| { | |
| "paper_id": "O12-3002", | |
| "header": { | |
| "generated_with": "S2ORC 1.0.0", | |
| "date_generated": "2023-01-19T08:03:12.711970Z" | |
| }, | |
| "title": "Predicting the Semantic Orientation of Terms in E-HowNet", | |
| "authors": [ | |
| { | |
| "first": "", | |
| "middle": [], | |
| "last": "\uf9e1\u653f\u5112", | |
| "suffix": "", | |
| "affiliation": { | |
| "laboratory": "", | |
| "institution": "National Taiwan University", | |
| "location": {} | |
| }, | |
| "email": "" | |
| }, | |
| { | |
| "first": "Ru", | |
| "middle": [], | |
| "last": "Li", | |
| "suffix": "", | |
| "affiliation": { | |
| "laboratory": "", | |
| "institution": "National Taiwan University", | |
| "location": {} | |
| }, | |
| "email": "" | |
| }, | |
| { | |
| "first": "Chi-Hsin", | |
| "middle": [], | |
| "last": "Yu", | |
| "suffix": "", | |
| "affiliation": { | |
| "laboratory": "", | |
| "institution": "National Taiwan University", | |
| "location": {} | |
| }, | |
| "email": "" | |
| }, | |
| { | |
| "first": "Hsin-Hsi", | |
| "middle": [], | |
| "last": "Chen", | |
| "suffix": "", | |
| "affiliation": { | |
| "laboratory": "", | |
| "institution": "National Taiwan University", | |
| "location": {} | |
| }, | |
| "email": "hhchen@ntu.edu.tw" | |
| } | |
| ], | |
| "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 results showed that the proposed approach can achieve 92.33% accuracy.", | |
| "pdf_parse": { | |
| "paper_id": "O12-3002", | |
| "_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 results showed that the proposed approach can achieve 92.33% accuracy.", | |
| "cite_spans": [], | |
| "ref_spans": [], | |
| "eq_spans": [], | |
| "section": "Abstract", | |
| "sec_num": null | |
| } | |
| ], | |
| "body_text": [ | |
| { | |
| "text": "\u60c5\u7dd2\u5206\u6790(Sentiment Analysis)\u5728\u73fe\u4eca\u7684\u7db2\uf937\u4e16\u754c\u4e2d\uff0c\u6709\u8a31\u591a\u5be6\u969b\u4e14\u91cd\u8981\u7684\u904b\u7528\uff0c\uf9b5\u5982 \u5f9e\u7db2\uf937\u7684\u8a55\uf941\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 \u7b49\u3002\uf967\u7ba1\u5c0d\uf906\u5b50\u6216\u6587\u4ef6\u5c64\u6b21\u7684\u60c5\u7dd2\u5206\u6790\uff0c\u610f\ufa0a\u8a5e\u8a5e\u5178\u90fd\u662f\u4e00\u500b\u91cd\u8981\u7684\u8cc7\u6e90\u3002\u901a\u5e38\u610f\ufa0a\u8a5e \u8a5e\u5178\u662f\u7528\u4eba\u5de5\uf92d\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 or objective)\u3001\u6975\u6027(orientation/polarity)\u3001)\u53ca\u6975\u6027\u7684\u5f37\ufa01(strength) (Esuli & Sebastiani, 2005) \u3002\u9019\u4e9b\u60c5\u7dd2\u5c6c\u6027\u5c0d\uf967\u540c\u7684\u61c9\u7528\u6709\uf967\u540c\u7684\u91cd\u8981\u6027\uff0c\u6a19\u8a18\u96e3\ufa01\u4e5f\u5404\uf967\u76f8\u540c\uff0c\u901a\u5e38\u8a5e\u5f59\u7684 \u6975\u6027\u662f\u6700\u5bb9\uf9e0\u9032\ufa08\u6a19\u8a18\u7684\u5c6c\u6027\u3002 \u6a19\u8a18\u60c5\u7dd2\u5c6c\u6027\u6642\uff0c\u7814\u7a76\u8005\u53ef\u4ee5\u5f9e\uf9b2\u958b\u59cb\u6536\u96c6\u8a5e\u5f59\u4ee5\u5efa\uf9f7\u610f\ufa0a\u8a5e\u8a5e\u5178\uff0c\u5982\u53f0\u5927\u610f\ufa0a\u8a5e \u8a5e\u5178 NTUSD (Ku & Chen, 2007) \u3002\u5728\u53e6\u4e00\u65b9\u9762\uff0c\u4e5f\u6709\u7814\u7a76\u8005\u5617\u8a66\u70ba\u81ea\u7136\u8a9e\u8a00\u8655\uf9e4\u4e2d\u7684\u8a31 \u591a\u73fe\u5b58\u7684\u8cc7\u6e90\uff0c\u6dfb\u52a0\u60c5\u7dd2\u5c6c\u6027\uff0c\u5982 SentiWordNet (Esuli & Sebastiani, 2006a) (Esuli & Sebastiani, 2006b; Kamps, Marx, Mokken, & De Rijke, 2004; Turney & Littman, 2003) Yuen et al.(2004) (Han, Mo, Zuo, & Duan, 2010; Li, Ma, & Guo, 2009; Lu, Song, Zhang, & Tsou, 2010; Yao, Wu, Liu, & Zheng, 2006) (Dietterich, 1998) ", | |
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| { | |
| "start": 230, | |
| "end": 256, | |
| "text": "(Esuli & Sebastiani, 2005)", | |
| "ref_id": "BIBREF4" | |
| }, | |
| { | |
| "start": 355, | |
| "end": 372, | |
| "text": "(Ku & Chen, 2007)", | |
| "ref_id": "BIBREF11" | |
| }, | |
| { | |
| "start": 427, | |
| "end": 454, | |
| "text": "(Esuli & Sebastiani, 2006a)", | |
| "ref_id": "BIBREF5" | |
| }, | |
| { | |
| "start": 455, | |
| "end": 482, | |
| "text": "(Esuli & Sebastiani, 2006b;", | |
| "ref_id": "BIBREF6" | |
| }, | |
| { | |
| "start": 483, | |
| "end": 521, | |
| "text": "Kamps, Marx, Mokken, & De Rijke, 2004;", | |
| "ref_id": "BIBREF10" | |
| }, | |
| { | |
| "start": 522, | |
| "end": 545, | |
| "text": "Turney & Littman, 2003)", | |
| "ref_id": "BIBREF15" | |
| }, | |
| { | |
| "start": 546, | |
| "end": 563, | |
| "text": "Yuen et al.(2004)", | |
| "ref_id": null | |
| }, | |
| { | |
| "start": 564, | |
| "end": 592, | |
| "text": "(Han, Mo, Zuo, & Duan, 2010;", | |
| "ref_id": "BIBREF7" | |
| }, | |
| { | |
| "start": 593, | |
| "end": 613, | |
| "text": "Li, Ma, & Guo, 2009;", | |
| "ref_id": "BIBREF12" | |
| }, | |
| { | |
| "start": 614, | |
| "end": 644, | |
| "text": "Lu, Song, Zhang, & Tsou, 2010;", | |
| "ref_id": "BIBREF14" | |
| }, | |
| { | |
| "start": 645, | |
| "end": 673, | |
| "text": "Yao, Wu, Liu, & Zheng, 2006)", | |
| "ref_id": "BIBREF16" | |
| }, | |
| { | |
| "start": 674, | |
| "end": 692, | |
| "text": "(Dietterich, 1998)", | |
| "ref_id": "BIBREF2" | |
| } | |
| ], | |
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| "section": "\u7dd2\uf941", | |
| "sec_num": "1." | |
| }, | |
| { | |
| "text": "\uff0c\u6548\u80fd\u5f9e 67%\u5230 88%\uf967\u7b49\uff0c\u4f46\u56e0\u70ba\u9019\u4e9b\u6f14\u7b97\u6cd5\u6240\u7528\u7684\u8cc7\uf9be\u96c6\u4e26\uf967 \u76f8\u540c\uff0c\u5be6\u9a57\u904e\u7a0b\u53ca\u8a55\u4f30\u6a19\u6e96\u4e5f\uf967\u4e00\u6a23\uff0c(\u6709\u7528 Accuracy\u3001Precision\u3001\u6216 F-Measure)\uff0c\u6240 \u4ee5\u6548\u80fd\u6c92\u6709\u8fa6\u6cd5\u76f4\u63a5\u6bd4\u8f03\u3002 \u5716 1.\u300c\u6c7d\u6cb9\u300d\u7684\u5ee3\u7fa9\u77e5\u7db2\u5b9a\u7fa9\u5f0f \u5728 \u4e2d \u6587 \u7684 \u60c5 \u7dd2 \u5c6c \u6027 \u6a19 \u8a18 \u76f8 \u95dc \u7814 \u7a76 \uff0c", | |
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| "section": "\u7dd2\uf941", | |
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| { | |
| "text": "\uff0c\u6240\u5f97\u5230\u7684\u6548\u80fd\u5728\uf967\u540c\u7684\u6307\u6a19(Accuracy\u3001Precision\u3001\u6216 F-Measure)\u4e0b\uff0c \u5f9e 89%\u5230 96%\uf967\u7b49\u3002\u56e0\u70ba\u57fa\u6e96\uf967\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 3. \u7279\u5fb5\u62bd\u53d6\u53ca\u6a5f\u5668\u5b78\u7fd2\u6f14\u7b97\u6cd5 \u7531\u65bc\u6211\u5011\u904b\u7528\u76e3\u7763\u5f0f\u6a5f\u5668\u5b78\u7fd2\u6f14\u7b97\u6cd5\uf92d\u8a13\uf996\u4e8c\u5143\u5206\uf9d0\u5668(binary classifier)\uff0c\u6700\u91cd\u8981\u7684 \u554f\u984c\u662f\u70ba\u8a5e\u5f59\u62bd\u53d6\u51fa\u6709\u7528\u7684\u7279\u5fb5\u3002\u5728\u6b64\uf941\u6587\u4e2d\uff0c\u6211\u5011\u5206\u5225\u5f9e E-HowNet \u53ca Google Chinese Web 5-gram \u9019\uf978\u500b\uf92d\u6e90\u62bd\u53d6\uf978\u5927\uf9d0\u7684\u7279\u5fb5\uff0c\u63a5\u8457\u5c07\u9019\uf978\u500b\uf92d\u6e90\u7684\u7279\u5fb5\u7d44\u5408\u8a13\uf996\u5206\uf9d0\u5668\u3002 \u6b64\u5916\uff0c\u6211\u5011\u4e5f\u5617\u8a66\u4f7f\u7528\u7d44\u5408\u5f0f\u7684\u76e3\u7763\u5f0f\u6a5f\u5668\u5b78\u7fd2\u6f14\u7b97\u6cd5(ensemble approach)\uff0c\uf92d\uf901\u9032 \u4e00\u6b65\u5f97\u5230\uf901\u9ad8\u7684\u6548\u80fd\uff0c\u4ee5\u4e0b\u6211\u5011\u5206\u5225\u8a73\u7d30\u4ecb\u7d39\u3002 \uf9e1\u653f\u5112 \u7b49 3.1 \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\uf97e V i = (w i,j ) = (w i,1 , w i,2 , \u2026, w i,n ) \u8868\u793a\uff0c\u5176\u4e2d n \u70ba\u5411\uf97e\u7684\u7dad\ufa01\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\uf92d\u9032\ufa08\u5b9a\u7fa9\uff0c\u516c\u5f0f (1) \u5b9a\u7fa9 V i \u4e2d\u6bcf\u500b\u7279\u5fb5\u7684\u6b0a\u91cd\uff1a \u23a9 \u23a8 \u23a7 = j \u7fa9\u539f , 0 j \u7fa9\u539f \u4e2d i \u5b9a\u7fa9\u5f0f , 1 , \uf967\u51fa\u73fe \u51fa\u73fe \u5982\u679c j i w (1) \u4ee5\u5716 1\u300c\u6c7d\u6cb9\u300d\u9019\u500b\u8a5e\u5f59\u70ba\uf9b5\uff0c\u5176\u5b9a\u7fa9\u5f0f\u4e2d\u51fa\u73fe\uf9ba\u7fa9\u539f material\uff0c\u6240\u4ee5\u5b83\u7684\u503c w \u6c7d\u6cb9 , material \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\uf9ba 2567 \u500b\u7fa9\u539f\uf92d\u7576\u7279\u5fb5\u3002 \u5ee3\u7fa9\u77e5\u7db2\u7684\u8a5e\u5f59\u6709\u6b67\uf962\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\u51fa\u73fe\u983b\uf961\u6700\u9ad8\u7684\u8a9e\u610f(\u9664\uf9ba\u56db\u500b\u8a5e\u5f59\uf9b5\u5916)\uff0c\u6240\u4ee5\u6211\u5011\u7528\u8a5e\u5f59\u7684\u7b2c\u4e00\u500b\u8a9e\u610f\uf92d\u62bd\u53d6\u7279 \u5fb5\u3002\u53ea\u5f9e\u8a5e\u5f59\u7684\u4e00\u500b\u8a9e\u610f\u62bd\u53d6\u7279\u5fb5\uff0c\u800c\uf967\u628a\u8a72\u8a5e\u5f59\u6240\u6709\u7684\u8a9e\u610f\u653e\u5728\u4e00\u8d77\uff0c\u4ee3\u8868\u9019\u7a2e\u65b9\u6cd5 \u53ef\u70ba\uf967\u540c\u7684\u8a9e\u610f\u7d66\u51fa\uf967\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\u4ee5\u7121\u6cd5\u5c0d\u8a9e\u610f\u7684\u5c64\u7d1a\u9032\ufa08\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\u4e0a\u5957\u7528\u3002 3.1.1 \u57fa\u790e\u7fa9\u539f\u7279\u5fb5\u52a0\u6b0a\u503c \u9664\uf9ba\u516c\u5f0f (1) \u7684\u65b9\u5f0f\u5916\uff0c\u6211\u5011\u53ef\u4ee5\uf9dd\u7528\uf901\u591a E-HowNet \u7684\u7279\u6027\uff0c\uf92d\u62bd\u53d6\u51fa\u6709\u7528\u7684\u8cc7\u8a0a\u3002 \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 2 \u7684\u6a39\uf9fa\u7d50\u69cb\u3002\u5728 \u9019\u6a39\uf9fa\u7d50\u69cb\u4e2d\uff0c\u7fa9\u539f\u6240\u5728\u7684\u6df1\ufa01\u662f\u4e00\u500b\u6709\u7528\u7684\u8cc7\u8a0a\uff0c\u56e0\u6b64\u6211\u5011\u4eff\u7167\uf9c7\u7fa4&\uf9e1\u7d20\u5efa(\u5218 & \uf9e1, 2002)\u7684\u516c\u5f0f\uff0c\u5c07\u6df1\ufa01\u7684\u8cc7\u8a0a\u7576\u4f5c\u6b0a\u91cd\u5f15\u5165\u516c\u5f0f (1)\uff0c\u5f97\u5230\u516c\u5f0f (2)\u3002 \u5716 2.\u300c\u5929\uf9d4\u4e4b\uf914\u300d\u5b9a\u7fa9\u5f0f\u7684\u6a39\uf9fa\u8868\u793a \u5ee3\u7fa9\u77e5\u7db2\u8a5e\u5f59\u610f\ufa0a\u6975\u6027\u7684\u9810\u6e2c 25 \u23aa \u23a9 \u23aa \u23a8 \u23a7 \u00d7 + = j \u7fa9\u539f , 0 j \u7fa9\u539f \u4e2d i \u5b9a\u7fa9\u5f0f , 1 1 , , \uf967\u51fa\u73fe \u51fa\u73fe \u5982\u679c j i j i d w \u03b1 (2) \u516c\u5f0f (2) \u4e2d\uff0c\u03b1 \u662f\u53ef\u8abf\u7684\uf96b\uf969\uff0c j i d , \u662f\u8a5e\u5f59 i \u8ddf\u7fa9\u539f j \u7684\u8ddd\uf9ea\uff0c\u9019\u53ef\u7528\u7fa9\u539f j \u7684\u6df1\ufa01\u8868 \u793a\u3002\u8abf\u6574\u516c\u5f0f (2) \u4e2d\u7684 \u03b1 \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) \u03b1 < 0 : \u6df1\ufa01\u8d8a\u6df1\uff0c\u8868\u793a\u8a72\u7fa9\u539f\u6709\u8f03\u591a\u8cc7\u8a0a\uff0c\u61c9\u7d66\u8f03\u9ad8\u6b0a\u91cd\u3002 (\u53ef\u80fd\u4e8c) \u03b1 > 0 : \u6df1\ufa01\u8d8a\u6df1\uff0c\u8868\u793a\u8a72\u7fa9\u539f\u6709\u8f03\u5c11\u8cc7\u8a0a\uff0c\u61c9\u7d66\u8f03\u5c11\u6b0a\u91cd\u3002 \u7531\u65bc \u03b1 < 0 \u6642\uff0cw i,j \u53ef\u80fd\u8b8a\u70ba\u8ca0\u503c\uff0c\u6240\u4ee5\u6700\u5c0f\u7684 \u03b1 \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 3.1.2 \u52a0\u5165\u5426\u5b9a\u95dc\u4fc2\u8abf\u6574\u7279\u5fb5\u7684\u52a0\u6b0a\u503c \u5728\u8a08\u7b97\u7fa9\u539f\u6df1\ufa01\u6642\uff0c\u53ef\u80fd\u6703\u7d93\u904e\u5e36\u6709\u5426\u5b9a\u610f\u7fa9\u7684\u95dc\u4fc2\uff0c\uf9b5\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\uf92d\u8868\u793a\u53ef\u80fd\u6703\uf901\u597d\uff0c\u56e0\u6b64\u6211\u5011\u5c07\u5426\u5b9a\u7684\u6982\uf9a3\u5f15\u5165\u516c\u5f0f (3) \u5982\u4e0b\uff1a \u23aa \u23a9 \u23aa \u23a8 \u23a7 \u00d7 + = j \u7fa9\u539f , 0 j \u7fa9\u539f \u4e2d i \u5b9a\u7fa9\u5f0f , 1 , , , \uf967\u51fa\u73fe \u51fa\u73fe \u5982\u679c j i j i j i d Neg w \u03b1 (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\uf974\u6709\u5247 j i Neg , \u70ba \u2212 1\uff0c \uf974\u7121\u5247 j i Neg , \u70ba", | |
| "cite_spans": [], | |
| "ref_spans": [], | |
| "eq_spans": [], | |
| "section": "\u7dd2\uf941", | |
| "sec_num": "1." | |
| }, | |
| { | |
| "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": "\u7dd2\uf941", | |
| "sec_num": "1." | |
| }, | |
| { | |
| "text": "\u7528 V i = (c i,1 , c i,2 ,\u2026, c i,m ) \u7684\u65b9\u5f0f\uf92d\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\uf9e4\u624d\u6703\u6709\u6bd4\u8f03\u597d\u7684\u7d50\u679c\u3002 \u6211 \u5011 \u5be6 \u9a57 \uf9ba \uf978 \u500b \uf967 \u540c \u7684 \u65b9 \u6cd5 \uf92d \u8655 \uf9e4 \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\uf97e V i \u7528\u516c\u5f0f (4) \u8655\uf9e4\uff1b\u7b2c\u4e8c\u7a2e\u662f Esuli & Sebastiani (2005) \u6240\u63d0\u7684\u9918\u5f26\u6a19\u6e96\u5316 TFIDF (cosine-normalized TF-IDF)\uff0c\u4ed6\u5011\u7528\u8a72\u65b9\u6cd5\uf92d\u8655\uf9e4 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 \u211c \u2208 = \u2211 \u2264 \u2264 1 2 , ) ( (4) m m k k i i i tfidf TFIDF TFIDF CosNorm \u211c \u2208 = \u2211 \u2264 \u2264 1 2 , ) ( ) ,..., , ( , 2 , 1 , m i i i i tfidf tfidf tfidf TFIDF = j j i j i idf tf tfidf * , , = \u2211 \u2208 = = D k j k j i j i j i c c j c tf , , , , \u7e3d\u51fa\u73fe\u6b21\uf969 \u7279\u5fb5 } , 0 : { log ) log( , 1 D i c i D df idf j i j j \u2208 \u2200 > = = \u2212 (5) \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\uf97e\u7b49\u9577\uff0c\u6d88\u6389\u6b21\uf969\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\ufa08\u6a19\u6e96\u5316(normalization)\uff0c\u6240\u4ee5 tf i , j \u6703 \u9664\u4ee5\u7279\u5fb5 j \u7684\u7e3d\u51fa\u73fe\u6b21\uf969\uff0c\u53e6\u5916\u518d\u8003\u616e\u7279\u5fb5 j \u7684\u7a00\u6709\ufa01\uff0c\u6240\u4ee5\u4e58\u4e0a idf j \uff0c\u6700\u5f8c\u518d\u8b93\u6240 \u6709\u8a5e\u5f59\u7684\u5411\uf97e\u7b49\u9577\u3002\u6211\u5011\u6703\u5728\u5f8c\u9762\u7684\u5be6\u9a57\u4e2d\uff0c\u6bd4\u8f03\u9019\uf978\u7a2e\uf967\u540c\u6b0a\u91cd\u8655\uf9e4\u65b9\u5f0f\u7684\u6548\u80fd\u3002 3.3 \uf967\u540c\u7279\u5fb5\u7684\u7d44\u5408 \u6211\u5011\u7528\uf9ba\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 ) \uf92d \u8868\u793a\u8a5e\u5f59 i\u3002\u5982\u679c\u60f3\u540c\u6642\u4f7f\u7528\u9019\uf978\u7a2e\u7279\u5fb5\u4e2d\u7684\u8cc7\u8a0a\uff0c\u4e00\u7a2e\u76f4\u89c0\u7684\u65b9\u5f0f\uff0c\u662f\u5c07\uf978\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 ) \uf92d\u8868\u793a\u3002\u7531\u65bc\u57fa\u790e\u7fa9\u539f\u7279\u5fb5\u53ca\u8a9e\u7bc7 \u7279\u5fb5\u90fd\u6709\u8a31\u591a\uf967\u540c\u7684\u8b8a\u5f62\uff0c\u6211\u5011\u7121\u6cd5\u4e00\u4e00\u5617\u8a66\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 )", | |
| "cite_spans": [], | |
| "ref_spans": [], | |
| "eq_spans": [], | |
| "section": "\u7dd2\uf941", | |
| "sec_num": "1." | |
| }, | |
| { | |
| "text": "\uf92d\u6e2c\u8a66\u5206\uf9d0\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\u8a66\u8cc7\uf9be\u4f9d\u7167\uf978\u500b\u5206\uf9d0\u5668 (\u4ee5\u4e0b\u7a31\u70ba\u5206\uf9d0\u5668 A \u8207\u5206\uf9d0\u5668 B) \u7684\u6a19\u8a18\uff0c \u5206\u6210\u56db\u7d44\u4e26\u8a08\uf969\u3002\u5176\u4e2d\u6e2c\u8a66\u6a23\u672c\uf969\u5373\u70ba\u4e0b\u9762 n 1,1 \u3001n 0,1 \u3001n 1,0 \u3001n 0,0 \u56db\u500b\uf969\u5b57\u7684\u7e3d\u5408\uff0c\u5728\u865b \u7121\u5047\u8a2d(null hypothesis)\u4e2d\uff0c\uf978\u500b\u5206\uf9d0\u5668\u61c9\u5177\u6709\u76f8\u540c\u7684\u932f\u8aa4\uf961\uff0c\u4e5f\u5c31\u662f n 0,1 =n 1,0 \u3002 n 1,1 \uff1a \u5206\uf9d0\u5668 A \u8207\u5206\uf9d0\u5668 B \u7686\u6b63\u78ba\u6a19\u8a18 \u7684\u6a23\u672c\uf969 n 0,1 \uff1a \u5206\uf9d0\u5668 A \u6a19\u8a18\u932f\u8aa4\uff0c\u4f46\u5206\uf9d0\u5668 B \u6a19\u8a18\u6b63\u78ba\u7684\u6a23\u672c\uf969 n 1,0 \uff1a \u5206\uf9d0\u5668 B \u6a19\u8a18\u932f\u8aa4\uff0c\u4f46\u5206\uf9d0\u5668 A \u6a19\u8a18\u6b63\u78ba\u7684\u6a23\u672c\uf969 n 0,0 \uff1a \u5206\uf9d0\u5668 A \u8207\u5206\uf9d0\u5668 B \u7686\u932f\u8aa4\u6a19\u8a18", | |
| "cite_spans": [], | |
| "ref_spans": [], | |
| "eq_spans": [], | |
| "section": "\u7dd2\uf941", | |
| "sec_num": "1." | |
| }, | |
| { | |
| "text": "http://ehownet.iis.sinica.edu.tw/", | |
| "cite_spans": [], | |
| "ref_spans": [], | |
| "eq_spans": [], | |
| "section": "", | |
| "sec_num": null | |
| } | |
| ], | |
| "back_matter": [], | |
| "bib_entries": { | |
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| "TABREF0": { | |
| "content": "<table><tr><td>(Extended-HowNet Ontology)\uff0c\u4e26\u7528\u9019\u4e9b\u65b0\u7684\u8a9e\u7fa9\u7fa9\u539f\uff0c\u4ee5\u7d50\u69cb\u5316\u7684\u5f62\u5f0f\uf92d\u5b9a\u7fa9\u8a5e\u689d\uff0c</td></tr><tr><td>\u8a5e\u689d\u5b9a\u7fa9\u5f0f\u7684\uf9b5\u5b50\u5982\u5716 1\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\uf92d\u8a0e\uf941\u3002\u5728\u82f1\u6587\u65b9\u9762\uff0c\u6700\u65e9\u662f\u7531</td></tr><tr><td>Hatzivassiloglou & McKeown(1997) \u5728 1997 \uf98e\u91dd\u5c0d\u5f62\u5bb9\u8a5e\u6240\u505a\u7684\u7814\u7a76\uff0c\u4ed6\u5011\u6240\u7528\u7684\u5f62\u5bb9</td></tr><tr><td>\u8a5e\u5206\u5225\u6709\u6b63\u9762\u8a5e 657 \u500b\u53ca\u8ca0\u9762\u8a5e 679 \u500b\uff0c\u8a72\uf941\u6587\u4f9d\u64da\uf967\u540c\u7684\u5be6\u9a57\u8a2d\u5b9a\uff0c\u76e3\u7763\u5f0f\u6a5f\u5668\u5b78\u7fd2</td></tr><tr><td>\u7684\u6e96\u78ba\uf961(Accuracy)\u7531 82% \u5230 90%\u3002\u4e4b\u5f8c\uf9d3\u7e8c\u6709\uf967\u540c\u7684\u7814\u7a76\uff0c\u6240\u7528\u591a\u70ba\u534a\u76e3\u7763\u5f0f\u6a5f</td></tr><tr><td>\u5668\u5b78\u7fd2\u7684\u6f14\u7b97\u6cd5</td></tr><tr><td>\u8981\u5168\u90e8\u7528\u4eba\u5de5\u9032\ufa08\u6a19\u8a18\u4e4b\u6210\u672c\u592a\u9ad8\u3002\u56e0\u6b64\uff0c\u901a\u5e38\u7684\u4f5c\u6cd5\u662f\u5c11\uf97e\u6a19\u8a18\u4e00\u4e9b\u8a5e\u5f59\uff0c\u518d\u7528\u6a5f\u5668</td></tr><tr><td>\u5b78\u7fd2\u65b9\u6cd5\uff0c\u70ba\u5269\u4e0b\u7684\u8a5e\u5f59\u9032\ufa08\u81ea\u52d5\u6a19\u8a18\uff0c\u96d6\u7136\u81ea\u52d5\u6a19\u8a18\u7684\u6e96\u78ba\uf961\uf967\u5982\u4eba\u5de5\u6a19\u8a18\uff0c\u4f46\u5c0d\u4e00</td></tr><tr><td>\u822c\u61c9\u7528\u6709\u67d0\u7a2e\u7a0b\ufa01\u7684\u5e6b\u52a9\u3002</td></tr><tr><td>\u5728\u4e2d\u6587\u81ea\u7136\u8a9e\u8a00\u8655\uf9e4\uff0cNTUSD \u662f\u4e00\u90e8\u91cd\u8981\u7684\u610f\ufa0a\u8a5e\u8a5e\u5178\uff0c\u4f46\u6b64\u8a5e\u5178\u53ea\u5305\u62ec\u8a5e\u5f59\u53ca</td></tr><tr><td>\u6975\u6027\u7684\u8cc7\u8a0a\u3002\u53e6\u4e00\u65b9\u9762\uff0c\u8463\u632f\u6771\u5148\u751f\u548c\u9673\u514b\u5065\u6559\u6388\u6240\u5efa\uf9f7\u7684\u77e5\u7db2\u548c\u5ee3\u7fa9\u77e5\u7db2(Z. Dong &</td></tr><tr><td>Dong, 2006; \u9673\u514b\u5065, \u9ec3, \u65bd, & \u9673, 2004)\uff0c\u662f\u91cd\u8981\u7684\u8a9e\u610f\u8cc7\u6e90\u3002\u5c0d\u65bc\u6bcf\u500b\u8a5e\u5f59\uff0c\u90fd\u7528\u6709</td></tr><tr><td>\u9650\u7684\u7fa9\u539f\u7d66\u4e88\u7cbe\u78ba\u7684\u5b9a\u7fa9\uff0c\u4f46\u9019\u4e9b\u5b9a\u7fa9\u537b\u7f3a\u4e4f\u60c5\u7dd2\u7684\u8a9e\u610f\u6a19\u8a18\u3002\u56e0\u6b64\uff0c\u5982\u4f55\u81ea\u52d5\u70ba\u5ee3\u7fa9</td></tr><tr><td>\u77e5\u7db2\u52a0\u4e0a\u60c5\u7dd2\u6a19\u8a18\uff0c\u6210\u70ba\u4e00\u500b\u91cd\u8981\u7684\u8ab2\u984c\uff0c\u4e5f\u662f\u672c\u7814\u7a76\u7684\u76ee\u7684\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\uf9dd\u7528 NTUSD \u8ddf\u5ee3\u7fa9\u77e5\u7db2\u8a5e\u5f59\u7684</td></tr><tr><td>\u4ea4\u96c6\u5efa\uf9f7\u6a19\u6e96\u7b54\u6848\u96c6\uff0c\u518d\u7531\u6a19\u6e96\u7b54\u6848\u96c6\u8a13\uf996\u51fa\u5206\uf9d0\u5668\uff0c\u70ba\u5176\u4ed6\u5ee3\u7fa9\u77e5\u7db2\u8a5e\u5f59\u9032\ufa08\u6a19\u8a18\u3002</td></tr><tr><td>\u5982\u4f55\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</td></tr><tr><td>\u8b70\u984c\u3002\u5728\u6b64\u7814\u7a76\u4e2d\uff0c\u6211\u5011\u6709\u7cfb\u7d71\u7684\u5617\u8a66\u62bd\u53d6\u5404\u7a2e\uf967\u540c\u7684\u8a5e\u5f59\u7279\u5fb5\uff0c\u6700\u5f8c\u5f97\u5230\u9ad8\u6e96\u78ba\uf961\u7684</td></tr><tr><td>\u4e8c\u5143\u5206\uf9d0\u5668(binary classifiers)\u7528\u4ee5\u81ea\u52d5\u6a19\u8a18\u6b63\u8ca0\u9762\u60c5\u7dd2\u6a19\u8a18\u3002</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</td></tr><tr><td>E-HowNet \u53ca \u8ddf\u77e5\u7db2\uf99a\u7d50\uff0c\u4e26\u4f5c\uf9ba\u4e00\u4e9b\u4fee\u6539\uff0c\u6700\u5f8c\u5f62\u6210\u5ee3\u7fa9\u77e5\u7db2(Extended-HowNet, E-HowNet 1 )\u3002</td></tr><tr><td>\u8a5e\u5eab\u5c0f\u7d44\u4fee\u6539\u4e26\u64f4\u5c55\u77e5\u7db2\u539f\u5148\u7684\u8a9e\u7fa9\u7fa9\u539f\u89d2\u8272\u77e5\uf9fc\u672c\u9ad4\uff0c\u5efa\u69cb\u51fa\u5ee3\u7fa9\u77e5\u7db2\u77e5\uf9fc\u672c\u9ad4</td></tr></table>", | |
| "num": null, | |
| "type_str": "table", | |
| "text": "\u3002\u4f46\u73fe\u6709\u8cc7\u6e90 \u7684\u8a9e\u5f59\uf97e\u901a\u5e38\u5f88\u5927\uff0c\u5982 WordNet 3.0 \u5c31\u5305\u62ec 206,941 \u500b\uf967\u540c\u7684\u82f1\u6587\u5b57\u7fa9 (word-sense pair) \uff0c 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\uf941\u6587\u7684\u6210\u679c\u3002 2. \u76f8\u95dc\u7814\u7a76 \u8463\u632f\u6771\u5148\u751f\u65bc 1998 \uf98e\u5275\u5efa\u77e5\u7db2(HowNet)\uff0c\u4e26\u5728 2003 \uf98e\uff0c\u8ddf\u4e2d\u592e\u7814\u7a76\u9662\u8cc7\u8a0a\u6240\u8a5e\u5eab\u5c0f \u7d44\u5728 2003 \uf98e\uff0c\u5c07\u4e2d\u7814\u9662\u8a5e\u5eab\u5c0f\u7d44\u8a5e\u5178(CKIP Chinese Lexical Knowledge Base)\u7684\u8a5e\u689d", | |
| "html": null | |
| }, | |
| "TABREF1": { | |
| "content": "<table><tr><td>2004 \uf98e \uf9dd \u7528 Turney &</td></tr><tr><td>Littman(2003)</td></tr></table>", | |
| "num": null, | |
| "type_str": "table", | |
| "text": "\u7684\u534a\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\uf9be\u96c6\u4e0a \u505a\u5be6\u9a57\uff0c\u5f97\u5230\u6700\u9ad8\u7684\u6210\u7e3e\u662f 80.23%\u7684\u7cbe\u78ba\ufa01\u53ca 85.03%\u7684\u53ec\u56de\uf961\u3002\u4e4b\u5f8c\u5f9e 2006 \u5230 2010 \uf98e\uff0c\uf9d3\u7e8c\u7684\u7814\u7a76\u4f7f\u7528\uf967\u540c\u7684\u8cc7\uf9be\u96c6\uff0c\u7528\uf967\u540c\uf9d0\u578b\u7684\u6a5f\u5668\u5b78\u7fd2\u6f14\u7b97\u6cd5\uf92d\u8655\uf9e4\u9019\u500b\u554f\u984c", | |
| "html": null | |
| }, | |
| "TABREF2": { | |
| "content": "<table><tr><td>3.2.1 Google Web 5-gram\u7279\u5fb5\u62bd\u53d6</td></tr><tr><td>\u6211\u5011\u4f7f\u7528\u7279\u5fb5\u8ddf\u8a5e\u5f59\u7684\u540c\u51fa\u73fe(co-occurrence)\u6b21\uf969\u505a\u70ba\u7279\u5fb5\u503c\uff0c\u4ee5\u5716 3 \u70ba\uf9b5\uff0c\u5982\u679c\u8a5e</td></tr><tr><td>+1\u3002\u53e6\u5916\uff0c\u5982\u679c\u6a39\uf9fa\u7d50\u69cb\u4e0a\u9762\u7684\u7fa9\u539f\u88ab\u5426\u5b9a\u610f\u7fa9\u7684\u95dc\u4fc2\u6240\u4fee\u98fe\uff0c\u9019\u5426\u5b9a \u5f59\u662f\u300c\u6050\u5413\u300d\uff0c\u4ee5\u300c\u975e\u6cd5\u300d\u7576\u7279\u5fb5\u503c\uff0c\u5247\u540c\u51fa\u73fe\u6b21\uf969\u6703\u5c07\u6240\u6709\u300c\u6050\u5413\u300d\u53ca\u300c\u975e\u6cd5\u300d\u4e00\u540c</td></tr><tr><td>\u610f\u7fa9\u6703\u50b3\u905e\u5230\u4e0b\u9762\u7684\u7fa9\u539f\u3002 \u51fa\u73fe\u7684 5-gram \u6b21\uf969\u76f8\u52a0\u3002\u5728\u4e0a\u9762\u7684\uf9b5\u5b50\u4e2d\uff0c\u300c\u6050\u5413\u300d\u53ca\u300c\u975e\u6cd5\u300d\u7684\u540c\u51fa\u73fe\u6b21\uf969\u70ba</td></tr><tr><td>574+200+4463 + 705=5942 \u6b21\u3002 3.2 \u8a9e\u7bc7(context)\u7279\u5fb5 \u53e6\u5916\uff0c\u7531\u65bc\u5ee3\u7fa9\u77e5\u7db2\u8ddf Google Web 5-gram \u7684\u65b7\u8a5e\u6a19\u6e96\u4e26\uf967\u4e00\u81f4\uff0c\u6240\u4ee5\u5728\u8655\uf9e4\u6642\u628a</td></tr><tr><td>\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 Google Web 5-gram \u7684\u7a7a\u767d\u53bb\u6389\uff0c\u76f4\u63a5\u627e\u51fa\u300c\u8a5e\u5f59\u300d\u8ddf\u300c\u7279\u5fb5\u300d\u9019\uf978\u5b57\uf905\u662f\u5426\u540c\u6642\u51fa\u73fe\uff0c</td></tr><tr><td>\u7121\u6cd5\u6b63\u78ba\u7372\u5f97\u8a5e\u5f59\u7684\u6975\u6027\u3002 \uf92d\u8a08\u7b97\u6b21\uf969\uff0c\u9019\u6a23\u53ef\u4ee5\u907f\u514d\u65b7\u8a5e\u6a19\u6e96\uf967\u4e00\u6240\u7522\u751f\u7684\u554f\u984c\u3002\uf9b5\u5982 \u300c\u4e00\u4e8b\u7121\u6210\u300d\u5728 Google Web</td></tr><tr><td>\u7b2c\u4e00\u500b\u7f3a\u9ede\u662f\u8a5e\u5f59\u6240\u6a19\u7684\u7fa9\u539f\uf97e\u592a\u5c11\uff0c\u56e0\u70ba\u8a5e\u5f59\u662f\u7528\u4eba\u5de5\u6a19\u793a\u7fa9\u539f\uff0c\u6240\u4ee5\u7121\u6cd5\u7d66\u4e88 5-gram \u4e2d\u88ab\u65b7\u6210\u56db\u500b\u7368\uf9f7\u7684\u8a5e\uff0c\u5c07\u7a7a\u767d\u53bb\u6389\u5c31\u53ef\u4ee5\u6b63\u78ba\u6bd4\u5c0d\u5230\u3002</td></tr><tr><td>\u5f88\u591a\u6a19\u793a\u3002\u9019\u8868\u793a\u8a5e\u5f59\u64c1\u6709\u7684\u8cc7\u8a0a\uf97e\u6709\u9650\uff0c\u6703\u9020\u6210\u5206\uf9d0\u5668\u7121\u6cd5\u6709\u6548\u5b78\u7fd2\u3002\u7b2c\u4e8c\u500b\u7f3a\u9ede\u662f \u56e0\u70ba\u9019\uf9e8\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</td></tr><tr><td>\u7fa9\u539f\uf969\uf97e\u592a\u5c11\uff0c\u9019\u6703\u9020\u6210\u8a9e\u7fa9\u7684\u5283\u5206\uf967\u5920\u7cbe\u78ba\uff0c\u7121\u6cd5\u986f\u793a\u51fa\u771f\u5be6\u7684\u8a9e\u7fa9\u5dee\u5225\uff0c\uf9b5\u5982\u300c\u660e \u4e9b\u8a5e\uff0c\u5c31\u53ef\u7b97\u51fa\u8868\u793a\u8a5e\u5f59 i \u7684\u5411\uf97e</td></tr><tr><td>\u54f2\u4fdd\u8eab\u300d\u8ddf\u300c\ufa0a\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\ufa0a\uff0c\u300c\ufa0a</td></tr><tr><td>\u98a8\u8f49\u8235\u300d\u537b\u662f\u8ca0\u9762\u610f\ufa0a\u3002\u7b2c\u4e09\u500b\u7f3a\u9ede\u662f\u5ee3\u7fa9\u77e5\u7db2\u5b9a\u7fa9\u6a19\u6e96\u7684\u5dee\uf962\uff0c\uf9b5\u5982\uff0c\u5c08\u6709\u540d\u8a5e\u5728\u5ee3</td></tr><tr><td>\u7fa9\u77e5\u7db2\u4e2d\u6703\u7528\u5ba2\u89c0\u7684\u7fa9\u539f\uf92d\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</td></tr><tr><td>(\u5982\u300c\u83ab\u672d\u7279\u300d\u662f\u5c08\u6709\u540d\u8a5e\uff0c\u4f46\u537b\u5e38\u7528\uf92d\u7576\u6b63\u9762\u610f\ufa0a)\uff0c\u9019\u7a2e\u5dee\uf962\u6703\u5f15\u5165\u7a0b\ufa01\uf967\u7b49\u7684\u96dc</td></tr><tr><td>\u8a0a\u5230\u5206\uf9d0\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\uf9b5\u5982\u300c\u4e7e\u6de8\u4fd0\uf918\u300d</td></tr><tr><td>\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</td></tr></table>", | |
| "num": null, | |
| "type_str": "table", | |
| "text": "\u6240\u5efa\uf9f7\u7684 Google Web \uf9e1\u653f\u5112 \u7b49 5-gram Version 1\uff0c\uf92d\u62bd\u53d6\u8a9e\u7bc7\u7279\u5fb5\u3002Google Web 5-gram \u662f Google \u5f9e\u7db2\uf937\u4e2d\u6536\u96c6\u5927\uf97e\u7684 \u7c21\u9ad4\u4e2d\u6587\u7db2\u9801\uff0c\u4e26\u7d93\u904e\u8655\uf9e4\u6240\u5efa\uf9f7\u7684\u8cc7\u6e90\u3002\u4ed6\u5011\u6536\u96c6\uf9ba 882,996,532,572 \u500b\u5b57\u7b26 (token) \uff0c \u5171 102,048,435,515 \u500b\uf906\u5b50\uff0c\u7d93\u904e\u65b7\u8a5e\u5f8c\u5efa\u6210 n-gram\u3002n-gram \u7684 n \u5f9e 1 \u5230 5\uff0c\u4e26\u4e14\u53ea \u4fdd\uf9cd\u983b\uf961\u5927\u65bc 40 \u7684 n-gram\u3002Google Web 5-gram \u7684\uf9b5\u5b50\u5982\u5716 3 \u6240\u793a\u3002", | |
| "html": null | |
| }, | |
| "TABREF3": { | |
| "content": "<table><tr><td>\u5ee3\u7fa9\u77e5\u7db2\u8a5e\u5f59\u610f\ufa0a\u6975\u6027\u7684\u9810\u6e2c</td><td>27</td></tr><tr><td>3.2</td><td/></tr><tr><td>NTUSD \u5b8c\u6574\u7248\u3002</td><td/></tr></table>", | |
| "num": null, | |
| "type_str": "table", | |
| "text": "i\u300d\u8ddf\u300c\u7279\u5fb5 j\u300d\u9019\uf978\u5b57\uf905\u540c\u51fa\u73fe\u7684\u6b21\uf969\u3002\u5728\u6211\u5011\u7684\u5be6\u9a57\u4e2d\uff0c\u5171\u5617\u8a66\uf9ba \u5341\u7a2e\uf967\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\ufa01\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\ufa01\u6700\u5c11\u70ba 2)\u3001\u4ee5\u53ca", | |
| "html": null | |
| }, | |
| "TABREF5": { | |
| "content": "<table><tr><td>\u7684\u6a23\u672c\uf969 McNemar \u6aa2\u5b9a\u5efa\u69cb\u5728\u5361\u65b9\u9069\u5408\ufa01\u6aa2\u5b9a(\u03c72 test goodness of fit)\u4e0a\uff0c\u6574\uf9e4\u800c\u5f97\u7684\u6aa2\u5b9a 0 , 1 1 , 0 2 0 , 1 1 , 0 ) 1 ( n n n n + \u2212 \u2212 \uff0c\u6b64\u6aa2\u5b9a\u503c\u5728 n 0,1 +n 1,0 \u5920\u5927\u7684\u6642\u5019\u6703\u8da8\u8fd1\u65bc\u81ea\u7531\ufa01\u70ba 1 \u7684\u5361\u65b9\u5206\u914d\uff0c \u56e0\u6b64\u5728\u986f\u8457\u6c34\u6e96(significant level)\u70ba 0.95 \u6642\uff0c\u6b64\u503c\uf974\u5927\u65bc \u503c\u70ba 8415 . 3 2 95 . 0 , 1 = \u03c7 \uff0c\u5247\u62d2\u7d55\u865b\u7121\u5047 \u8a2d\u3002\u6211\u5011\u7528 (McNemar \u6aa2\u5b9a\u7d50\u679c, p-value) \uf92d\u986f\u793a\u6211\u5011\u7684\u6aa2\u5b9a\u7d50\u679c\uff0c\uf9b5\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 4.2 \u57fa\u790e\u7fa9\u539f\u7279\u5fb5\u7684\u6548\u80fd 89.4% 89.6% 89.8% 90.0% Accuracy PBF PBFN \u5ee3\u7fa9\u77e5\u7db2\u8a5e\u5f59\u610f\ufa0a\u6975\u6027\u7684\u9810\u6e2c 31 \u6211\u5011\u4f7f\u7528\u4e09\u7a2e\uf967\u540c\u7684\u52a0\u6b0a\u65b9\u5f0f\u5f97\u5230\u7684\u9810\u6e2c\u6e96\u78ba\uf961\u5982\u5716 5\uff0c\u5716\u4e2d\u6211\u5011\u4e5f\u628a\u7279\u5fb5\u96c6\u7684\u7279 \u5fb5\uf969\u7531\u5de6\u81f3\u53f3\u7531\u5c0f\u5230\u5927\u6392\uf99c\u3002 \u5f9e\u5716 5 \u53ef\u4ee5\u770b\u51fa\uff0c\u6c92\u6709\u6a19\u6e96\u5316\u7684\u539f\u59cb\u983b\uf961\u7684\u6700\u4f73\u6e96\u78ba\uf961\u70ba 59.70%\uff0c\u4f7f\u7528\u7684\u7279\u5fb5\u96c6\u70ba \u300c\u5ee3\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\uf961\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\uf961\u70ba 88.23%\uff0c\u6b64\u6642\u4f7f\u7528\u7684\u7279\u5fb5\u96c6\u70ba \u300c\u5ee3\u7fa9\u77e5\u7db2\u52d5\u8a5e\u300d \uff0c \uf9e1\u653f\u5112 \u7b49 \u63d0\u5347\uff0c\u63d0\u5347\u5f8c\u7684\u6700\u9ad8\u6e96\u78ba\uf961\u70ba 92. 3276%\uff0c\u4f7f\u7528\u300c\u5ee3\u7fa9\u77e5\u7db2\u6240\u6709\u8a5e\u5f59 All+PBFN \u03b1 = \u22120.03 \u300d \u548c\u300c\u6700\u5e38\u51fa\u73fe 10000 \u8a5e(\u9577\ufa01\u22672) F10000-2+PBFN \u03b1 = \u22120.03 \u300d\u70ba\u7279\u5fb5\u96c6\u6642\u7686\u6709\u76f8\u540c\u7684\u6e96 \u78ba\uf961\u3002\u4e0a\u5716\u4e2d\uff0c\u300c\u5ee3\u7fa9\u77e5\u7db2\u6240\u6709\u8a5e\u5f59 All\u300d\u6e96\u78ba\uf961\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 95.0% \u5ee3\u7fa9\u77e5\u7db2\u8a5e\u5f59\u610f\ufa0a\u6975\u6027\u7684\u9810\u6e2c 33 \u8868 4 \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 = \u22120.03 \uff0c\u6240\u4ee5\u5c07\u5176\u5ffd\uf976\u3002\u300c\u7e3d\u9ad4\u6548\u80fd\u300d\u662f\u6307\u5206\uf9d0\u5668\u8a13\uf996\u6642\u7684\u6574\u9ad4\u6548\u80fd\u3002\u8868\u4e2d\uff0c\u4e00\uf91d \u4e2d\u6700\u4f73\u7684\u6a19\u8a18\u6548\u80fd\u4ee5\u7c97\u9ad4\u5b57\u8868\u793a\u3002 \u8868 4 \u4e2d\u6211\u5011\u53ef\u4ee5\u767c\u73fe\uff0c\u8a13\uf996\u6642\uff0cF10000-2+PBFN \u03b1 = \u22120.03 \u6709\u6700\u9ad8\u7684\u7e3d\u9ad4\u6548\u80fd\uff0c\u5176\u5404\u8a5e \u6027\u6548\u80fd\u9664\uf9ba\u5f62\u5bb9\u8a5e\u5916\uff0c\u591a\u662f\u6700\u597d\uff1b\u8003\uf97e\u5230\u8cc7\uf9be\u96c6\u4e2d\u5f62\u5bb9\u8a5e\u7684\uf969\uf97e\u4e26\uf967\u591a\uff0c\u9019\u8868\u793a\u7d44\u5408\u591a \u500b\u5206\uf9d0\u5668\u5f8c\uff0c\u6548\u80fd\u7684\u63d0\u6607\u7a7a\u9593\u53ef\u80fd\u6709\u9650\u3002\u8868 4 \u4e2d\u53e6\u4e00\u500b\u503c\u5f97\u6ce8\u610f\u7684\u4e00\u9ede\u662f\u8a13\uf996\u8cc7\uf9be\u96c6\u7684 34 \uf9e1\u653f\u5112 \u7b49 4.6 \u76f8\u95dc\u7814\u7a76\u6548\u80fd\u6bd4\u8f03 \u6211\u5011\u7e3d\u7d50\u524d\u9762\u5404\u7a2e\uf967\u540c\u7684\u5be6\u9a57\u7d50\u679c\uff0c\u756b\u6210\u5716 7\uff0c\uf92d\u65b9\uf965\u6211\u5011\u6bd4\u8f03\u6548\u80fd\u3002\u5176\u4e2d\uff0cgloss \u8868\u57fa \u790e\u7fa9\u539f\u7279\u5fb5 PBFN \u03b1 = \u22120.03 \uff0c\u6700\u597d\u7684\u6548\u80fd\u5230 92.3276%\u3002 92.3276% 92.3276% 95.0% \u5ee3\u7fa9\u77e5\u7db2\u8a5e\u5f59\u610f\ufa0a\u6975\u6027\u7684\u9810\u6e2c 35 5. \u7d50\uf941 \u672c\u7814\u7a76\u4f7f\u7528\uf9ba Google Web 5-gram Version 1 \uf92d\u62bd\u53d6\u8a9e\u7bc7\u7279\u5fb5\uff0c\u4e26\u52a0\u4e0a\uf92d\u81ea E-HowNet \u7684 \u57fa\u790e\u7fa9\u539f\u7279\u5fb5\uff0c\u7528\u76e3\u7763\u5f0f\u6a5f\u5668\u5b78\u7fd2\u7684\u65b9\u6cd5\uff0c\uf92d\u9810\u6e2c E-HowNet \u8a5e\u5f59\u7684\u610f\ufa0a\u6975\u6027\u3002\u96d6\u7136\u55ae \u7368\u4f7f\u7528\uf967\u540c\u7684\u7279\u5fb5\u5df2\u7d93\u53ef\u4ee5\u63a5\u8fd1 90% \u7684\u6e96\u78ba\uf961\uff0c\u4f46\u5982\u679c\u628a\uf978\u7a2e\u7279\u5fb5\u90fd\u52a0\u4ee5\u4f7f\u7528\uff0c\u5206\uf9d0 \u5668\u7684\u6975\u6027\u9810\u6e2c\u7684\u6e96\u78ba\uf961\u53ef\u5230\u9054 92.33% \u7684\u9ad8\u6e96\u78ba\uf961\uff1b\u4ee5\u9019\u7a2e\u65b9\u5f0f\u5efa\uf9f7\u7684\u5206\uf9d0\u5668\uff0c\u53ef\u7528\uf92d \u81ea\u52d5\u6a19\u8a18 E-HowNet \u8a5e\u5f59\u7684\u610f\ufa0a\u6975\u6027\u3002 \u5716 4 \uf9e1\u653f\u5112 \u7b49 \u5716 4. \u5ee3\u7fa9\u77e5\u7db2\u7279\u5fb5\u65bc\uf967\u540c \u03b1 \u503c\u7684\u6548\u80fd\u6bd4\u8f03 \u6211\u5011\u5f9e\u5716 4 \u53ef\u4ee5\u770b\u51fa\uff0c\u63cf\u8ff0 PBFN \u7684\u6298\u7dda\u5728\u6240\u6709\u7684 \u03b1 \u503c\u4e0b\uff0c\u6e96\u78ba\uf961\u7686\uf976\u9ad8\u65bc PBF\uff0c \u4f46\u662f\uf978\u500b\u6700\u5927\u503c (\u03b1 = \u22120.02) \u7684\u5dee\u8ddd\u50c5 0.1724%\uff0c\u6b64\u5dee\u8ddd\u70ba\uf967\u986f\u8457\uff0c\u6aa2\u5b9a\u7d50\u679c (1.50, 0.22)\u3002 \u7531\u65bc \u03b1 < 0 \u6709\u6700\u4f73\u6548\u80fd\uff0c\u9019\u8868\u793a\u6df1\ufa01\u8f03\u6df1\u7d66\u8f03\u9ad8\u6b0a\u91cd\uff0c\u8a72\u7fa9\u539f\u6709\u8f03\u597d\u7684\u7279\u5fb5\uff0c\u53ef\u4ee5\u7d66\u5206 \uf9d0\u5668\u5b78\u7fd2\u3002 4.3 \u8a9e\u7bc7\u7279\u5fb5\u7684\u6548\u80fd \u8a9e\u7bc7\u7279\u5fb5\u4f7f\u7528\u5341\u7d44\u7279\u5fb5\u96c6\u7684\u540d\u7a31\uff0c\u4ee5\u53ca\u7279\u5fb5\uf969\uf97e\uff0c\u5982\u8868 3 \u6240\u793a\u3002\u5728\u8868\u4e2d\uff0c\u6211\u5011\u4f7f\u7528\u7279\u5fb5 \u96c6\u4ee3\u865f\uf92d\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\u591a \u7684\u662f All \u7684\u7279\u5fb5\u96c6\uff0c\u6709 86,712 \u500b\u8a5e\u3002 \u8868 3. \u8a9e\u7bc7\u7279\u5fb5\u6240\u4f7f\u7528\u7684\u7279\u5fb5\u96c6\u8207\u5176\u7279\u5fb5\uf969 \u7279\u5fb5\u96c6 \u7279\u5fb5\u96c6\u4ee3\u865f \u7279\u5fb5\uf969 \u5ee3\u7fa9\u77e5\u7db2\u540d\u8a5e Noun 46,807 \u5ee3\u7fa9\u77e5\u7db2\u52d5\u8a5e Verb 37,109 \u5ee3\u7fa9\u77e5\u7db2\u526f\u8a5e Adv. 2,364 \u5ee3\u7fa9\u77e5\u7db2\u5f62\u5bb9\u8a5e Adj. 948 All 86,712 \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 F5000-2 97.2635% 98.0392% 97.1705% 96.0912% 94.9153% 94.8718% \u7684\uf969\uf97e\u3002 \u5ee3\u7fa9\u77e5\u7db2\u6240\u6709\u8a5e\u5f59 89.0% 89.2% -0.05 -0.04 -0.03 -0.02 -0.01 0 0.01 0.02 0.03 0.04 0.05 Alpha \u6b64\u6548\u80fd\u8ddf\u5176\u4ed6\uf978\u8005\u7684\u5dee\u8ddd\u70ba\u986f\u8457\uff0c\u6aa2\u5b9a\u7d50\u679c (4.61, 0.03)\u3002 \u5716 5. \u4f7f\u7528\u8a9e\u7bc7\u7279\u5fb5\u6642\u7684\u9810\u6e2c\u6548\u80fd \u5716 5 \u4e2d\u7279\u5fb5\u96c6\u7684\u500b\uf969\uff0c\u4e26\u6c92\u6709\u7d55\u5c0d\u7684\u5f71\u97ff\uff0c\u4f46\uf974\u500b\uf969\u592a\u5c11\uff0c\u5982\u7279\u5fb5\u500b\uf969\u5c0f\u65bc 2364 \u500b\uff0c\u5247\u6548\u80fd\u6703\u660e\u986f\u8b8a\u5dee\u3002\u5716 4 \u4e2d\u7684\u6700\u4f73\u503c PBFN(\u03b1 = \u22120.02)\u70ba 89.61%\uff0c\u7279\u5fb5\u500b\uf969\u70ba 2,567 \u500b\uff0c\u9019\u500b\u503c\u6bd4\u5716 5 \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\u9019 \u5dee\u8ddd\u70ba\uf967\u986f\u8457\uff0c\u6aa2\u5b9a\u7d50\u679c (2.49, 0.11)\u3002 4.4 \u7d44\u5408\uf967\u540c\u7279\u5fb5\u7684\u6548\u80fd 50.0% 60.0% 70.0% 80.0% 90.0% 100.0% Adj. (948) Adv. (2364) F5000-1 (5000) F5000-2 (5000) F10000-1 (10000) F10000-2 (10000) Verb (37109) NTUSD (42614) Noun (46807) All (86712) Accuracy Feature set Original Frequency Cos-Normalized Frequency Cos-Normalized TFIDF \u5716 6. \u5ee3\u7fa9\u77e5\u7db2\u3001\u8a9e\u7bc7\u7279\u5fb5\u3001\u8207\u7d44\u5408\u7279\u5fb5\u7684\u6e96\u78ba\uf961\u6bd4\u8f03 4.5 \u7d44\u5408\u5f0f\u7684\u76e3\u7763\u5f0f\u6a5f\u5668\u5b78\u7fd2\u6f14\u7b97\u6cd5\u6548\u80fd \u5728\u5716 6 \u4e2d\uff0c\u7d44\u5408\u51fa\u7684\u7279\u5fb5\u96c6\u6709\u5341\u500b\uff0c\u6240\u4ee5\u5171\u6709\u5341\u500b\u5206\uf9d0\u5668\uff0c\u6bcf\u500b\u5206\uf9d0\u5668\u5728\u8a13\uf996\u6642\uff0c\u5c0d\uf967 \u540c\u8a5e\u6027\u6709\uf967\u540c\u7684\u6548\u80fd\uff0c\u6211\u5011\u5c07\u9019\u5341\u500b\u5206\uf9d0\u5668\u5c0d\u65bc\u6bcf\u500b\u8a5e\u6027\u6a19\u8a18\u7684\u6548\u80fd(\u5167\u90e8\u6e2c\u8a66)\u6574\uf9e4 \u6210\u8868 4\u3002 \u8868 4. \u8a13\uf996\u8cc7\uf9be\u96c6\u4e2d\uff0c\u7d44\u5408\u7279\u5fb5\u5c0d\uf967\u540c\u8a5e\u6027\u7684\u6a19\u8a18\u6e96\u78ba\uf961 \u7279\u5fb5\u96c6\u4ee3\u865f \u7e3d\u9ad4\u6548\u80fd \u8a13\uf996\u8cc7\uf9be\u96c6\u4e2d\uff0c\u4f9d\u8a5e\u6027\u5206\u5225\u8a08\u7b97\u7684\u6e96\u78ba\uf961 \u540d\u8a5e \u52d5\u8a5e \u526f\u8a5e \u5f62\u5bb9\u8a5e \u5176\u4ed6 Adj. 94.3223% 95.9559% 94.2167% 89.9023% 93.2203% 82.0513% Adv. 95.3243% 96.5074% 95.2795% 92.1824% 91.5254% 84.6154% F5000-1 96.1000% 97.3039% 96.0110% 92.8339% 94.9153% 89.7436% 75.0% 80.0% 85.0% 90.0% Adj. (948) Adv. (2364) F5000-1 (5000) F5000-2 (5000) F10000-1 (10000) F10000-2 (10000) Verb (37109) NTUSD (42614) Noun (46807) All (86712) Accuracy Feature set Gloss Context Combine gloss and Context \u5167\u90e8\u6e2c\u8a66\u6548\u80fd(inside test)F10000-2+PBFN \u03b1 = \u22120.03 \u7684 97.5005% \u8ddf\u5be6\u969b\u6e2c\u8a66\u6548\u80fd 92. 3276%\u76f8\u6bd4\uff0c\ufa09\u4f4e\uf9ba 5.31%\uff0c\u9019\ufa09\u4f4e\u5e45\ufa01\u4e26\uf967\u5927\uff0c\u986f\u793a\u9019\u5206\uf9d0\u5668\u7684 generalization \u80fd\uf98a\uf967 \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\uf9d0\u5668(Bergsma, Pitler, & Lin, 2010)\u3002 \u6211\u5011\u7528\u5167\u90e8\u6e2c\u8a66\u6548\u80fd\uf92d\u6311\u9078\u5206\uf9d0\u5668\uff0c\u4ee5\uf965\u7528\u5728\u7d44\u5408\u5f0f\u7684\u76e3\u7763\u5f0f\u6a5f\u5668\u5b78\u7fd2\u6f14\u7b97\u6cd5\u4e2d\u3002 \u6211\u5011\u5728\u8868 4 \u4e2d\u9078\uf967\u540c\u8a5e\u6027\u505a\u5f97\u6700\u597d\u7684\u5206\uf9d0\u5668\uf92d\u7d44\u5408\uff0c\u5982\u679c\u6548\u80fd\u76f8\u540c\uff0c\u5247\u9078\u7279\u5fb5\uf969\uf97e\u8f03\u5c11 \u7684\u90a3\u4e00\u500b\u5206\uf9d0\u5668\uff0c\u56e0\u70ba\u7279\u5fb5\uf969\u8f03\u5c11\u901a\u5e38\u5728\u672a\u770b\u904e\u7684\u8cc7\uf9be\u96c6\u6703\u505a\u5f97\u8f03\u597d\u3002\u7d44\u5408\u51fa\u7684\u5206\uf9d0\u5668 \u6211\u5011\u7a31\u70ba EnsembleClassifier\uff0c\u5176\u7d50\u679c\uf99c\u5728\u8868 5\uff0c\u5176\u4e2d F10000-2+PBFN \u03b1 = \u22120.03 \u65bc\u5404\u8a5e\u6027\u7684 \u6a19\u8a18\u6548\u80fd\u4e5f\uf99c\u51fa\uf92d\u6bd4\u8f03\u3002 \u8868 5. \u7d44\u5408\u5206\uf9d0\u5668\u65bc\u5404\u8a5e\u6027\u7684\u6a19\u8a18\u6548\u80fd\u53ca\u6bd4\u8f03 \u5206 \uf9d0 \u5668 \u8a5e\u6027 F10000-2+PBFN \u03b1 = \u22120.03 \u5206\uf9d0\u5668 \u65bc\u5404\u8a5e\u6027\u7684\u6a19\u8a18\u6548\u80fd \u7d44\u5408\u5206\uf9d0\u5668 EnsembleClassifier \u65bc\u5404\u8a5e\u6027\u7684\u6a19\u8a18\u6548\u80fd \u6b63\u78ba \u500b\uf969 \u932f\u8aa4 \u500b\uf969 \u6e96\u78ba\uf961 \u4f7f\u7528\u7684 \u5206\uf9d0\u5668 \u6b63\u78ba \u500b\uf969 \u589e\u6e1b \u932f\u8aa4 \u500b\uf969 \u6e96\u78ba\uf961 \u540d\u8a5e 371 37 90.9314% F10000-2 371 (+0) 37 90.9314% \u52d5\u8a5e 1,681 130 92.8216% F10000-2 1,681 (+0) 130 92.8216% \u526f\u8a5e 67 9 88.1579% F5000-2 69 (+2) 7 90.7895% \u5f62\u5bb9\u8a5e 14 1 93.3333% Noun 12 (-2) 3 80.0000% \u5176\u4ed6 9 1 90.0000% F5000-2 9 (+0) 1 90.0000% \u7e3d\uf969 2,142 178 92.3276% 2142 (+0) 178 92.3276% \u8868 5 \u4e2d\uff0c\u6211\u5011\u4e5f\uf99c\u51fa\u6bcf\u7a2e\u8a5e\u6027\u505a\u932f\u8207\u505a\u5c0d\u7684\u500b\uf969\uff0c\u4e26\u4ee5 F10000-2+PBFN \u03b1 = \u22120.03 \u5206\uf9d0 \u5668\u70ba\u57fa\u6e96\uff0c\u770b\u7d44\u5408\u5f8c\u7684\u5206\uf9d0\u5668\uff0c\u5728\u5404\u8a5e\u6027\u4e2d\u505a\u5c0d\u505a\u932f\u7684\u6b21\uf969\u7684\u589e\u6e1b\uff0c\u7528\u62ec\u865f\uf92d\u6a19\u51fa\u589e\u6e1b \u5716 7. \u56db\u7a2e\u65b9\u6cd5\u6548\u80fd\u6bd4\u8f03 \u7531\u65bc\u6211\u5011\u4f7f\u7528 NTUSD\uff0c\u6211\u5011\u60f3\u770b\u770b NTUSD \u4eba\uf9d0\u6a19\u8a18\u7684\u6548\u80fd\u8ddf\u6211\u5011\u5206\uf9d0\u5668\u7684\u6548\u80fd\u6709 \u4f55\u5dee\uf962\u3002\u5728 Ku & Chen (2007)\u7684\u7814\u7a76\u4e2d\uff0c\u5c0d\u8a5e\u6709\u5206\u56db\uf9d0\u6a19\u8a18\uff0c\u5206\u5225\u662f\u6b63\u9762\u3001\u8ca0\u9762\u3001\u4e2d\uf9f7\u3001 \u53ca\u975e\u610f\ufa0a\u8a5e\uff0c\u4e26\u8058\u8acb\u6a19\u8a18\u8005\u5c0d NTUSD \u9032\ufa08\u6a19\u8a18\uff0c\u6211\u5011\u5c07\u8a72\u7814\u7a76\u4e2d\u6a19\u8a18\u8005\u7684\u6700\u4f73\u6a19\u8a18\u6548 \u80fd\u8207\u672c\u7814\u7a76\u7684\u6bd4\u8f03\u5982\u8868 6\u3002\u7531\u65bc\u4eba\uf9d0\u6a19\u8a18\u8005\u662f\u5c07\u8a5e\u5206\u6210\u56db\uf9d0\uff0c\u4f46\u6211\u5011\u7684\u7cfb\u7d71\u53ea\u5206\uf978\uf9d0\uff0c \u6240\u4ee5\u9019\uf969\u64da\u6c92\u6709\u8fa6\u6cd5\u8ddf\u6211\u5011\u7684\u7d50\u679c\u76f4\u63a5\u76f8\u6bd4\u8f03\uff1b\u4f46\u6211\u5011\u4ecd\u53ef\u5f9e\u8868 6 \u4e2d\u770b\u51fa\uff0c\u672c\u7814\u7a76\u6240\u7522 \u751f\u7684\u6b63\u8ca0\u9762\u8a5e\u5f59\u81ea\u52d5\u6a19\u8a18\u6f14\u7b97\u6cd5\uff0c\u5df2\u9054\u5230\uf9ba\u5f88\u9ad8\u7684\u6548\u80fd\u3002 \u8868 6. NTUSD \u6a19\u8a18\u8005\u8207\u672c\u7814\u7a76\u6a19\u8a18\u6548\u80fd\u6bd4\u8f03 \u5206\uf9d0\u5668 Recall Precision F-Measure F10000-2+PBFN \u03b1 = \u22120.03 92.36% 92.20% 92.27% \u4e09\u4eba\u4e2d\u6700\u4f73\u7684\u4eba\uf9d0\u6a19\u8a18\u8005 96.58% 88.87% 92.56% 88.2328% 90.0% Gloss Context Combine gloss and Context Ensemble classifier \u6211\u5011\u5e0c\u671b\u5728\u672a\uf92d\u80fd\u628a\u9019\u7a2e\u65b9\u5f0f\uff0c\u5f80\uf967\u540c\u7684\u65b9\u5411\u64f4\u5c55\uff0c\uf92d\u7d66\u4e88 E-HowNet \u8a5e\u5f59\uf901\u591a\u610f Accuracy \ufa0a\u7684\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\ufa01\u7b49\u3002\u9664\u6b64\u4e4b\u5916\uff0c\u56e0\u70ba E-HowNet \u8a5e\u5f59\u6709\u8a31\u591a\uf967\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\uf92d\u9032\ufa08 \u6a19\u8a18\u3002\u85c9\u7531\u63d0\u4f9b\uf901\u7cbe\u78ba\u7684\u5b57\u5f59\u610f\ufa0a\u6a19\u8a18\u8cc7\u8a0a\uff0c\uf92d\u652f\u63f4\uf906\u5b50\u53ca\u6587\u4ef6\u5c64\u6b21\u7684\u610f\ufa0a\u5206\u6790\u3002 \u8868 6 89.6121% 85.0% \u81f4\u8b1d</td></tr><tr><td>\u6700\u5e38\u51fa\u73fe 5000 \u8a5e \u5fb5\u96c6\uff0c\u5206\u5225\u8207\u5ee3\u7fa9\u77e5\u7db2\u7279\u5fb5\u6548\u80fd\u6700\u597d\u7684 PBFN \u03b1 = \u22120.03 \u7d44\u5408\uff0c\uf92d\u8a13\uf996\u5206\uf9d0\u5668\uff0c\u5206\uf9d0\u5668\u9810\u6e2c\u6e96 F5000-1 5,000 F10000-1 96.2400% 97.3652% 96.1767% 92.8339% 94.9153% 89.7436% EnsembleClassifier \u6240\u5f97\u6210\u7e3e\u8ddf F10000-2+PBFN \u03b1 = \u22120.03 \u76f8\u540c\uff0c\u9019\u8868\u793a\u76ee\u524d\u7684\u5206\uf9d0\u5668\u7d44</td></tr><tr><td>\u6700\u5e38\u51fa\u73fe 5000 \u8a5e(\u9577\ufa01\u22672) \u78ba\uf961\u5982\u5716 6\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 \u90a3\u689d F5000-2 5,000 F10000-2 97.5005% 98.2843% 97.4189% 96.0912% 94.9153% 94.8718% \u5408\u65b9\u5f0f\uff0c\u7121\u6cd5\u63d0\u5347\u6548\u80fd\u3002</td></tr><tr><td>\u6700\u5e38\u51fa\u73fe 10000 \u8a5e \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\uf9b5\u5982 F10000-1 Verb 96.5632% 97.5490% 96.5079% 94.4625% 91.5254% 89.7436% 10,000 \u300cF10000-2+PBFN \u03b1 = \u22120.03 \u300d\u8868\u793a\u300c\u6700\u5e38\u51fa\u73fe 10000 \u8a5e(\u9577\ufa01\u22672)\u300d\u8ddf\u300cPBFN \u03b1 = \u22120.03 \u300d\uf978 NTUSD 96.8218% 97.3039% 96.8254% 95.1140% 93.2203% 94.8718% \u6700\u5e38\u51fa\u73fe 10000 \u8a5e(\u9577\ufa01\u22672) F10000-2 10,000 \u500b\u7279\u5fb5\u96c6\u7684\u7d44\u5408\u3002 Noun 96.8541% 98.1005% 96.6460% 96.0912% 96.6102% 89.7436%</td></tr><tr><td>NTUSD(\u5b8c\u6574\u7248) \u6211\u5011\u5f9e\u5716 6 \u53ef\u4ee5\u770b\u51fa\uff0c\u5c07\u5ee3\u7fa9\u77e5\u7db2\u7279\u5fb5\u8207\u5916\u90e8\u8a9e\uf9be\u7279\u5fb5\u7d44\u5408\u4e4b\u5f8c\uff0c\u6e96\u78ba\uf961\u90fd\u6709\u986f\u8457 NTUSD 42,614 All 96.4124% 97.4265% 96.3699% 93.1596% 94.9153% 89.7436%</td></tr></table>", | |
| "num": null, | |
| "type_str": "table", | |
| "text": "\u70ba\u57fa\u790e\u7fa9\u539f\u65b9\u6cd5\u5728\uf967\u540c \u03b1 \u503c\u6240\u5f97\u5230\u7684\u9810\u6e2c\u6e96\u78ba\uf961\uff0c\u5176\u4e2d\u516c\u5f0f (2) \u7684\u7d50\u679c\u662f PBF (Prime-Based Feature)\u90a3\u689d\u6298\u7dda\uff0c\u6700\u4f73\u7684 \u03b1 \u503c\u70ba \u22120.02\uff0c\u6e96\u78ba\uf961\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\uf961\u70ba 89.6121%\u3002 \u4e2d\uff0c\u4eba\uf9d0\u6a19\u8a18\u8005\u7684 Recall \u53ca Precision \u53d6\u81ea Ku & Chen (2007)\u3002F10000-2+PBFN \u03b1 = \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\uf9be\u96c6\u6709\u8f03\u591a\u7684\u8ca0\u9762\u8a5e\u5f59\uff0c\u6240\u4ee5\u6700\u5f8c\u7684\u6e96\u78ba\uf961 92.33% \u6bd4 F + \u9ad8\u3002 Research of this paper was partially supported by National Science Council (Taiwan) under the contract NSC 98-2221-E-002-175-MY3.", | |
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