| { | |
| "paper_id": "O11-1010", | |
| "header": { | |
| "generated_with": "S2ORC 1.0.0", | |
| "date_generated": "2023-01-19T08:05:30.880149Z" | |
| }, | |
| "title": "Predicting the Semantic Orientation of Terms in E-HowNet", | |
| "authors": [ | |
| { | |
| "first": "Cheng-Ru", | |
| "middle": [], | |
| "last": "\u674e\u653f\u5112", | |
| "suffix": "", | |
| "affiliation": { | |
| "laboratory": "", | |
| "institution": "National Taiwan University", | |
| "location": { | |
| "addrLine": "#1, Sec.4, Roosevelt Road", | |
| "postCode": "10617", | |
| "settlement": "Taipei", | |
| "country": "Taiwan" | |
| } | |
| }, | |
| "email": "" | |
| }, | |
| { | |
| "first": "Chi-Hsin", | |
| "middle": [], | |
| "last": "Li", | |
| "suffix": "", | |
| "affiliation": { | |
| "laboratory": "", | |
| "institution": "National Taiwan University", | |
| "location": { | |
| "addrLine": "#1, Sec.4, Roosevelt Road", | |
| "postCode": "10617", | |
| "settlement": "Taipei", | |
| "country": "Taiwan" | |
| } | |
| }, | |
| "email": "" | |
| }, | |
| { | |
| "first": "Hsin-Hsi", | |
| "middle": [], | |
| "last": "Yu", | |
| "suffix": "", | |
| "affiliation": { | |
| "laboratory": "", | |
| "institution": "National Taiwan University", | |
| "location": { | |
| "addrLine": "#1, Sec.4, Roosevelt Road", | |
| "postCode": "10617", | |
| "settlement": "Taipei", | |
| "country": "Taiwan" | |
| } | |
| }, | |
| "email": "" | |
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| { | |
| "first": "\u570b\u7acb\u53f0\u7063\u5927\u5b78\u8cc7\u8a0a\u5de5\u7a0b\u7cfb", | |
| "middle": [], | |
| "last": "Chen", | |
| "suffix": "", | |
| "affiliation": { | |
| "laboratory": "", | |
| "institution": "National Taiwan University", | |
| "location": { | |
| "addrLine": "#1, Sec.4, Roosevelt Road", | |
| "postCode": "10617", | |
| "settlement": "Taipei", | |
| "country": "Taiwan" | |
| } | |
| }, | |
| "email": "[email protected]" | |
| } | |
| ], | |
| "year": "", | |
| "venue": null, | |
| "identifiers": {}, | |
| "abstract": "The semantic orientation of terms is fundamental for sentiment analysis in sentence and document levels. Although some Chinese sentiment dictionaries are available, how to predict the orientation of terms automatically is still important. In this paper, we predict the semantic orientation of terms of E-HowNet. We extract many useful features from different sources to represent a Chinese term in E-HowNet, and use a supervised machine learning algorithm to predict its orientation. Our experimental result showed that the proposed approach can achieve 92.33% accuracy, which is comparable to the accuracy of human taggers.", | |
| "pdf_parse": { | |
| "paper_id": "O11-1010", | |
| "_pdf_hash": "", | |
| "abstract": [ | |
| { | |
| "text": "The semantic orientation of terms is fundamental for sentiment analysis in sentence and document levels. Although some Chinese sentiment dictionaries are available, how to predict the orientation of terms automatically is still important. In this paper, we predict the semantic orientation of terms of E-HowNet. We extract many useful features from different sources to represent a Chinese term in E-HowNet, and use a supervised machine learning algorithm to predict its orientation. Our experimental result showed that the proposed approach can achieve 92.33% accuracy, which is comparable to the accuracy of human taggers.", | |
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| "section": "Abstract", | |
| "sec_num": null | |
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| "body_text": [ | |
| { | |
| "text": "EQUATION", | |
| "cite_spans": [], | |
| "ref_spans": [], | |
| "eq_spans": [ | |
| { | |
| "start": 0, | |
| "end": 8, | |
| "text": "EQUATION", | |
| "ref_id": "EQREF", | |
| "raw_str": "(\u4e00) \u3001\u57fa\u790e\u7fa9\u539f\u7279\u5fb5 \u5f9e E-HowNet \u62bd\u53d6\u7684\u7279\u5fb5\u7a31\u4e4b\u70ba\u57fa\u790e\u7fa9\u539f\u7279\u5fb5\uff0c\u4e5f\u5c31\u662f\u5c0d\u6bcf\u4e00\u500b E-HowNet \u7684\u8a5e\u5f59 i\uff0c \u7528\u4e00\u5411\u91cf V i = (w i,j ) = (w i,1 , w i,2 , \u2026, w i,n ) \u8868\u793a\uff0c\u5176\u4e2d n \u70ba\u5411\u91cf\u7684\u7dad\u5ea6\u3002 \u7531\u65bc\u6bcf\u4e00\u8a5e\u5f59\u7684\u6bcf\u4e00\u500b\u8a9e\u610f(sense)\u90fd\u6709\u4e00\u500b\u7d50\u69cb\u5316\u7684\u5b9a\u7fa9\u5f0f\uff0c\u800c\u4e14\u5b9a\u7fa9\u5f0f\u4e2d\u90fd\u7528\u7fa9 \u539f\u4f86\u9032\u884c\u5b9a\u7fa9\uff0c\u516c\u5f0f (1) \u5b9a\u7fa9 V i \u4e2d\u6bcf\u500b\u7279\u5fb5\u7684\u6b0a\u91cd\uff1a j \u7fa9\u539f , 0 j \u7fa9\u539f \u4e2d i \u5b9a\u7fa9\u5f0f , 1 , \u4e0d\u51fa\u73fe \u51fa\u73fe \u5982\u679c j i w", | |
| "eq_num": "(1)" | |
| } | |
| ], | |
| "section": "", | |
| "sec_num": null | |
| }, | |
| { | |
| "text": "j \u7fa9\u539f , 0 j \u7fa9\u539f \u4e2d i \u5b9a\u7fa9\u5f0f , 1 1 , , \u4e0d\u51fa\u73fe \u51fa\u73fe \u5982\u679c j i j i d w (2) \u516c\u5f0f (2) \u4e2d\uff0c \u662f\u53ef\u8abf\u7684\u53c3\u6578\uff0c j i d , \u662f\u8a5e\u5f59 i \u8ddf\u7fa9\u539f j \u7684\u8ddd\u96e2\uff0c\u9019\u53ef\u7528\u7fa9\u539f j \u7684\u6df1\u5ea6\u8868 \u793a\u3002\u8abf\u6574\u516c\u5f0f (2) \u4e2d\u7684 \uff0c\u8b93\u6211\u5011\u53ef\u4ee5\u5be6\u9a57\u90a3\u4e00\u7a2e\u65b9\u5f0f\uff0c\u624d\u61c9\u7d66\u8f03\u9ad8\u7684\u6b0a\u91cd\uff1a (\u53ef\u80fd\u4e00) < 0 : \u6df1\u5ea6\u8d8a\u6df1\uff0c\u8868\u793a\u8a72\u7fa9\u539f\u6709\u8f03\u591a\u8cc7\u8a0a\uff0c\u61c9\u7d66\u8f03\u9ad8\u6b0a\u91cd\u3002 (\u53ef\u80fd\u4e8c) > 0 : \u6df1\u5ea6\u8d8a\u6df1\uff0c\u8868\u793a\u8a72\u7fa9\u539f\u6709\u8f03\u5c11\u8cc7\u8a0a\uff0c\u61c9\u7d66\u8f03\u5c11\u6b0a\u91cd\u3002 \u7531\u65bc < 0 \u6642\uff0cw i,j \u53ef\u80fd\u8b8a\u70ba\u8ca0\u503c\uff0c\u6240\u4ee5\u6700\u5c0f\u7684 \u8a2d\u70ba \u22120.05\u3002\u53e6\u5916\uff0c\u7576 \u03b1 = 0\uff0c\u516c\u5f0f (2) \u6703\u7b49\u65bc\u516c\u5f0f (1)\uff0c\u6240\u4ee5\u6211\u5011\u5728\u505a\u5be6\u9a57\u6642\uff0c\u53ea\u8981\u4f7f\u7528\u516c\u5f0f (2) \u5373\u53ef\u3002 2\u3001\u52a0\u5165\u5426\u5b9a\u95dc\u4fc2\u8abf\u6574\u7279\u5fb5\u7684\u52a0\u6b0a\u503c \u5728\u8a08\u7b97\u7fa9\u539f\u6df1\u5ea6\u6642\uff0c\u53ef\u80fd\u6703\u7d93\u904e\u5e36\u6709\u5426\u5b9a\u610f\u7fa9\u7684\u95dc\u4fc2\uff0c\u4f8b\u5982\u300c\u4e00\u4e8b\u7121\u6210\u300d\u5b9a\u7fa9\u5f0f\u4e2d\u6709 \u300c{not({succeed|\u6210\u529f})}\u300d \uff0c\u53ef\u4ee5\u767c\u73fe succeed \u88ab not \u6240\u4fee\u98fe\u3002\u9019\u6642\uff0c\u7fa9\u539f succeed \u7684\u6b0a\u91cd \u7528\u8ca0\u503c\u4f86\u8868\u793a\u53ef\u80fd\u6703\u66f4\u597d\uff0c\u56e0\u6b64\u6211\u5011\u5c07\u5426\u5b9a\u7684\u6982\u5ff5\u5f15\u5165\u516c\u5f0f (3) \u5982\u4e0b\uff1a j \u7fa9\u539f , 0 j \u7fa9\u539f \u4e2d i \u5b9a\u7fa9\u5f0f , 1 , , , \u4e0d\u51fa\u73fe \u51fa\u73fe \u5982\u679c j i j i j i d Neg w (3) \u5176\u4e2d\uff0c j i Neg , \u8868\u793a\u7fa9\u539f j \u662f\u5426\u6709\u88ab\u5426\u5b9a\u610f\u7fa9\u7684\u95dc\u4fc2\u6240\u4fee\u98fe\uff0c\u82e5\u6709\u5247 j i Neg , \u70ba 1\uff0c\u82e5\u7121 \u5247 j i Neg , \u70ba", | |
| "cite_spans": [], | |
| "ref_spans": [], | |
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| "sec_num": null | |
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| { | |
| "text": "V i = (c i,j ) = (c i,1 , c i,2 ,\u2026, c i,m )\u3002\u5176\u4e2d\uff0cm \u662f\u7279\u5fb5\u96c6\u5408\u7684\u5927 \u5c0f\uff0cc i,j \u662f\u300c\u8a5e\u5f59", | |
| "cite_spans": [], | |
| "ref_spans": [], | |
| "eq_spans": [], | |
| "section": "", | |
| "sec_num": null | |
| }, | |
| { | |
| "text": "EQUATION", | |
| "cite_spans": [], | |
| "ref_spans": [], | |
| "eq_spans": [ | |
| { | |
| "start": 0, | |
| "end": 8, | |
| "text": "EQUATION", | |
| "ref_id": "EQREF", | |
| "raw_str": "\u7528 V i = (c i,1 , c i,2 ,\u2026, c i,m ) \u7684\u65b9\u5f0f\u4f86\u8868\u793a\u7684\u7f3a\u9ede\uff0c\u662f c i,j \u7684\u503c\u8b8a\u5316\u7684\u7bc4\u570d\u6703\u975e\u5e38\u5927\uff0c\u6700\u5c0f\u70ba 40\uff0c\u6700\u5927\u6703\u5230\u4e0a\u5343\u842c\u3002\u9019\u5728\u6a5f\u5668\u5b78\u7fd2\u4e2d\uff0c\u901a\u5e38\u9700\u8981\u505a\u9032\u4e00\u6b65\u7684\u8655\u7406\u624d\u6703\u6709\u6bd4\u8f03\u597d\u7684\u7d50\u679c\u3002 \u6211 \u5011 \u5be6 \u9a57 \u4e86 \u5169 \u500b \u4e0d \u540c \u7684 \u65b9 \u6cd5 \u4f86 \u8655 \u7406 \u9019 \u4e00 \u554f \u984c \uff1a \u7b2c \u4e00 \u7a2e \u662f \u4e00 \u822c \u7684 \u9918 \u5f26 \u6a19 \u6e96 \u5316 (cosine-normalization) \uff0c\u5c07\u539f\u672c\u7684\u5411\u91cf V i \u7528\u516c\u5f0f (4) \u8655\u7406\uff1b\u7b2c\u4e8c\u7a2e\u662f Esuli & Sebastiani[1] \u6240\u63d0\u7684\u9918\u5f26\u6a19\u6e96\u5316 TFIDF (cosine-normalized TF-IDF) \uff0c\u4ed6\u5011\u7528\u8a72\u65b9\u6cd5\u4f86\u8655\u7406 WordNet \u4e2d\u7684\u8a5e\u5f59\u7684\u6b0a\u91cd\uff0c\u5982\u516c\u5f0f (5) \u6240\u8ff0\u3002 m m k k i i i c V V CosNorm 1 2 , ) ( (4) m m k k i i i tfidf TFIDF TFIDF CosNorm 1 2 , ) ( ) ,..., , ( , 2 , 1 , m i i i i tfidf tfidf tfidf TFIDF j j i j i idf tf tfidf * , ,", | |
| "eq_num": "(5)" | |
| } | |
| ], | |
| "section": "", | |
| "sec_num": null | |
| }, | |
| { | |
| "text": "D k j k j i j i j i c c j c tf , , , , \u7e3d\u51fa\u73fe\u6b21\u6578 \u7279\u5fb5 } , 0 : { log ) log( , 1 D i c i D df idf j i j j \u516c\u5f0f (5)\u4e2d D \u8868\u793a\u6587\u4ef6\u7684\u96c6\u5408\uff0c\u6b64\u8655\u628a\u8a5e\u5f59 i \u7576\u6210\u6587\u4ef6\uff0c\u7279\u5fb5 j \u7576\u6210 term\u3002 \u516c\u5f0f (4) \u7684\u6a19\u6e96\u5316\u53ef\u4ee5\u8b93\u6240\u6709\u8a5e\u5f59\u7684\u5411\u91cf\u7b49\u9577\uff0c\u6d88\u6389\u6b21\u6578\u8b8a\u5316\u904e\u5927\u7684\u7f3a\u9ede\u3002\u516c\u5f0f (5) \u7684 \u60f3\u6cd5\u5247\u8a8d\u70ba\u7279\u5fb5 j \u7684\u6b0a\u91cd\uff0c\u61c9\u8a72\u5148\u8de8\u8a5e\u5f59\u9032\u884c\u6a19\u6e96\u5316(normalization) \uff0c\u6240\u4ee5 tf i , j \u6703\u9664 \u4ee5\u7279\u5fb5 j \u7684\u7e3d\u51fa\u73fe\u6b21\u6578\uff0c\u53e6\u5916\u518d\u8003\u616e\u7279\u5fb5 j \u7684\u7a00\u6709\u5ea6\uff0c\u6240\u4ee5\u4e58\u4e0a idf j \uff0c\u6700\u5f8c\u518d\u8b93\u6240\u6709 \u8a5e\u5f59\u7684\u5411\u91cf\u7b49\u9577\u3002\u6211\u5011\u6703\u5728\u5f8c\u9762\u7684\u5be6\u9a57\u4e2d\uff0c\u6bd4\u8f03\u9019\u5169\u7a2e\u4e0d\u540c\u6b0a\u91cd\u8655\u7406\u65b9\u5f0f\u7684\u6548\u80fd\u3002 (\u4e09) \u3001\u4e0d\u540c\u7279\u5fb5\u7684\u7d44\u5408 \u6211\u5011\u7528\u4e86\u57fa\u790e\u7fa9\u539f\u7279\u5fb5 (w i,1 , w i,2 ,\u2026, w i,n ) = (w i,j ) \uff0c\u53ca\u8a9e\u7bc7\u7279\u5fb5 (c i,1 , c i,2 ,\u2026, c i,m ) = (c i,j ) \u4f86 \u8868\u793a\u8a5e\u5f59 i\u3002\u5982\u679c\u60f3\u540c\u6642\u4f7f\u7528\u9019\u5169\u7a2e\u7279\u5fb5\u4e2d\u7684\u8cc7\u8a0a\uff0c\u4e00\u7a2e\u76f4\u89c0\u7684\u65b9\u5f0f\uff0c\u662f\u5c07\u5169\u7a2e\u7279\u5fb5\u8868 \u793a\u65b9\u5f0f\u6df7\u5408\uff0c\u7528 V i = (w i,1 , w i,2 ,\u2026, w i,n ,c i,1 , c i,2 ,\u2026, c i,m ) \u4f86\u8868\u793a\u3002\u7531\u65bc\u57fa\u790e\u7fa9\u539f\u7279\u5fb5\u53ca\u8a9e\u7bc7 \u7279\u5fb5\u90fd\u6709\u8a31\u591a\u4e0d\u540c\u7684\u8b8a\u5f62\uff0c\u6211\u5011\u7121\u6cd5\u4e00\u4e00\u5617\u8a74\u6240\u6709\u53ef\u80fd\u7684\u7d44\u5408\uff0c\u6240\u4ee5\u6703\u5148\u5206\u5225\u7528\u5be6\u9a57\u627e \u51fa\u6700\u597d\u7684\u57fa\u790e\u7fa9\u539f\u7279\u5fb5 (w i,j ) \u53ca\u8a9e\u7bc7\u7279\u5fb5 (c i,j )\uff0c\u518d\u628a\u5169\u7a2e\u7279\u5fb5\u6df7\u5408\u4f86\u9032\u884c\u5be6\u9a57\u3002\u6211\u5011\u6c92 \u6709\u5c0d\u6df7\u5408\u5f8c\u7684\u5411\u91cf\u505a\u5176\u5b83\u7684\u8655\u7406\uff0c\u53ea\u662f\u76f4\u63a5\u4e32\u63a5\u6210\u70ba n+m \u7dad\u5411\u91cf\u3002 (\u56db) \u3001\u7d44\u5408\u5f0f\u7684\u76e3\u7763\u5f0f\u6a5f\u5668\u5b78\u7fd2\u6f14\u7b97\u6cd5(", | |
| "cite_spans": [], | |
| "ref_spans": [], | |
| "eq_spans": [], | |
| "section": "", | |
| "sec_num": null | |
| } | |
| ], | |
| "back_matter": [], | |
| "bib_entries": { | |
| "BIBREF0": { | |
| "ref_id": "b0", | |
| "title": "Determining the semantic orientation of terms through gloss classification", | |
| "authors": [ | |
| { | |
| "first": "A", | |
| "middle": [], | |
| "last": "Esuli", | |
| "suffix": "" | |
| }, | |
| { | |
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| "pages": "617--624", | |
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| "num": null, | |
| "urls": [], | |
| "raw_text": "A. Esuli and F. Sebastiani, \"Determining the semantic orientation of terms through gloss classification,\" In Proceedings of CIKM-05, pp. 617-624, 2005..", | |
| "links": null | |
| }, | |
| "BIBREF1": { | |
| "ref_id": "b1", | |
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| "year": 2007, | |
| "venue": "Journal of the American Society for Information Science and Technology", | |
| "volume": "58", | |
| "issue": "12", | |
| "pages": "1838--1850", | |
| "other_ids": {}, | |
| "num": null, | |
| "urls": [], | |
| "raw_text": "L.-W. Ku and H.-H. Chen, \"Mining opinions from the Web: Beyond relevance retrieval,\" Journal of the American Society for Information Science and Technology, vol. 58, no. 12, pp. 1838-1850, 2007.", | |
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| }, | |
| "BIBREF2": { | |
| "ref_id": "b2", | |
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| "authors": [ | |
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| "last": "Esuli", | |
| "suffix": "" | |
| }, | |
| { | |
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| "suffix": "" | |
| } | |
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| "year": 2006, | |
| "venue": "Proceedings of the 5th Conference on Language Resources and Evaluation (LREC 06)", | |
| "volume": "", | |
| "issue": "", | |
| "pages": "417--422", | |
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| "urls": [], | |
| "raw_text": "A. Esuli and F. Sebastiani, \"SentiWordNet: A publicly available lexical resource for opinion mining,\" In Proceedings of the 5th Conference on Language Resources and Evaluation (LREC 06), pp. 417-422, 2006.", | |
| "links": null | |
| }, | |
| "BIBREF3": { | |
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| }, | |
| { | |
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| "last": "Dong", | |
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| ], | |
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| "volume": "", | |
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| "num": null, | |
| "urls": [], | |
| "raw_text": "Z. Dong and Q. Dong, HowNet and the Computation of Meaning. World Scientific, 2006.", | |
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| "TABREF0": { | |
| "text": "Google Chinese Web 5-gram \u62bd\u53d6\u7279\u5fb5\u7684\u65b9\u6cd5\uff0c\u7b2c\u56db\u7bc0\u5448\u73fe\u5404\u7a2e\u5be6\u9a57\u7684\u7d50\u679c\u53ca\u5206\u6790\uff0c \u5305\u62ec\u8ddf NTUSD \u4eba\u5de5\u6a19\u8a18\u7684\u6bd4\u8f03\uff0c\u6700\u5f8c\u7e3d\u7d50\u8ad6\u6587\u7684\u6210\u679c\u3002 \u4e8c\u3001\u76f8\u95dc\u7814\u7a76 \u8463\u632f\u6771\u5148\u751f\u65bc 1998 \u5e74\u5275\u5efa\u77e5\u7db2(HowNet) \uff0c\u4e26\u5728 2003 \u5e74\uff0c\u8ddf\u4e2d\u592e\u7814\u7a76\u9662\u8cc7\u8a0a\u6240\u8a5e\u5eab\u5c0f \u7d44\u5728 2003 \u5e74\uff0c\u5c07\u4e2d\u7814\u9662\u8a5e\u5eab\u5c0f\u7d44\u8a5e\u5178(CKIP Chinese Lexical Knowledge Base)\u7684\u8a5e\u689d", | |
| "type_str": "table", | |
| "content": "<table><tr><td>\u4e00\u3001\u7dd2\u8ad6 \u6e96\u78ba\u7387(Accuracy)\u7531 82% \u5230 90%\u3002\u4e4b\u5f8c\u9678\u7e8c\u6709\u4e0d\u540c\u7684\u7814\u7a76\uff0c\u6240\u7528\u591a\u70ba\u534a\u76e3\u7763\u5f0f\u6a5f\u5668</td></tr><tr><td>\u5b78\u7fd2\u7684\u6f14\u7b97\u6cd5[7-9]\uff0c\u6548\u80fd\u5f9e 67%\u5230 88%\u4e0d\u7b49\uff0c\u4f46\u56e0\u70ba\u9019\u4e9b\u6f14\u7b97\u6cd5\u6240\u7528\u7684\u8cc7\u6599\u96c6\u4e26\u4e0d\u76f8</td></tr><tr><td>\u540c\uff0c\u5be6\u9a57\u904e\u7a0b\u53ca\u8a55\u4f30\u6a19\u6e96\u4e5f\u4e0d\u4e00\u6a23\uff0c (\u6709\u7528 Accuracy\u3001Precision\u3001\u6216 F-Measure) \uff0c\u6240\u4ee5 \u60c5\u7dd2\u5206\u6790(Sentiment Analysis)\u5728\u73fe\u4eca\u7684\u7db2\u8def\u4e16\u754c\u4e2d\uff0c\u6709\u8a31\u591a\u5be6\u969b\u4e14\u91cd\u8981\u7684\u904b\u7528\uff0c\u4f8b\u5982 \u5f9e\u7db2\u8def\u7684\u8a55\u8ad6\u6587\u7ae0\u4e2d\u5206\u6790\u6d88\u8cbb\u8005\u5c0d\u7522\u54c1\u7684\u8a55\u50f9\uff0c\u6216\u5206\u6790\u6d88\u8cbb\u8005\u5c0d\u7522\u54c1\u6027\u80fd\u7684\u95dc\u6ce8\u7126\u9ede\u7b49 \u6548\u80fd\u6c92\u6709\u8fa6\u6cd5\u76f4\u63a5\u6bd4\u8f03\u3002</td></tr><tr><td>\u7b49\u3002\u4e0d\u7ba1\u5c0d\u53e5\u5b50\u6216\u6587\u4ef6\u5c64\u6b21\u7684\u60c5\u7dd2\u5206\u6790\uff0c\u610f\u898b\u8a5e\u8a5e\u5178\u90fd\u662f\u4e00\u500b\u91cd\u8981\u7684\u8cc7\u6e90\u3002\u901a\u5e38\u610f\u898b\u8a5e</td></tr><tr><td>\u8a5e\u5178\u662f\u7528\u4eba\u5de5\u4f86\u6536\u96c6\u8a5e\u5f59\uff0c\u4e26\u7528\u4eba\u5de5\u6a19\u8a18\u8a5e\u5f59\u7684\u5404\u7a2e\u60c5\u7dd2\u5c6c\u6027\uff0c\u5305\u62ec\u4e3b\u5ba2\u89c0(subjective</td></tr><tr><td>or objective) \u3001\u6975\u6027(orientation/polarity)\u3001)\u53ca\u6975\u6027\u7684\u5f37\u5ea6(strength)[1]\u3002\u9019\u4e9b\u60c5\u7dd2\u5c6c</td></tr><tr><td>\u6027\u5c0d\u4e0d\u540c\u7684\u61c9\u7528\u6709\u4e0d\u540c\u7684\u91cd\u8981\u6027\uff0c\u6a19\u8a18\u96e3\u5ea6\u4e5f\u5404\u4e0d\u76f8\u540c\uff0c\u901a\u5e38\u8a5e\u5f59\u7684\u6975\u6027\u662f\u6700\u5bb9\u6613\u9032\u884c</td></tr><tr><td>\u6a19\u8a18\u7684\u5c6c\u6027\u3002</td></tr><tr><td>\u6a19\u8a18\u60c5\u7dd2\u5c6c\u6027\u6642\uff0c\u7814\u7a76\u8005\u53ef\u4ee5\u5f9e\u96f6\u958b\u59cb\u6536\u96c6\u8a5e\u5f59\u4ee5\u5efa\u7acb\u610f\u898b\u8a5e\u8a5e\u5178\uff0c\u5982\u53f0\u5927\u610f\u898b\u8a5e\u8a5e\u5178</td></tr><tr><td>NTUSD[2]\u3002\u5728\u53e6\u4e00\u65b9\u9762\uff0c\u4e5f\u6709\u7814\u7a76\u8005\u5617\u8a74\u70ba\u81ea\u7136\u8a9e\u8a00\u8655\u7406\u4e2d\u7684\u8a31\u591a\u73fe\u5b58\u7684\u8cc7\u6e90\uff0c\u6dfb\u52a0</td></tr><tr><td>\u60c5\u7dd2\u5c6c\u6027\uff0c\u5982 SentiWordNet[3]\u3002\u4f46\u73fe\u6709\u8cc7\u6e90\u7684\u8a9e\u5f59\u91cf\u901a\u5e38\u5f88\u5927\uff0c\u5982 WordNet 3.0 \u5c31\u5305</td></tr><tr><td>\u62ec 206,941 \u500b\u4e0d\u540c\u7684\u82f1\u6587\u5b57\u7fa9(word-sense pair) \uff0c\u8981\u5168\u90e8\u7528\u4eba\u5de5\u9032\u884c\u6a19\u8a18\u4e4b\u6210\u672c\u592a\u9ad8\u3002 \u5716\u4e00\u3001 \u300c\u6c7d\u6cb9\u300d\u7684\u5ee3\u7fa9\u77e5\u7db2\u5b9a\u7fa9\u5f0f</td></tr><tr><td>\u56e0\u6b64\uff0c\u901a\u5e38\u7684\u4f5c\u6cd5\u662f\u5c11\u91cf\u6a19\u8a18\u4e00\u4e9b\u8a5e\u5f59\uff0c\u518d\u7528\u6a5f\u5668\u5b78\u7fd2\u65b9\u6cd5\uff0c\u70ba\u5269\u4e0b\u7684\u8a5e\u5f59\u9032\u884c\u81ea\u52d5\u6a19</td></tr><tr><td>\u8a18\uff0c\u96d6\u7136\u81ea\u52d5\u6a19\u8a18\u7684\u6e96\u78ba\u7387\u4e0d\u5982\u4eba\u5de5\u6a19\u8a18\uff0c\u4f46\u5c0d\u4e00\u822c\u61c9\u7528\u6709\u67d0\u7a2e\u7a0b\u5ea6\u7684\u5e6b\u52a9\u3002</td></tr><tr><td>\u5728\u4e2d\u6587\u7684\u60c5\u7dd2\u5c6c\u6027\u6a19\u8a18\u76f8\u95dc\u7814\u7a76\uff0cYuen et al.[10]2004 \u5e74\u5229\u7528 Turney & Littman[7] \u7684\u534a \u5728\u4e2d\u6587\u81ea\u7136\u8a9e\u8a00\u8655\u7406\uff0cNTUSD \u662f\u4e00\u90e8\u91cd\u8981\u7684\u610f\u898b\u8a5e\u8a5e\u5178\uff0c\u4f46\u6b64\u8a5e\u5178\u53ea\u5305\u62ec\u8a5e\u5f59\u53ca\u6975\u6027 \u76e3\u7763\u5f0f\u6a5f\u5668\u5b78\u7fd2\u6f14\u7b97\u6cd5\uff0c\u5728\u6b63\u9762\u8a5e 604 \u500b\u53ca\u8ca0\u9762\u8a5e 645 \u500b\u7684\u8cc7\u6599\u96c6\u4e0a\u505a\u5be6\u9a57\uff0c\u5f97\u5230\u6700\u9ad8 \u7684\u8cc7\u8a0a\u3002\u53e6\u4e00\u65b9\u9762\uff0c\u8463\u632f\u6771\u5148\u751f\u548c\u9673\u514b\u5065\u6559\u6388\u6240\u5efa\u7acb\u7684\u77e5\u7db2[4]\u548c\u5ee3\u7fa9\u77e5\u7db2[5]\uff0c\u662f\u91cd\u8981 \u7684\u6210\u7e3e\u662f 80.23%\u7684\u7cbe\u78ba\u5ea6\u53ca 85.03%\u7684\u53ec\u56de\u7387\u3002\u4e4b\u5f8c\u5f9e 2006 \u5230 2010 \u5e74\uff0c\u9678\u7e8c\u7684\u7814\u7a76\u4f7f \u7684\u8a9e\u610f\u8cc7\u6e90\u3002\u5c0d\u65bc\u6bcf\u500b\u8a5e\u5f59\uff0c\u90fd\u7528\u6709\u9650\u7684\u7fa9\u539f\u7d66\u4e88\u7cbe\u78ba\u7684\u5b9a\u7fa9\uff0c\u4f46\u9019\u4e9b\u5b9a\u7fa9\u537b\u7f3a\u4e4f\u60c5\u7dd2 \u7528\u4e0d\u540c\u7684\u8cc7\u6599\u96c6\uff0c\u7528\u4e0d\u540c\u985e\u578b\u7684\u6a5f\u5668\u5b78\u7fd2\u6f14\u7b97\u6cd5\u4f86\u8655\u7406\u9019\u500b\u554f\u984c[11-14]\uff0c\u6240\u5f97\u5230\u7684\u6548\u80fd \u7684\u8a9e\u610f\u6a19\u8a18\u3002\u56e0\u6b64\uff0c\u5982\u4f55\u81ea\u52d5\u70ba\u5ee3\u7fa9\u77e5\u7db2\u52a0\u4e0a\u60c5\u7dd2\u6a19\u8a18\uff0c\u6210\u70ba\u4e00\u500b\u91cd\u8981\u7684\u8ab2\u984c\uff0c\u4e5f\u662f\u672c \u5728\u4e0d\u540c\u7684\u6307\u6a19(Accuracy\u3001Precision\u3001\u6216 F-Measure)\u4e0b\uff0c\u5f9e 89%\u5230 96%\u4e0d\u7b49\u3002\u56e0\u70ba\u57fa \u7814\u7a76\u7684\u76ee\u7684\u3002 \u6e96\u4e0d\u540c\uff0c\u9019\u4e9b\u6548\u80fd\u4e00\u6a23\u6c92\u6709\u8fa6\u6cd5\u76f4\u63a5\u6bd4\u8f03\uff0c\u4f46\u76f8\u8f03\u65bc\u82f1\u6587\uff0c\u6210\u7e3e\u5247\u660e\u986f\u63d0\u9ad8\u3002</td></tr><tr><td>\u672c\u7814\u7a76\u63d0\u51fa\u70ba\u5ee3\u7fa9\u77e5\u7db2\u52a0\u4e0a\u60c5\u7dd2\u6a19\u8a18\u7684\u65b9\u6cd5\uff0c\u9996\u5148\u5229\u7528 NTUSD \u8ddf\u5ee3\u7fa9\u77e5\u7db2\u8a5e\u5f59\u7684\u4ea4\u96c6</td></tr><tr><td>\u5efa\u7acb\u6a19\u6e96\u7b54\u6848\u96c6\uff0c\u518d\u7531\u6a19\u6e96\u7b54\u6848\u96c6\u8a13\u7df4\u51fa\u5206\u985e\u5668\uff0c\u70ba\u5176\u4ed6\u5ee3\u7fa9\u77e5\u7db2\u8a5e\u5f59\u9032\u884c\u6a19\u8a18\u3002\u5982\u4f55</td></tr><tr><td>\u6709\u6548\u7684\u904b\u7528\u76e3\u7763\u5f0f\u6a5f\u5668\u5b78\u7fd2\u6f14\u7b97\u6cd5\uff0c\u5982\u4f55\u70ba\u8a5e\u5f59\u62bd\u53d6\u51fa\u6709\u7528\u7684\u7279\u5fb5\uff0c\u662f\u4e3b\u8981\u7684\u6311\u6230\u8b70 \u4e09\u3001\u7279\u5fb5\u62bd\u53d6\u53ca\u6a5f\u5668\u5b78\u7fd2\u6f14\u7b97\u6cd5</td></tr><tr><td>\u984c\u3002\u5728\u6b64\u7814\u7a76\u4e2d\uff0c\u6211\u5011\u6709\u7cfb\u7d71\u7684\u5617\u8a74\u62bd\u53d6\u5404\u7a2e\u4e0d\u540c\u7684\u8a5e\u5f59\u7279\u5fb5\uff0c\u6700\u5f8c\u5f97\u5230\u8ddf\u4eba\u5de5\u6a19\u8a18\u6e96</td></tr><tr><td>\u78ba\u7387\u4e0d\u76f8\u4e0a\u4e0b\u7684\u5206\u985e\u5668\u3002 \u7531\u65bc\u6211\u5011\u904b\u7528\u76e3\u7763\u5f0f\u6a5f\u5668\u5b78\u7fd2\u6f14\u7b97\u6cd5\u4f86\u8a13\u7df4\u5206\u985e\u5668\uff0c\u6700\u91cd\u8981\u7684\u554f\u984c\u662f\u70ba\u8a5e\u5f59\u62bd\u53d6\u51fa\u6709\u7528</td></tr><tr><td>\u7b2c\u4e8c\u7bc0\u4ecb\u7d39\u5ee3\u7fa9\u77e5\u7db2\u3001\u53ca\u82f1\u6587\u548c\u4e2d\u6587\u76f8\u95dc\u7684\u60c5\u7dd2\u5c6c\u6027\u6a19\u8a18\u7814\u7a76\uff0c\u7b2c\u4e09\u7bc0\u4ecb\u7d39\u5f9e E-HowNet \u7684\u7279\u5fb5\u3002\u5728\u6b64\u8ad6\u6587\u4e2d\uff0c\u6211\u5011\u5206\u5225\u5f9e E-HowNet \u53ca Google Chinese Web 5-gram \u9019\u5169\u500b\u4f86</td></tr><tr><td>\u6e90\u62bd\u53d6\u5169\u5927\u985e\u7684\u7279\u5fb5\uff0c\u63a5\u8457\u5c07\u9019\u5169\u500b\u4f86\u6e90\u7684\u7279\u5fb5\u7d44\u5408\u8a13\u7df4\u5206\u985e\u5668\u3002\u6b64\u5916\uff0c\u6211\u5011\u4e5f\u5617\u8a74\u4f7f \u7528\u7d44\u5408\u5f0f\u7684\u76e3\u7763\u5f0f\u6a5f\u5668\u5b78\u7fd2\u6f14\u7b97\u6cd5(ensemble approach) \uff0c\u4f86\u66f4\u9032\u4e00\u6b65\u5f97\u5230\u66f4\u9ad8\u7684\u6548\u80fd\uff0c \u53ca \u8ddf\u77e5\u7db2\u9023\u7d50\uff0c\u4e26\u4f5c\u4e86\u4e00\u4e9b\u4fee\u6539\uff0c\u6700\u5f8c\u5f62\u6210\u5ee3\u7fa9\u77e5\u7db2(Extended-HowNet, E-HowNet) \u3002\u8a5e \u4ee5\u4e0b\u6211\u5011\u5206\u5225\u8a73\u7d30\u4ecb\u7d39\u3002</td></tr><tr><td>\u5eab\u5c0f\u7d44\u4fee\u6539\u4e26\u64f4\u5c55\u77e5\u7db2\u539f\u5148\u7684\u8a9e\u7fa9\u7fa9\u539f\u89d2\u8272\u77e5\u8b58\u672c\u9ad4\uff0c\u5efa\u69cb\u51fa\u5ee3\u7fa9\u77e5\u7db2\u77e5\u8b58\u672c\u9ad4</td></tr><tr><td>(Extended-HowNet Ontology) \uff0c\u4e26\u7528\u9019\u4e9b\u65b0\u7684\u8a9e\u7fa9\u7fa9\u539f\uff0c\u4ee5\u7d50\u69cb\u5316\u7684\u5f62\u5f0f\u4f86\u5b9a\u7fa9\u8a5e\u689d\uff0c</td></tr><tr><td>\u8a5e\u689d\u5b9a\u7fa9\u5f0f\u7684\u4f8b\u5b50\u5982\u5716\u4e00\u3002</td></tr><tr><td>\u6709\u95dc\u60c5\u7dd2\u5c6c\u6027\u6a19\u8a18\u7684\u7814\u7a76\uff0c\u6211\u5011\u5206\u70ba\u82f1\u6587\u53ca\u4e2d\u6587\u4f86\u8a0e\u8ad6\u3002\u5728\u82f1\u6587\u65b9\u9762\uff0c\u6700\u65e9\u662f\u7531</td></tr><tr><td>Hatzivassiloglou & McKeown[6]\u5728 1997 \u5e74\u91dd\u5c0d\u5f62\u5bb9\u8a5e\u6240\u505a\u7684\u7814\u7a76\uff0c\u4ed6\u5011\u6240\u7528\u7684\u5f62\u5bb9\u8a5e</td></tr><tr><td>\u5206\u5225\u6709\u6b63\u9762\u8a5e 657 \u500b\u53ca\u8ca0\u9762\u8a5e 679 \u500b\uff0c\u8a72\u8ad6\u6587\u4f9d\u64da\u4e0d\u540c\u7684\u5be6\u9a57\u8a2d\u5b9a\uff0c\u76e3\u7763\u5f0f\u6a5f\u5668\u5b78\u7fd2\u7684</td></tr></table>", | |
| "num": null, | |
| "html": null | |
| }, | |
| "TABREF1": { | |
| "text": "\u4ee5\u5716\u4e00 \u300c\u6c7d\u6cb9\u300d \u9019\u500b\u8a5e\u5f59\u70ba\u4f8b\uff0c\u5176\u5b9a\u7fa9\u5f0f\u4e2d\u51fa\u73fe\u4e86\u7fa9\u539f material\uff0c\u6240\u4ee5\u5b83\u7684\u503c w \u6c7d\u6cb9 , material", | |
| "type_str": "table", | |
| "content": "<table><tr><td>\u5c31\u6703\u662f 1\uff0c\u5176\u4ed6\u6c92\u51fa\u73fe\u7684\u7fa9\u539f\uff0c\u503c\u5c31\u6703\u662f 0\u3002\u6211\u5011\u5171\u4f7f\u7528\u4e86 2567 \u500b\u7fa9\u539f\u4f86\u7576\u7279\u5fb5\u3002</td></tr><tr><td>\u5ee3\u7fa9\u77e5\u7db2\u7684\u8a5e\u5f59\u6709\u6b67\u7570\u6027\uff0c\u4e5f\u5c31\u662f\u6bcf\u500b\u8a5e\u5f59\u53ef\u80fd\u6709\u8a31\u591a\u8a9e\u610f\u3002\u800c\u8a5e\u5f59\u7684\u7b2c\u4e00\u500b\u8a9e\u610f\uff0c\u662f</td></tr><tr><td>\u51fa\u73fe\u983b\u7387\u6700\u9ad8\u7684\u8a9e\u610f (\u9664\u4e86\u56db\u500b\u8a5e\u5f59\u4f8b\u5916) \uff0c\u6240\u4ee5\u6211\u5011\u7528\u8a5e\u5f59\u7684\u7b2c\u4e00\u500b\u8a9e\u610f\u4f86\u62bd\u53d6\u7279\u5fb5\u3002</td></tr><tr><td>\u53ea\u5f9e\u8a5e\u5f59\u7684\u4e00\u500b\u8a9e\u610f\u62bd\u53d6\u7279\u5fb5\uff0c\u800c\u4e0d\u628a\u8a72\u8a5e\u5f59\u6240\u6709\u7684\u8a9e\u610f\u653e\u5728\u4e00\u8d77\uff0c\u4ee3\u8868\u9019\u7a2e\u65b9\u6cd5\u53ef\u70ba</td></tr><tr><td>\u4e0d\u540c\u7684\u8a9e\u610f\u7d66\u51fa\u4e0d\u540c\u7684\u6975\u6027\u9810\u6e2c\u3002\u53ea\u662f\u7531\u65bc\u76ee\u524d NTUSD \u6975\u6027\u6a19\u8a18\u53ea\u5230\u8a5e\u5f59\u7684\u5c64\u7d1a\uff0c\u6240</td></tr><tr><td>\u4ee5\u7121\u6cd5\u5c0d\u8a9e\u610f\u7684\u5c64\u7d1a\u9032\u884c\u6975\u6027\u9810\u6e2c\u3002\u4f46\u53ea\u8981\u6709\u8a9e\u610f\u5c64\u7d1a\u7684\u6975\u6027\u6a19\u8a18\uff0c\u6211\u5011\u9019\u7a2e\u505a\u6cd5\u53ef\u99ac</td></tr><tr><td>\u4e0a\u5957\u7528\u3002</td></tr><tr><td>1\u3001\u57fa\u790e\u7fa9\u539f\u7279\u5fb5\u52a0\u6b0a\u503c</td></tr><tr><td>\u9664\u4e86\u516c\u5f0f (1) \u7684\u65b9\u5f0f\u5916\uff0c\u6211\u5011\u53ef\u4ee5\u5229\u7528\u66f4\u591a E-HowNet \u7684\u7279\u6027\uff0c\u4f86\u62bd\u53d6\u51fa\u6709\u7528\u7684\u8cc7\u8a0a\u3002</td></tr><tr><td>\u4e00\u500b\u53ef\u80fd\u7684\u65b9\u5f0f\u662f\u5b9a\u7fa9\u5f0f\u4e2d\u7684\u7d50\u69cb\uff0c\u5982\u679c\u628a\u5b9a\u7fa9\u5f0f\u5c55\u958b\uff0c\u6703\u5f97\u5230\u5982\u5716\u4e8c\u7684\u6a39\u72c0\u7d50\u69cb\u3002\u5728</td></tr><tr><td>\u9019\u6a39\u72c0\u7d50\u69cb\u4e2d\uff0c\u7fa9\u539f\u6240\u5728\u7684\u6df1\u5ea6\u662f\u4e00\u500b\u6709\u7528\u7684\u8cc7\u8a0a\uff0c\u56e0\u6b64\u6211\u5011\u4eff\u7167\u5289\u7fa4&\u674e\u7d20\u5efa[15]\u7684</td></tr><tr><td>\u516c\u5f0f\uff0c\u5c07\u6df1\u5ea6\u7684\u8cc7\u8a0a\u7576\u4f5c\u6b0a\u91cd\u5f15\u5165\u516c\u5f0f (1)\uff0c\u5f97\u5230\u516c\u5f0f (2)\u3002</td></tr><tr><td>\u5716\u4e8c\u3001 \u300c\u5929\u502b\u4e4b\u6a02\u300d\u5b9a\u7fa9\u5f0f\u7684\u6a39\u72c0\u8868\u793a</td></tr></table>", | |
| "num": null, | |
| "html": null | |
| }, | |
| "TABREF2": { | |
| "text": "Liu et al.[16] \u6240\u5efa\u7acb\u7684 Google Web 5-gram Version 1\uff0c\u4f86\u62bd\u53d6 \u8a9e\u7bc7\u7279\u5fb5\u3002Google Web 5-gram \u662f Google \u5f9e\u7db2\u8def\u4e2d\u6536\u96c6\u5927\u91cf\u7684\u7c21\u9ad4\u4e2d\u6587\u7db2\u9801\uff0c\u4e26\u7d93\u904e\u8655 \u7406\u6240\u5efa\u7acb\u7684\u8cc7\u6e90\u3002\u4ed6\u5011\u6536\u96c6\u4e86 882,996,532,572 \u500b\u5b57\u7b26(token) \uff0c\u5171 102,048,435,515 \u500b \u53e5\u5b50\uff0c\u7d93\u904e\u65b7\u8a5e\u5f8c\u5efa\u6210 n-gram\u3002n-gram \u7684 n \u5f9e 1 \u5230 5\uff0c\u4e26\u4e14\u53ea\u4fdd\u7559\u983b\u7387\u5927\u65bc 40 \u7684 n-gram\u3002Google Web 5-gram \u7684\u4f8b\u5b50\u5982\u5716\u4e09\u6240\u793a\u3002 \u5716\u4e09\u3001Google Web 5-gram \u8cc7\u6599\u7bc4\u4f8b \u4e0a\u5716\u4e2d\uff0c\u8868\u793a\u300c\u6050\u5413 \u6216 \u975e\u6cd5 \u9a9a\u6270 \u7684\u300d\u9019\u4e00 5-gram \u5171\u51fa\u73fe\u4e86 4463 \u6b21\u3002\u5f9e\u5716\u4e2d\u6211\u5011\u4e5f \u53ef\u770b\u5230\uff0cGoogle Web 5-gram \u662f\u7c21\u9ad4\u4e2d\u6587\uff0c\u4f46\u5ee3\u7fa9\u77e5\u7db2\u70ba\u7e41\u9ad4\u4e2d\u6587\uff0c\u6240\u4ee5\u6211\u5011\u5148\u5c07\u5ee3\u7fa9 \u77e5\u7db2\u7528 Microsoft Word \u7ffb\u8b6f\u70ba\u7c21\u9ad4\u4e2d\u6587\uff0c\u4e4b\u5f8c\u624d\u4f7f\u7528 Google Web 5-gram \u9019\u4e00\u8a9e\u6599\u5eab\u3002 \u8a9e\u6599\u5eab\u5728\u4f7f\u7528\u6642\uff0c\u53ea\u7528 5-gram \u7684\u90e8\u5206\u4f86\u62bd\u53d6\u7279\u5fb5\u3002", | |
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| "content": "<table><tr><td>+1\u3002\u53e6\u5916\uff0c\u5982\u679c\u6a39\u72c0\u7d50\u69cb\u4e0a\u9762\u7684\u7fa9\u539f\u88ab\u5426\u5b9a\u610f\u7fa9\u7684\u95dc\u4fc2\u6240\u4fee\u98fe\uff0c\u9019\u5426\u5b9a\u610f \u7fa9\u6703\u50b3\u905e\u5230\u4e0b\u9762\u7684\u7fa9\u539f\u3002 (\u4e8c) \u3001\u8a9e\u7bc7(context)\u7279\u5fb5 \u5ee3\u7fa9\u77e5\u7db2\u96d6\u7136\u6709\u56b4\u8b39\u7684\u5b9a\u7fa9\u5f0f\u53ef\u7528\u4ee5\u8868\u793a\u8a5e\u5f59\uff0c\u4f46\u662f\u6709\u56db\u500b\u7f3a\u9ede\uff0c\u9020\u6210\u53ea\u7528\u7fa9\u539f\u7576\u7279\u5fb5 \u7121\u6cd5\u6b63\u78ba\u7372\u5f97\u8a5e\u5f59\u7684\u6975\u6027\u3002 \u7b2c\u4e00\u500b\u7f3a\u9ede\u662f\u8a5e\u5f59\u6240\u6a19\u7684\u7fa9\u539f\u91cf\u592a\u5c11\uff0c\u56e0\u70ba\u8a5e\u5f59\u662f\u7528\u4eba\u5de5\u6a19\u793a\u7fa9\u539f\uff0c\u6240\u4ee5\u7121\u6cd5\u7d66\u4e88\u5f88\u591a \u6a19\u793a\u3002\u9019\u8868\u793a\u8a5e\u5f59\u64c1\u6709\u7684\u8cc7\u8a0a\u91cf\u6709\u9650\uff0c\u6703\u9020\u6210\u5206\u985e\u5668\u7121\u6cd5\u6709\u6548\u5b78\u7fd2\u3002\u7b2c\u4e8c\u500b\u7f3a\u9ede\u662f\u7fa9\u539f \u6578\u91cf\u592a\u5c11\uff0c\u9019\u6703\u9020\u6210\u8a9e\u7fa9\u7684\u5283\u5206\u4e0d\u5920\u7cbe\u78ba\uff0c\u7121\u6cd5\u986f\u793a\u51fa\u771f\u5be6\u7684\u8a9e\u7fa9\u5dee\u5225\uff0c\u4f8b\u5982\u300c\u660e\u54f2\u4fdd \u8eab\u300d\u8ddf\u300c\u898b\u98a8\u8f49\u8235\u300d\u7684\u5b9a\u7fa9\u5f0f\u90fd\u662f\u300c{sly|\u72e1}\u300d \uff0c\u4f46\u300c\u660e\u54f2\u4fdd\u8eab\u300d\u662f\u6b63\u9762\u610f\u898b\uff0c \u300c\u898b\u98a8\u8f49 \u8235\u300d\u537b\u662f\u8ca0\u9762\u610f\u898b\u3002\u7b2c\u4e09\u500b\u7f3a\u9ede\u662f\u5ee3\u7fa9\u77e5\u7db2\u5b9a\u7fa9\u6a19\u6e96\u7684\u5dee\u7570\uff0c\u4f8b\u5982\uff0c\u5c08\u6709\u540d\u8a5e\u5728\u5ee3\u7fa9\u77e5 \u7db2\u4e2d\u6703\u7528\u5ba2\u89c0\u7684\u7fa9\u539f\u4f86\u5b9a\u7fa9\uff0c\u4f46\u8a72\u5c08\u6709\u540d\u8a5e\u7d93\u904e\u4f7f\u7528\uff0c\u537b\u53ef\u80fd\u6703\u5f15\u8d77\u4eba\u7684\u6b63\u53cd\u60c5\u7dd2\uff0c\u9019 \u7a2e\u5dee\u7570\u6703\u5f15\u5165\u7a0b\u5ea6\u4e0d\u7b49\u7684\u96dc\u8a0a\u5230\u5206\u985e\u5668\u4e2d\u3002\u7b2c\u56db\u500b\u7f3a\u9ede\u662f\u5ee3\u7fa9\u77e5\u7db2\u5c1a\u672a\u5c0d\u6240\u6709\u8a5e\u5f59\u6a19\u4e0a \u5b9a\u7fa9\u5f0f\uff0c\u4f8b\u5982\u300c\u4e7e\u6de8\u4fd0\u843d\u300d\u5728\u5ee3\u7fa9\u77e5\u7db2\u53ca NTUSD \u4e2d\u90fd\u51fa\u73fe\uff0c\u4f46\u5ee3\u7fa9\u77e5\u7db2\u537b\u6c92\u6709\u6a19\u4e0a\u5b9a \u7fa9\u5f0f\u3002 \u56e0\u6b64\u6211\u5011\u5f15\u5165\u8a9e\u7bc7\u7684\u7279\u6027\uff0c\u5f9e\u8a72\u8a5e\u5f59\u5728\u8a9e\u8a00\u4e2d\u7684\u5be6\u969b\u4f7f\u7528\u60c5\u6cc1\uff0c\u62bd\u53d6\u51fa\u8a5e\u5f59\u7684\u7279\u5fb5\uff0c\u4f86 \u6211\u5011\u4f7f\u7528\u7279\u5fb5\u8ddf\u8a5e\u5f59\u7684\u540c\u51fa\u73fe(co-occurrence)\u6b21\u6578\u505a\u70ba\u7279\u5fb5\u503c\uff0c\u4ee5\u5716\u4e09\u70ba\u4f8b\uff0c\u5982\u679c\u8a5e \u5f59\u662f\u300c\u6050\u5413\u300d \uff0c\u4ee5\u300c\u975e\u6cd5\u300d\u7576\u7279\u5fb5\u503c\uff0c\u5247\u540c\u51fa\u73fe\u6b21\u6578\u6703\u5c07\u6240\u6709\u300c\u6050\u5413\u300d\u53ca\u300c\u975e\u6cd5\u300d\u4e00\u540c \u51fa\u73fe\u7684 5-gram \u6b21\u6578\u76f8\u52a0\u3002\u5728\u4e0a\u9762\u7684\u4f8b\u5b50\u4e2d\uff0c \u300c\u6050\u5413\u300d\u53ca\u300c\u975e\u6cd5\u300d\u7684\u540c\u51fa\u73fe\u6b21\u6578\u70ba 574+200+4463 + 705=5942 \u6b21\u3002 \u53e6\u5916\uff0c\u7531\u65bc\u5ee3\u7fa9\u77e5\u7db2\u8ddf Google Web 5-gram \u7684\u65b7\u8a5e\u6a19\u6e96\u4e26\u4e0d\u4e00\u81f4\uff0c\u6240\u4ee5\u5728\u8655\u7406\u6642\u628a Google Web 5-gram \u7684\u7a7a\u767d\u53bb\u6389\uff0c\u76f4\u63a5\u627e\u51fa\u300c\u8a5e\u5f59\u300d\u8ddf\u300c\u7279\u5fb5\u300d\u9019\u5169\u5b57\u4e32\u662f\u5426\u540c\u6642\u51fa\u73fe\uff0c\u4f86\u8a08 \u7b97\u6b21\u6578\uff0c\u9019\u6a23\u53ef\u4ee5\u907f\u514d\u65b7\u8a5e\u6a19\u6e96\u4e0d\u4e00\u6240\u7522\u751f\u7684\u554f\u984c\u3002\u4f8b\u5982\u300c\u4e00\u4e8b\u7121\u6210\u300d\u5728 Google Web 5-gram \u4e2d\u88ab\u65b7\u6210\u56db\u500b\u7368\u7acb\u7684\u8a5e\uff0c\u5c07\u7a7a\u767d\u53bb\u6389\u5c31\u53ef\u4ee5\u6b63\u78ba\u6bd4\u5c0d\u5230\u3002 \u56e0\u70ba\u9019\u88e1\u7684\u8a5e\u5f59\u96c6\u5408\u5c31\u662f\u7b49\u5f85\u6a19\u793a\u6975\u6027\u7684\u8a5e\uff0c\u6240\u4ee5\u6211\u5011\u53ea\u8981\u6307\u5b9a\u7279\u5fb5\u7684\u96c6\u5408\u5305\u62ec\u90a3\u4e9b \u88dc\u511f\u9019\u4e9b\u7f3a\u9ede\u3002\u6211\u5011\u4f7f\u7528 1\u3001Google Web 5-gram \u7279\u5fb5\u62bd\u53d6 \u8a5e\uff0c\u5c31\u53ef\u7b97\u51fa\u8868\u793a\u8a5e\u5f59 i \u7684\u5411\u91cf</td></tr></table>", | |
| "num": null, | |
| "html": null | |
| }, | |
| "TABREF3": { | |
| "text": "i\u300d\u8ddf\u300c\u7279\u5fb5 j\u300d\u9019\u5169\u5b57\u4e32\u540c\u51fa\u73fe\u7684\u6b21\u6578\u3002\u5728\u6211\u5011\u7684\u5be6\u9a57\u4e2d\uff0c\u5171\u5617\u8a74\u4e86 \u5341\u7a2e\u4e0d\u540c\u7684\u7279\u5fb5\u96c6\u5408\uff0c\u5206\u5225\u662f\u5ee3\u7fa9\u77e5\u7db2\u7684\u540d\u8a5e\u3001\u5ee3\u7fa9\u77e5\u7db2\u7684\u52d5\u8a5e\u3001\u5ee3\u7fa9\u77e5\u7db2\u7684\u526f\u8a5e\u3001\u5ee3 \u7fa9\u77e5\u7db2\u7684\u5f62\u5bb9\u8a5e\u3001\u5ee3\u7fa9\u77e5\u7db2\u6240\u6709\u8a5e\u5f59\u3001Google Web 5-gram \u6700\u5e38\u51fa\u73fe\u7684 5000 \u8a5e\u3001Google Web 5-gram \u6700\u5e38\u51fa\u73fe\u7684 5000 \u8a5e(\u4f46\u8a5e\u5f59\u9577\u5ea6\u6700\u5c11\u70ba 2) \u3001Google Web 5-gram \u6700\u5e38\u51fa\u73fe \u7684 10000 \u8a5e\u3001Google Web 5-gram \u6700\u5e38\u51fa\u73fe\u7684 10000 \u8a5e(\u4f46\u8a5e\u5f59\u9577\u5ea6\u6700\u5c11\u70ba 2) \u3001\u4ee5\u53ca NTUSD \u5b8c\u6574\u7248\u3002", | |
| "type_str": "table", | |
| "content": "<table><tr><td>2\u3001Google Web 5-gram \u7279\u5fb5\u503c\u8655\u7406</td></tr></table>", | |
| "num": null, | |
| "html": null | |
| }, | |
| "TABREF4": { | |
| "text": "ensemble approach) Based Feature)\u90a3\u689d\u6298\u7dda\uff0c\u6700\u4f73\u7684 \u03b1 \u503c\u70ba \u22120.02\uff0c\u6e96\u78ba\u7387\u70ba 89.4397%\u3002\u7576 PBF \u4e2d \u03b1 = 0\uff0c\u8a72\u7d50\u679c\u5373\u70ba\u516c\u5f0f (1) \u7684\u7d50\u679c\u3002\u516c\u5f0f (3) \u7684\u7d50\u679c\u662f PBFN(Prime-Based Feature with Negation)\u90a3\u689d\u6298\u7dda\uff0c\u6700\u4f73\u7684 \u03b1 \u503c\u70ba \u22120.02 \u53ca \u22120.03\uff0c\u6e96\u78ba\u7387\u70ba 89.6121%\u3002 \u5716\u56db\u3001\u5ee3\u7fa9\u77e5\u7db2\u7279\u5fb5\u65bc\u4e0d\u540c \u03b1 \u503c\u7684\u6548\u80fd\u6bd4\u8f03 \u6211\u5011\u5f9e\u5716\u56db\u53ef\u4ee5\u770b\u51fa\uff0c\u63cf\u8ff0 PBFN \u7684\u6298\u7dda\u5728\u6240\u6709\u7684 \u03b1 \u503c\u4e0b\uff0c\u6e96\u78ba\u7387\u7686\u7565\u9ad8\u65bc PBF\uff0c\u4f46\u662f \u22120.03 \u7684\u9810\u6e2c\u7d50\u679c\u70ba (True Positive, False Positive, True Negative, False Negative) = (TP, FP, TN, FN) = (968, 77, 1174, 101)\uff0c\u5176\u4e2d Positive \u8868\u6b63\u9762\u6975\u6027\u3002\u6211\u5011\u5206\u5225\u5c0d\u6b63\u8ca0\u9762\u6975\u6027\u8a08\u7b97 Recall\u3001Precision \u53ca F-Measure (R + \u3001P + \u3001F + \u3001R \u2212 \u3001P \u2212 \u3001F \u2212 )\uff0c\u5176\u4e2d\uff0cP + =TP/(TP+FP)\u3001 R + =TP/(TP+FN)\u3001F + = 2P + R + /(P + +R + )\u3001P \u2212 =TN/(TN+FN)\u3001R \u2212 =TN/(TN+FP)\u3001F \u2212 = 2P \u2212 R \u2212 /(P \u2212 +R \u2212 )\uff0c\u6700\u5f8c\u7cfb\u7d71\u7684 Recall=(R + +R \u2212 )/2\u3001Precision=(P + +P \u2212 )/2 \u53ca F-Measure = (F + +F \u2212 )/2 = (91.58% + 92.95%)/2 = 92.27%\u3002\u7531\u8a08\u7b97\u4e2d\u6211\u5011\u53ef\u4ee5\u770b\u5230\uff0c\u6211\u5011\u7684\u7cfb\u7d71\u5c0d\u8ca0\u9762 \u6975\u6027\u505a\u5f97\u8f03\u597d\uff0c\u800c\u4e14\u56e0\u8cc7\u6599\u96c6\u6709\u8f03\u591a\u7684\u8ca0\u9762\u8a5e\u5f59\uff0c\u6240\u4ee5\u6700\u5f8c\u7684\u6e96\u78ba\u7387 92.33% \u6bd4 F + \u9ad8\u3002 \u4e94\u3001\u7d50\u8ad6 Research of this paper was partially supported by National Science Council (Taiwan) under the contract NSC 98-2221-E-002-175-MY3.", | |
| "type_str": "table", | |
| "content": "<table><tr><td>\u8868\u4e00\u3001\u5ee3\u7fa9\u77e5\u7db2\u3001NTUSD\u3001\u4ee5\u53ca\u4ea4\u96c6\u7684\u8cc7\u6599\u7b46\u6578 n 1,1 \uff1a \u5206\u985e\u5668 A \u8207\u5206\u985e\u5668 B \u7686\u6b63\u78ba n 0,1 \uff1a \u5206\u985e\u5668 A \u6a19\u8a18\u932f\u8aa4\uff0c\u4f46\u5206\u985e \u96c6\u4ee3\u865f\u4f86\u4ee3\u8868\u8a72\u7279\u5fb5\u96c6\u3002\u5341\u7d44\u7279\u5fb5\u96c6\u4e2d\uff0c\u6700\u5c11\u7684\u662f Adj \u7684\u7279\u5fb5\u96c6\uff0c\u53ea\u6709 948 \u500b\u8a5e\uff0c\u6700 \u5716\u4e94\u4e2d\u7279\u5fb5\u96c6\u7684\u500b\u6578\uff0c\u4e26\u6c92\u6709\u7d55\u5c0d\u7684\u5f71\u97ff\uff0c\u4f46\u82e5\u500b\u6578\u592a\u5c11\uff0c\u5982\u7279\u5fb5\u500b\u6578\u5c0f\u65bc 2364 \u500b\uff0c \u6a19\u8a18\u6548\u80fd\u4ee5\u7c97\u9ad4\u5b57\u8868\u793a\u3002 \u8868\u4e94\u4e2d\uff0c\u6211\u5011\u4e5f\u5217\u51fa\u6bcf\u7a2e\u8a5e\u6027\u505a\u932f\u8207\u505a\u5c0d\u7684\u500b\u6578\uff0c\u4e26\u4ee5 F10000-2+PBFN \u03b1 = \u22120.03 \u5206\u985e\u5668\u70ba</td></tr><tr><td>\u8cc7\u6599\u96c6 \u6a19\u8a18\u7684\u6a23\u672c\u6578 \u591a\u7684\u662f All \u7684\u7279\u5fb5\u96c6\uff0c\u6709 86,712 \u500b\u8a5e\u3002 \u5247\u6548\u80fd\u6703\u660e\u986f\u8b8a\u5dee\u3002\u5716\u56db\u4e2d\u7684\u6700\u4f73\u503c PBFN(\u03b1 = \u22120.02)\u70ba 89.61%\uff0c\u7279\u5fb5\u500b\u6578\u70ba 2,567 \u6b63\u9762 \u8ca0\u9762 \u7e3d\u6578 \u5668 B \u6a19\u8a18\u6b63\u78ba\u7684\u6a23\u672c\u6578 \u57fa\u6e96\uff0c\u770b\u7d44\u5408\u5f8c\u7684\u5206\u985e\u5668\uff0c\u5728\u5404\u8a5e\u6027\u4e2d\u505a\u5c0d\u505a\u932f\u7684\u6b21\u6578\u7684\u589e\u6e1b\uff0c\u7528\u62ec\u865f\u4f86\u6a19\u51fa\u589e\u6e1b\u7684\u6578</td></tr><tr><td>\u7531\u65bc\u5ee3\u7fa9\u77e5\u7db2\u8a5e\u5f59\u7684\u6bcf\u4e00\u500b\u610f\u7fa9(sense)\u90fd\u6a19\u6709\u8a5e\u6027\uff0c\u800c\u4e14\u6211\u5011\u7528\u4e86\u5f88\u591a\u4e0d\u540c\u7684\u7279\u5fb5 \u96c6\u5408\uff0c\u9019\u8868\u793a\u6211\u5011\u6703\u6709\u5f88\u591a\u4e0d\u540c\u7684\u5206\u985e\u5668\u3002\u5982\u679c\u4f9d\u4e0d\u540c\u8a5e\u6027\u9078\u64c7\u505a\u5f97\u6700\u597d\u7684\u5206\u985e\u5668\uff0c\u5247 \u53ef\u4ee5\u6709\u66f4\u597d\u7684\u6548\u80fd\u3002\u4f8b\u5982\uff0c\u5982\u679c\u5206\u985e\u5668 A \u7684\u7e3d\u9ad4\u6548\u80fd\u4e0d\u662f\u6700\u597d\uff0c\u4f46\u5982\u679c\u5b83\u5c0d\u540d\u8a5e\u505a\u7684 \u6548\u80fd\u662f\u6700\u597d\u7684\uff0c\u4e5f\u8a31\u62ff\u5b83\u4f86\u9810\u6e2c\u540d\u8a5e\u7684\u6975\u6027\u6703\u66f4\u6e96\u78ba\uff0c\u4f9d\u6b64\u985e\u63a8\u3002\u6211\u5011\u628a\u5ee3\u7fa9\u77e5\u7db2\u7684\u8a5e \u6027\uff0c\u5206\u70ba\u540d\u8a5e\u3001\u52d5\u8a5e\u3001\u526f\u8a5e\u3001\u5f62\u5bb9\u8a5e\u53ca\u5176\u4ed6\u5171\u4e94\u985e\uff0c\u5206\u5225\u9078\u5728\u8a72\u985e\u5225\u9810\u6e2c\u6700\u597d\u7684\u5206\u985e\u5668 \u4f86\u9810\u6e2c\u3002\u9019\u4f5c\u6cd5\u662f\u4e00\u7a2e\u5e38\u898b\u7684\u7d44\u5408\u4e0d\u540c\u5206\u985e\u5668\u7684\u7b56\u7565(ensemble approach) \uff0c\u6211\u5011\u4e5f\u6703 \u5c0d\u6b64\u9032\u884c\u5be6\u9a57\uff0c\u4f86\u89c0\u5bdf\u6548\u80fd\u3002 \u56db\u3001\u5be6\u9a57\u8207\u5206\u6790 (\u4e00) \u3001\u5be6\u9a57\u8cc7\u6599\u8207\u5be6\u9a57\u8a2d\u5b9a \u672c\u7814\u7a76\u4f7f\u7528\u570b\u7acb\u53f0\u7063\u5927\u5b78\u610f\u898b\u8a5e\u8a5e\u5178\u5b8c\u6574\u7248(NTUSD) \u3001\u8207\u5ee3\u7fa9\u77e5\u7db2\u7684\u4ea4\u96c6\uff0c\u4f5c\u70ba\u5be6\u9a57 \u8cc7\u6599\uff0c\u9019\u5169\u500b\u8cc7\u6599\u96c6\u7684\u8a5e\u5f59\u6578\u5982\u8868\u4e00\u3002\u8cc7\u6599\u96c6 E-HowNet\u2229NTUSD \u6703\u4f5c\u70ba\u6a19\u6e96\u7b54\u6848\u96c6\uff0c \u5728\u6211\u5011\u6240\u770b\u7684\u76f8\u95dc\u8ad6\u6587\u4e2d\uff0c\u9019\u500b\u7b54\u6848\u96c6\u7684\u5927\u5c0f\u662f\u6700\u5927\u7684\u4e00\u500b\u3002\u5be6\u9a57\u4f7f\u7528\u6a19\u6e96\u7b54\u6848\u96c6\u5176\u4e2d \u7684 80% \u70ba\u8a13\u7df4\u8cc7\u6599\u96c6\uff0c\u5176\u9918 20%\u70ba\u6e2c\u8a74\u8cc7\u6599\u96c6\uff0c\u4e26\u4f9d\u7167\u5be6\u9a57\u8cc7\u6599\u7684\u8a5e\u6027\u5206\u5e03\u4ee5\u53ca\u8a9e\u610f \u6975\u6027\u5206\u5e03\u4f5c\u5206\u5c64\u62bd\u6a23(stratified sampling) \u3002 E-HowNet N/A N/A 88,127 NTUSD 21,056 22,750 43,806 E-HowNet\u2229NTUSD 5,346 6,256 11,602 \u5206\u5c64\u62bd\u6a23\u8a73\u7d30\u7684\u4f5c\u6cd5\u5982\u4e0b\uff1a\u5148\u5c07\u8cc7\u6599\u4f9d\u7167\u4e94\u7a2e\u8a5e\u6027\u4ee5\u53ca\u5169\u7a2e\u6975\u6027\u5206\u6210\u5341\u500b\u5b50\u96c6\u5408\uff0c\u518d\u91dd \u5c0d\u6bcf\u500b\u5b50\u96c6\u5408\u53d6\u5176\u4e2d 80%\u4f5c\u70ba\u8a13\u7df4\u8cc7\u6599\uff0c\u53e6\u5916 20%\u4f5c\u70ba\u6e2c\u8a74\u8cc7\u6599\u3002\u7531\u65bc\u6211\u5011\u7684\u8cc7\u6599\u91cf \u5920\u591a\uff0c\u6240\u4ee5\u53ef\u4ee5\u4f7f\u7528\u9019\u7a2e\u62bd\u6a23\u3002\u9019\u7a2e\u62bd\u6a23\u4e3b\u8981\u7684\u597d\u8655\u5728\u65bc\u6211\u5011\u66f4\u5bb9\u6613\u5c0d\u6e2c\u8a74\u7d50\u679c\u9032\u884c\u66f4 \u591a\u7684\u5206\u6790\uff0c\u6211\u5011\u628a\u5206\u5c64\u62bd\u6a23\u7684\u7d50\u679c\u5217\u65bc\u8868\u4e8c\u3002 \u8868\u4e8c\u3001\u8a13\u7df4\u8cc7\u6599\u7684\u8a5e\u6027\u4ee5\u53ca\u50be\u5411\u5206\u5e03 \u8a5e\u6027 \u5168\u90e8\u8cc7\u6599\u96c6 \u6b63\u9762\u50be\u5411 \u767e\u5206\u6bd4 \u8a13\u7df4\u8cc7\u6599\u96c6 \u6e2c\u8a74\u8cc7\u6599\u96c6 \u6b63\u9762 \u8ca0\u9762 \u6b63\u9762 \u8ca0\u9762 \u6b63\u9762 n 1,0 \uff1a \u5206\u985e\u5668 B \u6a19\u8a18\u932f\u8aa4\uff0c\u4f46\u5206\u985e \u5668 A \u6a19\u8a18\u6b63\u78ba\u7684\u6a23\u672c\u6578 n 0,0 \uff1a \u5206\u985e\u5668 A \u8207\u5206\u985e\u5668 B \u7686\u932f\u8aa4 \u6a19\u8a18\u7684\u6a23\u672c\u6578 McNemar \u6aa2\u5b9a\u5efa\u69cb\u5728\u5361\u65b9\u9069\u5408\u5ea6\u6aa2\u5b9a (\u03c72 test goodness of fit)\u4e0a\uff0c\u6574\u7406\u800c\u5f97\u7684\u6aa2\u5b9a\u503c \u70ba 0 , 1 1 , 0 2 0 , 1 1 , 0 ) 1 ( n n n \u500b\uff0c\u9019\u500b\u503c\u6bd4\u5716\u4e94\u4e2d\u7684\u6700\u4f73\u503c 88.23%\u9084\u8981\u5927\uff0c\u9019\u8868\u793a\u5ee3\u7fa9\u77e5\u7db2\u4e2d\u7684\u7279\u5fb5\u6bd4\u8f03\u6e96\u78ba\uff0c\u4f46 \u91cf\u3002 \u8868\u4e09\u3001\u8a9e\u7bc7\u7279\u5fb5\u6240\u4f7f\u7528\u7684\u7279\u5fb5\u96c6\u8207\u5176\u7279\u5fb5\u6578 \u7279\u5fb5\u96c6 \u7279\u5fb5\u96c6\u4ee3\u865f \u8868\u56db\u3001\u8a13\u7df4\u8cc7\u6599\u96c6\u4e2d\uff0c\u7d44\u5408\u7279\u5fb5\u5c0d\u4e0d\u540c\u8a5e\u6027\u7684\u6a19\u8a18\u6e96\u78ba\u7387 \u9019\u5dee\u8ddd\u70ba\u4e0d\u986f\u8457\uff0c\u6aa2\u5b9a\u7d50\u679c (2.49, 0.11)\u3002 EnsembleClassifier \u6240\u5f97\u6210\u7e3e\u8ddf F10000-2+PBFN \u03b1 = \u22120.03 \u76f8\u540c\uff0c\u9019\u8868\u793a\u76ee\u524d\u7684\u5206\u985e\u5668\u7d44\u5408\u65b9 \u7279\u5fb5\u6578 \u5ee3\u7fa9\u77e5\u7db2\u540d\u8a5e Noun 46,807 (\u56db) \u3001\u7d44\u5408\u4e0d\u540c\u7279\u5fb5\u7684\u6548\u80fd \u8a13\u7df4\u8cc7\u6599\u96c6\u4e2d\uff0c\u4f9d\u8a5e\u6027\u5206\u5225\u8a08\u7b97\u7684\u6e96\u78ba\u7387 \u5f0f\uff0c\u7121\u6cd5\u63d0\u5347\u6548\u80fd\u3002 \u7279\u5fb5\u96c6\u4ee3\u865f \u7e3d\u9ad4\u6548\u80fd \u540d\u8a5e \u52d5\u8a5e \u526f\u8a5e \u5f62\u5bb9\u8a5e \u5176\u4ed6 n \uff0c\u6b64\u6aa2\u5b9a\u503c\u5728 n 0,1 +n 1,0 \u5920\u5927\u7684\u6642\u5019\u6703\u8da8\u8fd1\u65bc\u81ea\u7531\u5ea6\u70ba 1 \u7684\u5361\u65b9\u5206\u914d\uff0c\u56e0 \u6b64\u5728\u986f\u8457\u6c34\u6e96(significant level)\u70ba 0.95 \u6642\uff0c\u6b64\u503c\u82e5\u5927\u65bc 8415 . 3 2 95 . 0 , 1 \uff0c\u5247\u62d2\u7d55\u865b\u7121\u5047 \u8a2d\u3002\u6211\u5011\u7528 (McNemar \u6aa2\u5b9a\u7d50\u679c, p-value) \u4f86\u986f\u793a\u6211\u5011\u7684\u6aa2\u5b9a\u7d50\u679c\uff0c\u4f8b\u5982\u6aa2\u5b9a\u7d50\u679c (1.50, 0.22) \u8868\u793a\uff0cMcNemar \u6aa2\u5b9a\u7d50\u679c\u70ba 1.50 < 3.84\uff0c\u6240\u4ee5\u6c92\u6709\u901a\u904e McNemar \u6aa2\u5b9a\uff0cp-value \u70ba 0.22\u3002 (\u4e8c) \u3001\u57fa\u790e\u7fa9\u539f\u7279\u5fb5\u7684\u6548\u80fd \u5716\u56db\u70ba\u57fa\u790e\u7fa9\u539f\u65b9\u6cd5\u5728\u4e0d\u540c \u03b1 \u503c\u6240\u5f97\u5230\u7684\u9810\u6e2c\u6e96\u78ba\u7387\uff0c\u5176\u4e2d\u516c\u5f0f (2) \u7684\u7d50\u679c\u662f PBF \u5ee3\u7fa9\u77e5\u7db2\u52d5\u8a5e Verb 37,109 \u5ee3\u7fa9\u77e5\u7db2\u526f\u8a5e Adv. Adj. 94.3223% 95.9559% 94.2167% 89.9023% 93.2203% 82.0513% (\u516d) \u3001\u76f8\u95dc\u7814\u7a76\u6548\u80fd\u6bd4\u8f03 \u7d44\u5408\u7279\u5fb5\u6642\uff0c\u56e0\u70ba\u9918\u5f26\u6a19\u6e96\u5316\u6709\u6700\u597d\u7684\u6548\u80fd\uff0c\u6240\u4ee5\u8a9e\u7bc7\u7279\u5fb5\u9078\u64c7\u9918\u5f26\u6a19\u6e96\u5316\u5f8c\u7684\u5341\u7d44\u7279 2,364 \u5ee3\u7fa9\u77e5\u7db2\u5f62\u5bb9\u8a5e Adj. 948 \u5ee3\u7fa9\u77e5\u7db2\u6240\u6709\u8a5e\u5f59 All 86,712 \u6700\u5e38\u51fa\u73fe 5000 \u8a5e F5000-1 5,000 \u6700\u5e38\u51fa\u73fe 5000 \u8a5e(\u9577\u5ea6\u22672) F5000-2 5,000 \u6700\u5e38\u51fa\u73fe 10000 \u8a5e F10000-1 10,000 \u6700\u5e38\u51fa\u73fe 10000 \u8a5e(\u9577\u5ea6\u22672) F10000-2 10,000 NTUSD(\u5b8c\u6574\u7248) NTUSD Adv. 95.3243% 96.5074% 95.2795% 92.1824% 91.5254% 84.6154% \u5fb5\u96c6\uff0c\u5206\u5225\u8207\u5ee3\u7fa9\u77e5\u7db2\u7279\u5fb5\u6548\u80fd\u6700\u597d\u7684 PBFN \u03b1 = \u22120.03 \u7d44\u5408\uff0c\u4f86\u8a13\u7df4\u5206\u985e\u5668\uff0c\u5206\u985e\u5668\u9810\u6e2c F5000-1 96.1000% 97.3039% 96.0110% 92.8339% 94.9153% 89.7436% \u6211\u5011\u7e3d\u7d50\u524d\u9762\u5404\u7a2e\u4e0d\u540c\u7684\u5be6\u9a57\u7d50\u679c\uff0c\u756b\u6210\u5716\u4e03\uff0c\u4f86\u65b9\u4fbf\u6211\u5011\u6bd4\u8f03\u6548\u80fd\u3002\u5176\u4e2d\uff0cgloss \u8868 \u6e96\u78ba\u7387\u5982\u5716\u516d\u3002\u5176\u4e2d\u5ee3\u7fa9\u77e5\u7db2\u7279\u5fb5\u7684\u7279\u5fb5\u96c6\u6548\u80fd\u70ba\u56fa\u5b9a\uff0c\u56e0\u6b64\u4ee5\u6c34\u5e73\u76f4\u7dda\u8868\u793a(gloss F5000-2 97.2635% 98.0392% 97.1705% 96.0912% 94.9153% 94.8718% \u57fa\u790e\u7fa9\u539f\u7279\u5fb5 PBFN \u03b1 = \u22120.03 \uff0c\u6700\u597d\u7684\u6548\u80fd\u5230 92.3276%\u3002 \u90a3\u689d\u6298\u7dda)\u3002\u7d44\u5408\u800c\u6210\u7684\u7279\u5fb5\u96c6\uff0c\u4ee5\u300c\u8a9e\u7bc7\u7279\u5fb5\u96c6\u4ee3\u78bc+PBFN \u03b1 = \u22120.03 \u300d\u52a0\u4ee5\u547d\u540d\uff0c\u4f8b\u5982 \u300cF10000-2+PBFN \u03b1 = \u22120.03 \u300d\u8868\u793a\u300c\u6700\u5e38\u51fa\u73fe 10000 \u8a5e(\u9577\u5ea6\u22672)\u300d\u8ddf\u300cPBFN \u03b1 = \u22120.03 \u300d \u5169\u500b\u7279\u5fb5\u96c6\u7684\u7d44\u5408\u3002 \u6211\u5011\u5f9e\u5716\u516d\u53ef\u4ee5\u770b\u51fa\uff0c\u5c07\u5ee3\u7fa9\u77e5\u7db2\u7279\u5fb5\u8207\u5916\u90e8\u8a9e\u6599\u7279\u5fb5\u7d44\u5408\u4e4b\u5f8c\uff0c\u6e96\u78ba\u7387\u90fd\u6709\u986f\u8457\u63d0 F10000-1 96.2400% 97.3652% 96.1767% 92.8339% 94.9153% 89.7436% \u672c\u7814\u7a76\u4f7f\u7528\u4e86 Google Web 5-gram Version 1 \u4f86\u62bd\u53d6\u8a9e\u7bc7\u7279\u5fb5\uff0c\u4e26\u52a0\u4e0a\u4f86\u81ea E-HowNet \u7684 F10000-2 97.5005% 98.2843% 97.4189% 96.0912% 94.9153% 94.8718% Verb \u57fa\u790e\u7fa9\u539f\u7279\u5fb5\uff0c\u7528\u76e3\u7763\u5f0f\u6a5f\u5668\u5b78\u7fd2\u7684\u65b9\u6cd5\uff0c\u4f86\u9810\u6e2c E-HowNet \u8a5e\u5f59\u7684\u610f\u898b\u6975\u6027\u3002\u96d6\u7136\u55ae 96.5632% 97.5490% 96.5079% 94.4625% 91.5254% 89.7436% NTUSD \u7368\u4f7f\u7528\u4e0d\u540c\u7684\u7279\u5fb5\u5df2\u7d93\u53ef\u4ee5\u63a5\u8fd1 90% \u7684\u6e96\u78ba\u7387\uff0c\u4f46\u5982\u679c\u628a\u5169\u7a2e\u7279\u5fb5\u90fd\u52a0\u4ee5\u4f7f\u7528\uff0c\u5206\u985e 96.8218% 97.3039% 96.8254% 95.1140% 93.2203% 94.8718% \u5668\u7684\u6975\u6027\u9810\u6e2c\u7684\u6e96\u78ba\u7387\u53ef\u5230\u9054 92.33%\uff0c\u9019\u500b\u7d50\u679c\u8ddf\u4eba\u7684\u6a19\u8a18\u6e96\u78ba\u7387\u4e0d\u76f8\u4e0a\u4e0b\uff1b\u4ee5\u9019\u7a2e 42,614 Noun 96.8541% 98.1005% 96.6460% 96.0912% 96.6102% 89.7436% \u65b9\u5f0f\u5efa\u7acb\u7684\u5206\u985e\u5668\uff0c\u53ef\u7528\u4f86\u81ea\u52d5\u6a19\u8a18 E-HowNet \u8a5e\u5f59\u7684\u610f\u898b\u6975\u6027\u3002 \u5347\uff0c\u63d0\u5347\u5f8c\u7684\u6700\u9ad8\u6e96\u78ba\u7387\u70ba 92. 3276%\uff0c\u4f7f\u7528\u300c\u5ee3\u7fa9\u77e5\u7db2\u6240\u6709\u8a5e\u5f59 All+PBFN \u03b1 = \u22120.03 \u300d\u548c All 96.4124% 97.4265% 96.3699% 93.1596% 94.9153% 89.7436% \u300c\u6700\u5e38\u51fa\u73fe 10000 \u8a5e(\u9577\u5ea6\u22672) F10000-2+PBFN \u03b1 = \u22120.03 \u300d\u70ba\u7279\u5fb5\u96c6\u6642\u7686\u6709\u76f8\u540c\u7684\u6e96\u78ba \u6211\u5011\u5e0c\u671b\u5728\u672a\u4f86\u80fd\u628a\u9019\u7a2e\u65b9\u5f0f\uff0c\u5f80\u4e0d\u540c\u7684\u65b9\u5411\u64f4\u5c55\uff0c\u4f86\u7d66\u4e88 E-HowNet \u8a5e\u5f59\u66f4\u591a\u610f\u898b\u7684 \u8ca0\u9762 \u540d\u8a5e 2,040 931 1,109 45.64% 745 887 186 222 \u52d5\u8a5e 9,056 4,134 4922 45.65% 3,307 3,938 827 984 \u526f\u8a5e 383 206 177 53.79% 165 142 41 35 \u5f62\u5bb9\u8a5e 74 45 29 60.81% 36 23 9 6 \u5176\u4ed6 49 30 19 61.22% 24 15 6 4 \u7e3d\u6578 11,602 5,346 6,256 46.08% 4,277 5,005 1,069 1,251 -5 , 2 -3 , 2 -1 ,\u2026, 2 15 }\u3001g \u2208 {2 -15 , 2 -13 , 2 -11 , \u2026, 2 -3 }\uff0c\u7e3d\u5171 110 \u7d44\u53c3\u6578\uff0c\u53d6\u4e94\u758a\u4ea4\u53c9\u9a57\u8b49 (5-fold cross validation) \u4e2d\u5e73\u5747\u6e96\u78ba\u7387\u6700\u9ad8\u7684\u53c3\u6578\u3002 \u6211\u5011\u4f7f\u7528\u9810\u6e2c\u6e96\u78ba\u7387(accuracy)\u4f86\u6bd4\u8f03\u5206\u985e\u5668\u9593\u7684\u6548\u80fd\uff0c\u9019\u662f\u770b\u8a13\u7df4\u51fa\u7684\u5206\u985e\u5668\u5728\u6e2c \u8a74\u8cc7\u6599\u96c6\u4e2d\u7684\u6210\u7e3e\uff0c\u800c\u5206\u985e\u5668\u6703\u5c0d\u6e2c\u8a74\u8cc7\u6599\u96c6\u4e2d\u7684\u6240\u6709\u8a5e\u5f59\u90fd\u9032\u884c\u6975\u6027\u7684\u9810\u6e2c\u3002\u53e6\u5916\uff0c \u4f7f\u7528 McNemar \u6aa2\u5b9a[18]\u4f86\u6e2c\u8a74\u5206\u985e\u5668\u7684\u6548\u80fd\u5dee\u8ddd\u662f\u5426\u70ba\u986f\u8457\uff0c\u986f\u8457\u6c34\u6e96\u8a2d\u5b9a\u70ba 0.95\u3002 McNemar \u6aa2\u5b9a\u5c07\u6e2c\u8a74\u8cc7\u6599\u4f9d\u7167\u5169\u500b\u5206\u985e\u5668(\u4ee5\u4e0b\u7a31\u70ba\u5206\u985e\u5668 A \u8207\u5206\u985e\u5668 B)\u7684\u6a19\u8a18\uff0c \u5206\u6210\u56db\u7d44\u4e26\u8a08\u6578\u3002\u5176\u4e2d\u6e2c\u8a74\u6a23\u672c\u6578\u5373\u70ba\u4e0b\u9762 n 1,1 \u3001n 0,1 \u3001n 1,0 \u3001n 0,0 \u56db\u500b\u6578\u5b57\u7684\u7e3d\u5408\uff0c\u5728\u865b \u7121\u5047\u8a2d(null hypothesis)\u4e2d\uff0c\u5169\u500b\u5206\u985e\u5668\u61c9\u5177\u6709\u76f8\u540c\u7684\u932f\u8aa4\u7387\uff0c\u4e5f\u5c31\u662f n 0,1 =n 1,0 \u3002 \u7531\u65bc \u03b1 < 0 \u6709\u6700\u4f73\u6548\u80fd\uff0c\u9019\u8868\u793a\u6df1\u5ea6\u8f03\u6df1\u7d66\u8f03\u9ad8\u6b0a\u91cd\uff0c\u8a72\u7fa9\u539f\u6709\u8f03\u597d\u7684\u7279\u5fb5\uff0c\u53ef\u4ee5\u7d66\u5206 \u985e\u5668\u5b78\u7fd2\u3002 (\u4e09) \u3001\u8a9e\u7bc7\u7279\u5fb5\u7684\u6548\u80fd \u6211\u5011\u4f7f\u7528\u4e09\u7a2e\u4e0d\u540c\u7684\u52a0\u6b0a\u65b9\u5f0f\u5f97\u5230\u7684\u9810\u6e2c\u6e96\u78ba\u7387\u5982\u5716\u4e94\uff0c\u5716\u4e2d\u6211\u5011\u4e5f\u628a\u7279\u5fb5\u96c6\u7684\u7279\u5fb5\u6578 \u7531\u5de6\u81f3\u53f3\u7531\u5c0f\u5230\u5927\u6392\u5217\u3002 \u5716\u4e94\u3001\u4f7f\u7528\u8a9e\u7bc7\u7279\u5fb5\u6642\u7684\u9810\u6e2c\u6548\u80fd \u5f9e\u5716\u4e94\u53ef\u4ee5\u770b\u51fa\uff0c\u6c92\u6709\u6a19\u6e96\u5316\u7684\u539f\u59cb\u983b\u7387\u7684\u6700\u4f73\u6e96\u78ba\u7387\u70ba 59.70%\uff0c\u4f7f\u7528\u7684\u7279\u5fb5\u96c6\u70ba\u300c\u5ee3 \u7387\u70ba 83.41%\uff0c\u4f7f\u7528\u7684\u7279\u5fb5\u96c6\u70ba\u300c\u6700\u5e38\u51fa\u73fe 10000 \u8a5e\u300d \u3002\u800c\u7d93\u904e\u9918\u5f26\u6a19\u6e96\u5316\u7684\u7279\u5fb5\u503c\u5247\u53ef \u4ee5\u5f97\u5230\u6700\u4f73\u6548\u80fd\uff0c\u5176\u6700\u4f73\u6e96\u78ba\u7387\u70ba 88.23%\uff0c\u6b64\u6642\u4f7f\u7528\u7684\u7279\u5fb5\u96c6\u70ba\u300c\u5ee3\u7fa9\u77e5\u7db2\u52d5\u8a5e\u300d \uff0c\u6b64 \u5f62\u5bb9\u8a5e 14 1 93.3333% Noun 12 (-2) 3 80.0000% \u540c\u8a5e\u6027\u6709\u4e0d\u540c\u7684\u6548\u80fd\uff0c\u6211\u5011\u5c07\u9019\u5341\u500b\u5206\u985e\u5668\u5c0d\u65bc\u6bcf\u500b\u8a5e\u6027\u7684\u6a19\u8a18\u6548\u80fd\u6574\u7406\u6210\u8868\u56db\u3002\u8868\u56db \u526f\u8a5e 67 9 88.1579% F5000-2 69 (+2) 7 90.7895% 96.58% 88.87% 92.56% \u4e09\u4eba\u4e2d\u6700\u4f73\u7684\u4eba\u985e\u6a19\u8a18\u8005 \u5728\u5716\u516d\u4e2d\uff0c\u7d44\u5408\u51fa\u7684\u7279\u5fb5\u96c6\u6709\u5341\u500b\uff0c\u6240\u4ee5\u5171\u6709\u5341\u500b\u5206\u985e\u5668\uff0c\u6bcf\u500b\u5206\u985e\u5668\u5728\u8a13\u7df4\u6642\uff0c\u5c0d\u4e0d \u52d5\u8a5e 1,681 130 92.8216% F10000-2 1,681 (+0) 130 92.8216% F10000-2+PBFN \u03b1 = \u22120.03 92.36% 92.20% 92.27% \u7fa9\u77e5\u7db2\u540d\u8a5e\u300d \uff0c\u5176\u6548\u80fd\u6700\u5dee\u4e14\u5dee\u8ddd\u5f88\u5927\u3002\u9918\u5f26\u6a19\u6e96\u5316 TFIDF \u7684\u6548\u80fd\u6392\u5728\u4e2d\u9593\uff0c\u6700\u4f73\u6e96\u78ba \u7387\u3002\u4e0a\u5716\u4e2d\uff0c \u300c\u5ee3\u7fa9\u77e5\u7db2\u6240\u6709\u8a5e\u5f59 All\u300d\u6e96\u78ba\u7387\u5f9e 88.23%\u63d0\u5347\u81f3 92.33%\u6642\uff0c\u6b64\u5dee\u8ddd\u70ba\u986f \u8457\uff0c\u6aa2\u5b9a\u7d50\u679c (32.14, 1.4*10 -8 )\u3002 \u5716\u516d\u3001\u5ee3\u7fa9\u77e5\u7db2\u3001\u8a9e\u7bc7\u7279\u5fb5\u3001\u8207\u7d44\u5408\u7279\u5fb5\u7684\u6e96\u78ba\u7387\u6bd4\u8f03 (\u4e94) \u3001\u7d44\u5408\u5f0f\u7684\u76e3\u7763\u5f0f\u6a5f\u5668\u5b78\u7fd2\u6f14\u7b97\u6cd5\u6548\u80fd \u8868\u56db\u4e2d\u6211\u5011\u53ef\u4ee5\u767c\u73fe\uff0c\u8a13\u7df4\u6642\uff0cF10000-2+PBFN \u03b1 = \u22120.03 \u6709\u6700\u9ad8\u7684\u7e3d\u9ad4\u6548\u80fd\uff0c\u5176\u5404\u8a5e\u6027\u6548 \u80fd\u9664\u4e86\u5f62\u5bb9\u8a5e\u5916\uff0c\u591a\u662f\u6700\u597d\uff1b\u8003\u91cf\u5230\u8cc7\u6599\u96c6\u4e2d\u5f62\u5bb9\u8a5e\u7684\u6578\u91cf\u4e26\u4e0d\u591a\uff0c\u9019\u8868\u793a\u7d44\u5408\u591a\u500b\u5206 \u985e\u5668\u5f8c\uff0c\u6548\u80fd\u7684\u63d0\u6607\u7a7a\u9593\u53ef\u80fd\u6709\u9650\u3002\u8868\u56db\u4e2d\u53e6\u4e00\u500b\u503c\u5f97\u6ce8\u610f\u7684\u4e00\u9ede\u662f\u8a13\u7df4\u8cc7\u6599\u96c6\u7684\u5167\u90e8 \u6e2c\u8a74\u6548\u80fd(inside test)F10000-2+PBFN \u03b1 = \u22120.03 \u7684 97.5005% \u8ddf\u5be6\u969b\u6e2c\u8a74\u6548\u80fd 92. 3276% \u76f8\u6bd4\uff0c\u964d\u4f4e\u4e86 5.31%\uff0c\u9019\u964d\u4f4e\u5e45\u5ea6\u4e26\u4e0d\u5927\uff0c\u986f\u793a\u9019\u5206\u985e\u5668\u7684 generalization \u80fd\u529b\u4e0d\u932f\uff0c\u9019 \u4e5f\u662f\u4f7f\u7528 Google Web 5-gram \u7684\u512a\u9ede\uff0c\u5b83\u53ef\u7522\u751f\u8f03\u5f37\u5065(robust)\u7684\u5206\u985e\u5668[19]\u3002 \u6211\u5011\u7a31\u70ba EnsembleClassifier\uff0c\u5176\u7d50\u679c\u5217\u5728\u8868\u4e94\uff0c\u5176\u4e2d F10000-2+PBFN \u03b1 = \u22120.03 \u65bc\u5404\u8a5e\u6027 \u7684\u6a19\u8a18\u6548\u80fd\u4e5f\u5217\u51fa\u4f86\u6bd4\u8f03\u3002 \u8868\u4e94\u3001\u7d44\u5408\u5206\u985e\u5668\u65bc\u5404\u8a5e\u6027\u7684\u6a19\u8a18\u6548\u80fd\u53ca\u6bd4\u8f03 \u5206 \u985e \u5668 \u540d\u8a5e 371 37 90.9314% F10000-2 371 (+0) 37 90.9314% Recall Precision F-Measure \u5206\u985e\u5668 \u8a5e\u6027 F10000-2+PBFN \u03b1 = \u22120.03 \u5206\u985e\u5668 \u65bc\u5404\u8a5e\u6027\u7684\u6a19\u8a18\u6548\u80fd \u7d44\u5408\u5206\u985e\u5668 EnsembleClassifier \u65bc\u5404\u8a5e\u6027\u7684\u6a19\u8a18\u6548\u80fd \u6b63\u78ba \u500b\u6578 \u932f\u8aa4 \u500b\u6578 \u6e96\u78ba\u7387 \u4f7f\u7528\u7684 \u5206\u985e\u5668 \u6b63\u78ba \u500b\u6578 \u589e\u6e1b \u932f\u8aa4 \u500b\u6578 \u6e96\u78ba\u7387 \u5716\u4e03\u3001\u56db\u7a2e\u65b9\u6cd5\u6548\u80fd\u6bd4\u8f03 \u5dee\u7570\u3002\u5728 Ku & Chen [2]\u7684\u7814\u7a76\u4e2d\uff0c\u8058\u8acb\u6a19\u8a18\u8005\u5c0d\u820a\u7248 NTUSD \u9032\u884c\u6a19\u8a18\u3002\u820a\u7248 NTUSD \u70ba\u7d93\u904e\u7ffb\u8b6f\u7684 General Inquirer(GI)\u8207 Chinese Network Sentiment Dictionary(CNSD) \u7684\u7d44\u5408\uff0c\u6bcf\u500b\u8a5e\u5f59\u90fd\u6709\u4eba\u5de5\u7684\u610f\u898b\u6a19\u8a18\u3002\u8a72\u7814\u7a76\u4e2d\u6a19\u8a18\u8005\u7684\u6700\u4f73\u6a19\u8a18\u6548\u80fd\u8207\u672c\u7814\u7a76\u7684\u6bd4 \u8f03\u5982\u8868\u516d\uff0c\u5f9e\u8868\u516d\u4e2d\u53ef\u4ee5\u770b\u51fa\uff0c\u672c\u7814\u7a76\u6240\u7522\u751f\u7684\u81ea\u52d5\u6a19\u8a18\u6f14\u7b97\u6cd5\u9054\u5230\u4e86\u63a5\u8fd1\u4eba\u985e\u6a19\u8a18\u7684 \u6548\u80fd\u3002 \u8868\u516d\u3001NTUSD \u6a19\u8a18\u8005\u8207\u672c\u7814\u7a76\u6a19\u8a18\u6548\u80fd\u6bd4\u8f03 \u5c6c\u6027\uff0c\u9019\u5305\u62ec\u5c0d\u8a5e\u5f59\u6a19\u8a18\u4e3b\u5ba2\u89c0\u7684\u5c6c\u6027\u53ca\u6b63\u8ca0\u9762\u50be\u5411\u7684\u5f37\u5ea6\u7b49\u3002\u9664\u6b64\u4e4b\u5916\uff0c\u56e0\u70ba E-HowNet \u8a5e\u5f59\u6709\u8a31\u591a\u4e0d\u540c\u7684\u8a5e\u6027\uff0c\u6211\u5011\u4e5f\u5e0c\u671b\u80fd\u628a\u6211\u5011\u7684\u65b9\u6cd5\uff0c\u904b\u7528\u8a5e\u6027\u7684\u5c64\u6b21\u4f86\u9032 \u884c\u6a19\u8a18\u3002\u85c9\u7531\u63d0\u4f9b\u66f4\u7cbe\u78ba\u7684\u5b57\u5f59\u610f\u898b\u6a19\u8a18\u8cc7\u8a0a\uff0c\u4f86\u652f\u63f4\u53e5\u5b50\u53ca\u6587\u4ef6\u5c64\u6b21\u7684\u610f\u898b\u5206\u6790\u3002 \u81f4\u8b1d (Prime-\u5169\u500b\u6700\u5927\u503c(\u03b1 = \u22120.02)\u7684\u5dee\u8ddd\u50c5 0.1724%\uff0c\u6b64\u5dee\u8ddd\u70ba\u4e0d\u986f\u8457\uff0c\u6aa2\u5b9a\u7d50\u679c (1.50, 0.22)\u3002 \u6211\u5011\u5728\u8868\u56db\u4e2d\u9078\u4e0d\u540c\u8a5e\u6027\u505a\u5f97\u6700\u597d\u7684\u5206\u985e\u5668\u4f86\u7d44\u5408\uff0c\u5982\u679c\u6548\u80fd\u76f8\u540c\uff0c\u5247\u9078\u7279\u5fb5\u6578\u91cf\u8f03\u5c11 \u7531\u65bc\u6211\u5011\u4f7f\u7528 NTUSD\uff0c\u6211\u5011\u60f3\u770b\u770b NTUSD \u4eba\u985e\u6a19\u8a18\u7684\u6548\u80fd\u8ddf\u6211\u5011\u5206\u985e\u5668\u7684\u6548\u80fd\u6709\u4f55 \u7684\u90a3\u4e00\u500b\u5206\u985e\u5668\uff0c\u56e0\u70ba\u7279\u5fb5\u6578\u8f03\u5c11\u901a\u5e38\u5728\u672a\u770b\u904e\u7684\u8cc7\u6599\u96c6\u6703\u505a\u5f97\u8f03\u597d\u3002\u7d44\u5408\u51fa\u7684\u5206\u985e\u5668 \u53c3\u8003\u6587\u737b</td></tr><tr><td>\u6548\u80fd\u8ddf\u5176\u4ed6\u5169\u8005\u7684\u5dee\u8ddd\u70ba\u986f\u8457\uff0c\u6aa2\u5b9a\u7d50\u679c (4.61, 0.03)\u3002 \u4e2d\u7684\u7279\u5fb5\u96c6\u4ee3\u865f\u662f\u300c\u8a9e\u7bc7\u7279\u5fb5\u96c6\u4ee3\u78bc+PBFN \u03b1 = \u22120.03 \u300d\u7684\u7c21\u5beb\uff0c\u56e0\u70ba\u4f7f\u7528\u76f8\u540c\u7684 PBFN \u03b1 = \u5176\u4ed6 9 1 90.0000% F5000-2 9 (+0) 1 90.0000% \u8a9e\u7bc7\u7279\u5fb5\u4f7f\u7528\u5341\u7d44\u7279\u5fb5\u96c6\u7684\u540d\u7a31\uff0c\u4ee5\u53ca\u7279\u5fb5\u6578\u91cf\uff0c\u5982\u8868\u4e09\u6240\u793a\u3002\u5728\u8868\u4e2d\uff0c\u6211\u5011\u4f7f\u7528\u7279\u5fb5 \u22120.03 \uff0c\u6240\u4ee5\u5c07\u5176\u5ffd\u7565\u3002 \u300c\u7e3d\u9ad4\u6548\u80fd\u300d\u662f\u6307\u5206\u985e\u5668\u8a13\u7df4\u6642\u7684\u6574\u9ad4\u6548\u80fd\u3002\u8868\u4e2d\uff0c\u4e00\u6b04\u4e2d\u6700\u4f73\u7684 \u7e3d\u6578 2,142 178 92.3276% 2142 (+0) 178 92.3276%</td></tr></table>", | |
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