31\u662f\u300c\u771f\u6b63\uf967\u540c\u300d\uff1b \u6216\u8005\uff0c\u53ea\u6709\u4f7f\u7528\u5176\u4e2d\u4e00\u500b\u6216\u4e00\u500b\u4ee5\u4e0a\u5c0d\u8b6f\u8a5e\u5f59\uff0c\u9019\u500b\uf9fa\u6cc1\uff0c\u7a31\u4e4b\u300c\u90e8\u4efd\u76f8\u540c\u300d\uff0c\u800c\u5728\u300c\u90e8 \u4efd\u76f8\u540c\u300d\u7684\u5c0d\u8b6f\u8a5e\u5f59\uff0c\u5982\u679c\uf978\u908a\u7684\u5c0d\u8b6f\u8a5e\u5f59\u4f7f\u7528\u7684\u8a5e\u9996\u76f8\u540c\uff0c\u7a31\u4e4b\u300c\u8a5e\u9996\u76f8\u540c\u300d\uff0c\u5982\u679c \u53ea\u662f\u4f7f\u7528\u5230\u76f8\u540c\u7684\u5b57\uff0c\u5247\u7a31\u4e4b\u300c\u90e8\u4efd\u5b57\u5143\u76f8\u540c\u300d\uff0c\u5982\uff1a Synset CCD \u5c0d\u8b6f\u8a5e\u5f59 CWN \u5c0d\u8b6f\u8a5e\u5f59 bookshelf \u66f8\u67b6\u3001\u66f8\u6ac3\u3001\u66f8\u6ae5 \u66f8\u67b6\u3001\u66f8\u6ac3\u3001\u66f8\u6ae5 \u5b8c\u5168\u76f8\u540c lay off \u4e0b\u5d17 \u89e3\u96c7 \u5b8c\u5168\uf967\u540c immediately \uf9f7\u5373 \uf9f7\u523b \u8a5e\u9996\u76f8\u540c according \u64da\u5831 \u6839\u64da \u90e8\u5206\u5b57\u5143\u76f8\u540c \u5c0d\u65bc CWN \u8207 CCD \u7684\u5c0d\u6bd4\uff0c\u7e3d\u5171\u6709 70744 \u500b Synset \u662f\u5c0d\u8b6f\u76f8\u540c\u7684\uff0c\u5206\u5c6c\u65bc\u5f62\u5bb9\u8a5e\u3001 \u526f\u8a5e\u3001\u540d\u8a5e\u548c\u52d5\u8a5e\u9019\u56db\u500b\u8a5e\uf9d0\u7576\u4e2d\uff0c\u5176\u4e2d\uff0c\u4ee5\u540d\u8a5e\u5728 CWN \u8207 CCD \u7684\u5b8c\u5168\u76f8\u540c\u5c0d\u8b6f\u4e2d\uff0c \u6240\u4f54\u6bd4\uf9b5\u6700\u9ad8\uff0c\u6709 66.79%\uff1b\u53cd\u4e4b\uff0c\u52d5\u8a5e\u6240\u4f54\u6bd4\uf9b5\u6700\u4f4e\uff0c\u50c5\u6709 4.05%\uff0c\u5176\u8a73\u7d30\u7684\u5206\u4f48\u60c5\u6cc1\uff0c \u5982\u4e0b\u5716\u986f\u793a\uff1a \u540c\u7fa9\u8a5e\u96c6 0 20 40 60 \u5f62\u5bb9\u8a5e 24.59% 17394 \u526f\u8a5e 4.57% 3231 \u540d\u8a5e 66.79% 47253 \u52d5\u8a5e 4.05% 2866 % \u540c\u7fa9\u8a5e\u96c6 \u5f9e\u8868 1 \u5230\u8868 3\uff0c\u6211\u5011\u53ef\u4ee5\u6e05\u695a\u77e5\u9053\u5c0d\u65bc\u5404\u8a5e\uf9d0\uff0cCCD \u548c CWN \u5728\u5c0d\u8b6f\uf967\u540c\u7684\u8a5e\u5f59\uf9e8\uff0c \u4ecd\u7136\u6709\u4e9b\u7b97\u662f\u8a9e\u7fa9\u76f8\u8fd1\u7684\u76f8\u95dc\u8a5e\u5f59\uff0c\u6263\u9664\u9019\u4e9b\u76f8\u95dc\u8a5e\u5f59\u5f8c\uff0c\uf978\u5cb8\u8a5e\u5f59\u5728\u4f7f\u7528\u4e0a\u7684\u771f\u6b63\uf967 \u540c\uff0c\u5c31\u53ef\u6e05\u695a\u5448\u73fe\u3002\u81f3\u65bc\uff0c\u4e0a\u6587\u4e2d\uff0c\u6240\u63d0\u53ca\u95dc\u65bc\u300c\u52d5\u8a5e\u300d\u662f\uf978\u5cb8\u8a5e\u5f59\u4e2d\uff0c\u4f7f\u7528\u6700\u591a\uf967\u540c \u7684\uf9fa\u6cc1\uff0c\u6211\u5011\u5f9e\u8868 3 \u7684\u5206\u6790\u5f97\u77e5\uff0c\u5728\u52d5\u8a5e\u7684\u4f7f\u7528\u4e0a\uff0c\u56e0\u70ba\u8f03\u5e38\u51fa\u73fe\u540c\uf9d0\u8fd1\u7fa9\u8a5e\u6216\u8a9e\u7fa9\u76f8 \u8fd1\u76f8\u95dc\u8a5e\uf92d\u53d6\u4ee3\u539f\u672c\u7684\u8a5e\u5f59\u7684\uf9fa\u6cc1\uff0c\u6240\u4ee5\u300c\u8a5e\u9996\u76f8\u540c\u300d\u548c\u300c\u90e8\u5206\u5b57\u5143\u76f8\u540c\u300d\u9019\uf978\uf9d0\u4f54\uf9ba Synset type \u5716 4. \uf978\u5cb8\u4f7f\u7528\u5b8c\u5168\u76f8\u540c\u8a5e\u5f59\u7684\u5206\u4f48\u60c5\u6cc1 \u5f9e\u5716 4 \uf92d\u770b\uff0c\u66f2\u7dda\u5f4e\u66f2\u7684\u524d\u5f8c\uf978\u7aef\uff0c\u4ee3\u8868\uf978\u8005\u7684\u5dee\u8ddd\u8f03\u5927\uff0c\u9760\u5de6\u908a\u7684\u5f4e\u66f2\u66f2\u7dda\u90e8\u4efd\uff0c \u662f\u53f0\u7063\u5448\u73fe\u5f37\u52e2\u8a5e\u5f59\u7684\u73fe\u8c61\uff0c\u9760\u53f3\u908a\u7684\u5f4e\u66f2\u66f2\u7dda\u90e8\u4efd\uff0c\u5247\u662f\u5927\uf9d3\u5448\u73fe\u5f37\u52e2\u8a5e\u5f59\u7684\u73fe\u8c61\u3002 \u5728\u4f7f\u7528\u5b8c\u5168\u76f8\u540c\u8a5e\u5f59\u4e2d\uff0c\u5728\u5206\u6790\uf969\u64da\u5448\u73fe\u4e0a\u4ecd\u6709\u4e9b\u4f7f\u7528\u5dee\uf962\u7684\u73fe\u8c61\uff0c\u9019\u662f\u503c\u5f97\u6211\u5011\u6df1\u5165 CNA (\u7e41\u9ad4\u4e2d\u6587) XIN (\u7c21\u9ad4\u4e2d\u6587) \u9152\u6876 32 (0.157\u03bc) 20 (0.155\u03bc) \u4f7f\u7528\uf9fa\u6cc1\u975e\u5e38\u63a5\u8fd1 \u7d72\u74dc 1380 (6.78\u03bc) 96 (0.748\u03bc) \u4f7f\u7528\uf9fa\u6cc1\u6709\u5dee\uf962 \uf9c9\uf96e\u5200 \u96d9\u5203\u5c0f\u5200 2 (0.00982\u03bc) 273 (2.13\u03bc) \u7c21\u9ad4\u4e2d\u6587\u7db2\u9801\uff1a\u7d04\u6709 136,670,000 \u9805\u7d50\u679c Synset type \u6c11\u773e\u5c0d\u65bc\u67d0\u4e9b\u8a5e\u5f59\u7684\u4f7f\u7528\uf9fa\u6cc1\uff0c\u800c\u7121\u6cd5\u771f\u6b63\u63d0\u4f9b\uf978\u5cb8\u8a5e\u5f59\u6216\u4e16\u754c\u83ef\u8a9e\u5c0d\u6bd4\u7684\u7814\u7a76\u3002 \u7e41\u9ad4\u4e2d\u6587\u7db2\u9801\uff1a\u7d04\u6709 4,330,000 \u9805\u7d50\u679c -0.1 \u6240\u6709\u4e2d\u6587\u7db2\u9801\uff1a\u7d04\u6709 141,000,000 \u9805\u7d50\u679c \u7528\u6cd5\u662f\u6108\uf92d\u6108\u591a\u3002\u9019\u4e5f\uf96f\u660e\uff0cGoogle \u6240\u641c\u5c0b\u5230\u7684\u8cc7\uf9be\uff0c\u50c5\u53ef\u4ee5\u7576\u4f5c\u76ee\u524d\u53f0\u7063\u8207\u5927\uf9d3\u4e00\u822c \u4f7f\u7528\uf9fa\u6cc1\u6709\u986f\u8457\u5dee\uf962 -0.05 0 1 8 15 22 29 36 43 50 57 64 71 78 85 \u5dee\u8ddd \u6b64\u5916\uff0c\uf978\u5cb8\u76ee\u524d\u4ea4\uf9ca\u8f03\u983b\u7e41\uff0c\u5e38\u6709\u4e92\u76f8\u5f15\u7528\uff0c\u7121\u6cd5\u6392\u9664\uff0c\u76ee\u524d\uff0c\u5373\u53ef\u770b\u51fa\u5927\uf9d3\u7528\u300c\u8b66\u5bdf\u300d (3) \u51fa\u79df\uf902 \u9801\u8a0a\u606f\u5f97\u77e5\uf978\u5cb8\u7db2\u9801\u7e3d\uf969\u5404\uf974\u5e72\uff0c\u6240\u4ee5\uff0c\u6309\u5e38\uf9e4\u63a8\u65b7\uff0c\u5927\uf9d3\u7684\u7db2\u9801\u61c9\u8a72\u6bd4\u53f0\u7063\u591a\u5f88\u591a\u3002 \u5716 8. \uf978\u5cb8\u4f7f\u7528\u5b8c\u5168\uf967\u540c\u7684\u52d5\u8a5e\u8a5e\u5f59\u7684\u5206\u4f48\u60c5\u6cc1 \u6cc1\uff0c\u4f46\u662f\uff0c\u7562\u7adf\u7db2\uf937\u4e0a\u7684\u8cc7\u6e90\u662f\u6bd4\u8f03\u591a\u5143\u5316\u3001\u4e5f\u6bd4\u8f03\u5177\u6709\u8907\u96dc\u6027\uff0c\u800c\u4e14\uff0c\u6211\u5011\u7121\u6cd5\u5f9e\u7db2 \uf978\u8005\u5dee\u8ddd Synset type \u7db2\u9801\u300d\u7684\u7b46\uf969\uff0c\uf9b5\u5982\uff1a \u85c9\u7531 Google \u641c\u5c0b\u7db2\u9801\u7684\u8cc7\uf9be\uff0c\u96d6\u7136\u53ef\u4ee5\u5448\u73fe\u51fa\u53f0\u7063\u3001\u5927\uf9d3\u7684\uf978\u5cb8\u8a5e\u5f59\u5c0d\u6bd4\u4f7f\u7528\uf9fa 80 100 \u4ee5 WordNet \u70ba\u57fa\u790e\uff0cCWN \u6240\u5c0d\u8b6f\u7684\u7e41\u9ad4\u4e2d\u6587\u8207 CCD \u6240\u5c0d\u8b6f\u7684\u7c21\u9ad4\u4e2d\u6587\uff0c\uf978\u8005\u4f7f\u7528 \u5b8c\u5168\uf967\u76f8\u540c\u7684\u60c5\u6cc1\uff0c\u4f9d\u5404\u8a5e\uf9d0\u7684\u5206\u4f48\u60c5\u5f62\uff0c\u5982\u4e0b\u5716\u986f\u793a\uff1a \u8868 2. CCD \u548c CWN \u5c0d\u8b6f\uf967\u540c\u7684\u5206\u4f48\uf9fa\u6cc1 \u5f62\u5bb9\u8a5e \u526f\u8a5e \u540d\u8a5e \u52d5\u8a5e \u7e3d\uf969 \u540c\u7fa9\u8a5e\u96c6\uf969\uf97e 17915 3575 66025 12127 99642 \uf967\u540c\u5c0d\u8b6f\uf969\uf97e 521 344 18772 9261 28898 2.91% 9.62% 28.43% 76.37% 29.99% \u6700\u5c11 \u6700\u591a \u503c\u5f97\u4e00\u63d0\u7684\u662f\uff0c\u5728 CCD \u548c CWN \u7ffb\u8b6f\uf967\u540c\u7684\u5206\u4f48\uf9fa\u6cc1\uf9e8\uff0c\u5f88\u6e05\u695a\u5f97\u770b\u5230\uff0c\u300c\u52d5\u8a5e\u300d \u5728\uf978\u5cb8\u7684\u4f7f\u7528\uf9fa\u6cc1\uff0c\u6709\u6975\u5927\u7684\u5dee\uf962\u6027\uff0c\uf967\u904e\uff0c\u7531\u65bc\u5728\u6211\u5011\u5be6\u969b\u4f7f\u7528\u6f22\u8a9e\u6642\uff0c\u5e38\u6703\u4ee5\u540c\uf9d0 \u5e0c\u671b\u5c07\u6bcf\u4e00\u500b\u8a5e\uf9d0\u4e2d\uff0c\u6709\u9019\u6a23\u7684\u4f7f\u7528\u60c5\u5f62\u5206\uf9d0\u51fa\uf92d\uff0c\u4ee5\u5f97\u5230\u771f\u6b63\uf978\u5cb8\u4f7f\u7528\uf967\u540c\u8a5e\u5f59\u7684\u73fe \u8c61\u3002 \u6211\u5011\u4ee5\u8a5e\u5f59\u7684\u300c\u8a5e\u9996\u76f8\u540c\u300d\u3001\u300c\u90e8\u4efd\u5b57\u5143\u76f8\u540c\u300d\u548c\u300c\u771f\u6b63\uf967\u540c\u300d\u9019\u4e09\u5927\uf9d0\u70ba\u4e3b\uff0c\u5206 \u6790 CCD \u548c CWN \u5728\u5f62\u5bb9\u8a5e\u3001\u526f\u8a5e\u3001\u540d\u8a5e\u548c\u52d5\u8a5e\u9019\u56db\u500b\u8a5e\uf9d0\u7576\u4e2d\uff0c\u7ffb\u8b6f\uf967\u540c\u7684\u5206\u4f48\uf9fa\u6cc1\uff0c \u5982\u4e0b\u8868\u986f\u793a\uff1a \u8868 3. 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