| { | |
| "paper_id": "O17-3004", | |
| "header": { | |
| "generated_with": "S2ORC 1.0.0", | |
| "date_generated": "2023-01-19T07:59:27.774516Z" | |
| }, | |
| "title": "Exploring the Use of Neural Newtork based Features for Text Readability Classification", | |
| "authors": [ | |
| { | |
| "first": "\u66fe\u539a\u5f37", | |
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| "\uf02a" | |
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| "last": "\u3001\u9673\u67cf\u7433", | |
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| { | |
| "first": "\uf02a", | |
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| "last": "\u3001\u5b8b\u66dc\u5ef7", | |
| "suffix": "", | |
| "affiliation": {}, | |
| "email": "" | |
| }, | |
| { | |
| "first": "Hou-Chiang", | |
| "middle": [], | |
| "last": "Tseng", | |
| "suffix": "", | |
| "affiliation": {}, | |
| "email": "" | |
| }, | |
| { | |
| "first": "Berlin", | |
| "middle": [], | |
| "last": "Chen", | |
| "suffix": "", | |
| "affiliation": {}, | |
| "email": "[email protected]" | |
| }, | |
| { | |
| "first": "Yao-Ting", | |
| "middle": [], | |
| "last": "Sung", | |
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| "affiliation": {}, | |
| "email": "[email protected]" | |
| } | |
| ], | |
| "year": "", | |
| "venue": null, | |
| "identifiers": {}, | |
| "abstract": "Text readability refers to the degree to which a text can be understood by its readers: the higher the readability of a text for readers, the better the the", | |
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| "paper_id": "O17-3004", | |
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| "abstract": [ | |
| { | |
| "text": "Text readability refers to the degree to which a text can be understood by its readers: the higher the readability of a text for readers, the better the the", | |
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| "section": "Abstract", | |
| "sec_num": null | |
| } | |
| ], | |
| "body_text": [ | |
| { | |
| "text": "\u4e00\u822c\u800c\u8a00\uff0c\u53ef\u8b80\u6027(Readability)\u662f\u6307\u95b1\u8b80\u6750\u6599\u80fd\u5920\u88ab\u8b80\u8005\u6240\u7406\u89e3\u7684\u7a0b\u5ea6 (Dale & Chall, 1949; Klare, 1963 Klare, , 2000 Mc Laughlin, 1969 )\uff0c\u7576\u8b80\u8005\u95b1\u8b80\u9ad8\u53ef\u8b80\u6027\u7684\u6587\u4ef6\u6642\uff0c\u6703\u7522\u751f\u8f03\u597d\u7684\u7406 \u89e3\u53ca\u5b78\u5f8c\u4fdd\u7559\u6548\u679c (Klare, 1963 (Klare, , 2000 \u3002\u7531\u65bc\u6587\u4ef6\u7684\u53ef\u8b80\u6027\u5728\u77e5\u8b58\u50b3\u905e\u626e\u6f14\u6975\u70ba\u91cd\u8981\u7684\u89d2 \u8272\uff0c\u56e0\u6b64\u897f\u65b9\u7684\u53ef\u8b80\u6027\u516c\u5f0f\u767c\u5c55\u7684\u975e\u5e38\u65e9\uff0c\u5982\uff1a1923 \u5e74 Bertha \u7b49\u4eba\u5c31\u63d0\u51fa\u65b9\u6cd5\u4f86\u63a2\u8a0e\u6559 \u79d1\u66f8\u4e2d\u5b57\u5f59\u96e3\u5ea6\u7684\u554f\u984c (Bertha & Pressey, 1923) \u3002\u53e6\u5916\uff0cVogel \u548c Washburne \u5728 1928 \u5e74\u5247 \u662f\u63d0\u51fa\u4e00\u500b Winnetka Formula \u4f86\u8a55\u91cf\u5c0f\u5b69\u8b80\u7269\u7684\u53ef\u8b80\u6027 (Vogel & Washburne, 1928) \u3002\u7531\u65bc \u53ef\u8b80\u6027\u76f8\u95dc\u7684\u7814\u7a76\u975e\u5e38\u91cd\u8981\u3002\u56e0\u6b64\uff0c\u64da Chall \u8207 Dale \u5728 1995 \u5e74\u7684\u7d71\u8a08\uff0c\u5230 1980 \u5e74\u70ba\u6b62 \u76f8\u95dc\u7684\u53ef\u8b80\u6027\u516c\u5f0f\u5c31\u5df2\u7d93\u8d85\u904e 200 \u591a\u500b\u53ef\u8b80\u6027\u516c\u5f0f(Chall & Dale, 1995 \u3002\u9019\u4e9b\u50b3\u7d71\u7684\u53ef\u8b80 \u6027\u7814\u7a76\u5927\u591a\u4f7f\u7528\u8f03\u6dfa\u5c64\u7684\u8a9e\u8a00\u7279\u5fb5\u4f86\u767c\u5c55\u7dda\u6027\u7684\u53ef\u8b80\u6027\u516c\u5f0f\uff0c\u4f8b\u5982 Flesch Reading Ease \u63a1\u7528\u8a5e\u5f59\u7684\u5e73\u5747\u97f3\u7bc0\u6578\u8207\u5e73\u5747\u7684\u53e5\u5b50\u9577\u5ea6 (Flesch, 1948 )\u6216 Chall \u548c Dale \u8a08\u7b97\u96e3\u8a5e\u5728\u6587\u7ae0 \u4e2d\u7684\u6bd4\u7387 (Chall & Dale, 1995) \u7b49\uff0c\u90fd\u662f\u50b3\u7d71\u53ef\u8b80\u6027\u516c\u5f0f\u4ee3\u8868\u4e4b\u4e00\u3002\u7136\u800c\uff0c\u50b3\u7d71\u53ef\u8b80\u6027\u516c\u5f0f \u6240\u63a1\u7528\u7684\u6dfa\u5c64\u8a9e\u8a00\u7279\u5fb5\uff0c\u4e26\u4e0d\u8db3\u4ee5\u53cd\u6620\u6587\u4ef6\u96e3\u5ea6\u3002Graesser\u3001Singer \u548c Trabasso \u4fbf\u6307\u51fa\uff0c \u50b3\u7d71\u8a9e\u8a00\u7279\u5fb5\u516c\u5f0f\u7121\u6cd5\u53cd\u6620\u95b1\u8b80\u7684\u771f\u5be6\u6b77\u7a0b\uff0c\u6587\u4ef6\u7684\u8a9e\u610f\u8a9e\u6cd5\u53ea\u662f\u6587\u4ef6\u7684\u6dfa\u5c64\u8a9e\u8a00\u7279\u5fb5\uff0c \u6c92\u6709\u8003\u91cf\u6587\u4ef6\u7684\u51dd\u805a\u7279\u6027 (Graesser, Singer & Trabasso, 1994) \u3002Collins-Thompson \u4ea6\u6307\u51fa\u50b3 \u7d71\u53ef\u8b80\u6027\u516c\u5f0f\u50c5\u8457\u91cd\u5728\u6587\u4ef6\u7684\u8868\u6dfa\u8cc7\u8a0a\uff0c\u800c\u5ffd\u7565\u6587\u4ef6\u91cd\u8981\u7684\u6df1\u5c64\u7279\u5fb5\u3002\u9019\u4e5f\u8b93\u50b3\u7d71\u53ef\u8b80 \u6027\u516c\u5f0f\u5728\u9810\u6e2c\u6587\u672c\u53ef\u8b80\u6027\u7684\u7d50\u679c\u5e38\u906d\u53d7\u5230\u8cea\u7591 (Collins-Thompson, 2014) \u3002\u76f4\u5230\u4eca\u65e5\uff0c\u53ef \u8b80\u6027\u7684\u7814\u7a76\u4ecd\u6301\u7e8c\u4e0d\u65b7\u3002\u7814\u7a76\u4eba\u54e1\u70ba\u4e86\u514b\u670d\u50b3\u7d71\u53ef\u8b80\u6027\u516c\u5f0f\u7684\u7f3a\u9ede\uff0c\u5617\u8a66\u5229\u7528\u66f4\u7d30\u7dfb\u7684 \u6a5f\u5668\u5b78\u7fd2\u6f14\u7b97\u6cd5\u4f86\u767c\u5c55\u51fa\u975e\u7dda\u6027\u7684\u53ef\u8b80\u6027\u6a21\u578b\uff0c\u4e26\u7d0d\u5165\u66f4\u591a\u5143\u7684\u53ef\u8b80\u6027\u6307\u6a19\u4f86\u5171\u540c\u8a55\u91cf \u6587\u672c\u7684\u53ef\u8b80\u6027\uff0c\u4ee5\u63d0\u5347\u53ef\u8b80\u6027\u6a21\u578b\u7684\u6548\u80fd (Petersen & Ostendorf, 2009; Feng, Jansche, Huenerfauth & Elhadad, 2010; Sung et al., 2015) (Yan, Song & Li, 2006) \u3002\u91dd\u5c0d\u4e00\u822c\u8a9e\u8a00\u7279\u5fb5\u7121\u6cd5\u8868\u5fb5\u7279\u5b9a \u9818\u57df\u77e5\u8b58\u7d50\u69cb\u7684\u554f\u984c\uff0c\u958b\u59cb\u6709\u5b78\u8005\u91dd\u5c0d\u9019\u500b\u8b70\u984c\u9032\u884c\u7814\u7a76\u3002\u4f8b\u5982\uff0cYan \u7b49\u4eba\u5229\u7528\u672c\u9ad4\u8ad6 \u7684\u6280\u8853\u5c07\u7f8e\u570b\u570b\u5bb6\u91ab\u5b78\u8cc7\u6599\u5eab(Medical Subject Headings, MeSH)\u7684\u91ab\u5b78\u7b26\u865f\u968e\u5c64\u8cc7\u6599\u5eab \u4f5c\u70ba\u6982\u5ff5\u8cc7\u6599\u5eab\uff0c\u5f9e\u4e2d\u627e\u51fa\u6bcf\u4e00\u500b\u91ab\u5b78\u985e\u6587\u4ef6\u4e2d\u7684\u6982\u5ff5\uff0c\u4e26\u8a08\u7b97\u6982\u5ff5\u5230\u6b64\u6a39\u72c0\u7d50\u69cb\u6700\u5e95 \u90e8\u7684\u8ddd\u96e2\uff0c\u5f97\u51fa\u6bcf\u7bc7\u6587\u4ef6\u6982\u5ff5\u6df1\u5ea6\u6307\u6a19(Document Scope) (Yan et al., 2006) \u3002Borst \u7b49\u4eba\u5247 \u662f\u5229\u7528\u8a5e\u8868\u7684\u65b9\u5f0f\u5c07\u6bcf\u500b\u8a5e\u5f59\u7684\u300c\u985e\u5225\u8907\u96dc\u5ea6\u300d\u8207\u300c\u8a5e\u983b\u300d\u5169\u500b\u5206\u6578\u52a0\u7e3d\u4f86\u8a08\u7b97\u8a5e\u5f59\u8907 \u96dc\u5ea6\uff0c\u4f5c\u70ba\u8a55\u4f30\u91ab\u5b78\u985e\u7dda\u4e0a\u6587\u4ef6\u8a5e\u5f59\u3001\u53e5\u5b50\u53ca\u6587\u4ef6\u96e3\u5ea6\u7684\u4f9d\u64da (Borst, Gaudinat, Grabar & Boyer, 2008) ", | |
| "cite_spans": [ | |
| { | |
| "start": 39, | |
| "end": 59, | |
| "text": "(Dale & Chall, 1949;", | |
| "ref_id": "BIBREF12" | |
| }, | |
| { | |
| "start": 60, | |
| "end": 71, | |
| "text": "Klare, 1963", | |
| "ref_id": "BIBREF27" | |
| }, | |
| { | |
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| "end": 85, | |
| "text": "Klare, , 2000", | |
| "ref_id": "BIBREF28" | |
| }, | |
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| "start": 86, | |
| "end": 103, | |
| "text": "Mc Laughlin, 1969", | |
| "ref_id": "BIBREF34" | |
| }, | |
| { | |
| "start": 137, | |
| "end": 149, | |
| "text": "(Klare, 1963", | |
| "ref_id": "BIBREF27" | |
| }, | |
| { | |
| "start": 150, | |
| "end": 164, | |
| "text": "(Klare, , 2000", | |
| "ref_id": "BIBREF28" | |
| }, | |
| { | |
| "start": 246, | |
| "end": 270, | |
| "text": "(Bertha & Pressey, 1923)", | |
| "ref_id": "BIBREF1" | |
| }, | |
| { | |
| "start": 338, | |
| "end": 363, | |
| "text": "(Vogel & Washburne, 1928)", | |
| "ref_id": "BIBREF44" | |
| }, | |
| { | |
| "start": 368, | |
| "end": 405, | |
| "text": "\u53ef\u8b80\u6027\u76f8\u95dc\u7684\u7814\u7a76\u975e\u5e38\u91cd\u8981\u3002\u56e0\u6b64\uff0c\u64da Chall \u8207 Dale \u5728 1995", | |
| "ref_id": null | |
| }, | |
| { | |
| "start": 406, | |
| "end": 417, | |
| "text": "\u5e74\u7684\u7d71\u8a08\uff0c\u5230 1980", | |
| "ref_id": null | |
| }, | |
| { | |
| "start": 418, | |
| "end": 466, | |
| "text": "\u5e74\u70ba\u6b62 \u76f8\u95dc\u7684\u53ef\u8b80\u6027\u516c\u5f0f\u5c31\u5df2\u7d93\u8d85\u904e 200 \u591a\u500b\u53ef\u8b80\u6027\u516c\u5f0f(Chall & Dale, 1995", | |
| "ref_id": null | |
| }, | |
| { | |
| "start": 545, | |
| "end": 558, | |
| "text": "(Flesch, 1948", | |
| "ref_id": "BIBREF17" | |
| }, | |
| { | |
| "start": 588, | |
| "end": 608, | |
| "text": "(Chall & Dale, 1995)", | |
| "ref_id": "BIBREF6" | |
| }, | |
| { | |
| "start": 742, | |
| "end": 777, | |
| "text": "(Graesser, Singer & Trabasso, 1994)", | |
| "ref_id": "BIBREF21" | |
| }, | |
| { | |
| "start": 861, | |
| "end": 885, | |
| "text": "(Collins-Thompson, 2014)", | |
| "ref_id": "BIBREF11" | |
| }, | |
| { | |
| "start": 991, | |
| "end": 1019, | |
| "text": "(Petersen & Ostendorf, 2009;", | |
| "ref_id": "BIBREF37" | |
| }, | |
| { | |
| "start": 1020, | |
| "end": 1063, | |
| "text": "Feng, Jansche, Huenerfauth & Elhadad, 2010;", | |
| "ref_id": "BIBREF16" | |
| }, | |
| { | |
| "start": 1064, | |
| "end": 1082, | |
| "text": "Sung et al., 2015)", | |
| "ref_id": "BIBREF39" | |
| }, | |
| { | |
| "start": 1083, | |
| "end": 1105, | |
| "text": "(Yan, Song & Li, 2006)", | |
| "ref_id": "BIBREF45" | |
| }, | |
| { | |
| "start": 1292, | |
| "end": 1310, | |
| "text": "(Yan et al., 2006)", | |
| "ref_id": "BIBREF45" | |
| }, | |
| { | |
| "start": 1389, | |
| "end": 1428, | |
| "text": "(Borst, Gaudinat, Grabar & Boyer, 2008)", | |
| "ref_id": "BIBREF3" | |
| } | |
| ], | |
| "ref_spans": [], | |
| "eq_spans": [], | |
| "section": "\u7dd2\u8ad6 (Introduction)", | |
| "sec_num": "1." | |
| }, | |
| { | |
| "text": "\u5728\u6587\u672c\u53ef\u8b80\u6027\u7684\u7814\u7a76\u4e2d\uff0c\u6f5b\u5728\u8a9e\u610f\u5206\u6790(Latent Semantic Analysis, LSA)\u662f\u65e9\u671f\u975e\u5e38\u53d7\u6b61\u8fce \u7684\u8a9e\u610f\u5206\u6790\u6280\u8853\u4e4b\u4e00 (Landauer & Dumais, 1997; Landauer, Foltz & Laham, 1998) \u3002\u5176\u6280\u8853 \u5982\u5716 1 \u6240\u793a\uff0c\u6f5b\u5728\u8a9e\u610f\u5206\u6790\u50c5\u9700\u8981\u5c07\u8a5e\u5f59-\u6587\u7ae0\u77e9\u9663\u5229\u7528\u5947\u7570\u503c\u5206\u89e3(singular value decomposition, SVD) \u5c07\u7dad\u5ea6\u7e2e\u6e1b\uff0c\u4fbf\u53ef\u4ee5\u64f7\u53d6\u51fa\u8a9e\u6599\u5eab\u7684\u8a9e\u610f\u7a7a\u9593\u4f86\u8868\u9054\u6587\u4ef6\u6f5b\u85cf\u8a9e\u610f \u5c6c\u6027\uff0c\u5728\u53d6\u5f97\u6f5b\u5728\u8a9e\u610f\u7a7a\u9593(U)\u5f8c\u4fbf\u53ef\u4ee5\u53bb\u6e2c\u91cf\u4efb\u610f\u4e8c\u500b\u8a5e\u5f59\u3001\u53e5\u5b50\u3001\u6bb5\u843d\u53ca\u6587\u7ae0\u4e4b\u9593\u7684 \u8a9e\u610f\u76f8\u4f3c\u5ea6\u3002\u5728\u904e\u53bb\uff0c\u5df2\u7d93\u8a31\u591a\u5b78\u8005\u5229\u7528\u6f5b\u5728\u8a9e\u610f\u5206\u6790\u9019\u7a2e\u8868\u5fb5\u5b78\u7fd2\u6cd5\u61c9\u7528\u5728\u53ef\u8b80\u6027\u7684 \u76f8\u95dc\u7814\u7a76\u3002\u5982 Graesser \u7b49\u4eba\u5728 Coh-Metrix 3.0 \u4e2d\u63d0\u4f9b\u4e86\u516b\u500b\u8ddf LSA \u76f8\u95dc\u7684\u6307\u6a19\u4f86\u6e2c\u91cf\u53e5 \u5b50\u6216\u7bc7\u7ae0\u7684\u76f8\u4f3c\u7a0b\u5ea6 (Graesser, McNamara, Louwerse & Cai, 2004) Figure 1 . Latent semantic analysis using singular value decomposition to extract the latent semantic space.] \u53e6\u4e00\u500b\u5e38\u88ab\u61c9\u7528\u65bc\u53ef\u8b80\u6027\u7814\u7a76\u7684\u8868\u793a\u5b78\u7fd2\u6cd5\u662f\uff1a\u8a5e\u5411\u91cf\uff0c\u8a5e\u5411\u91cf\u8868\u793a\u7684\u89c0\u5ff5\u6700\u65e9\u7531 Hinton \u5728 1986 \u5e74\u6240\u63d0\u51fa\uff0c\u53c8\u88ab\u7a31\u70ba\u8a5e\u8868\u793a(Word Representation or Word Embedding) (Hinton, 1986) (Liu, Chen, Tseng & Chen, 2015) \u3002Tseng \u7b49\u4eba\u5247\u662f\u5c07 Word2vec \u7d50\u5408\u652f\u5411\u91cf\u6a5f (Tseng, Sung, Chen & Lee, 2016a) \u6216\u6df1\u5c64\u985e\u795e\u7d93\u7db2\u8def (Tseng, Hung, Sung & Chen, 2016b) \u767c\u5c55\u51fa\u4e00\u500b\u80fd\u5920\u540c\u6642\u5206\u6790\u570b\u6587\u79d1\u3001 \u793e\u6703\u79d1\u53ca\u81ea\u7136\u79d1\u7b49\u4e0d\u540c\u9818\u57df\u6587\u672c\u7684\u53ef\u8b80\u6027\u6a21\u578b\u3002", | |
| "cite_spans": [ | |
| { | |
| "start": 67, | |
| "end": 92, | |
| "text": "(Landauer & Dumais, 1997;", | |
| "ref_id": "BIBREF29" | |
| }, | |
| { | |
| "start": 93, | |
| "end": 123, | |
| "text": "Landauer, Foltz & Laham, 1998)", | |
| "ref_id": "BIBREF31" | |
| }, | |
| { | |
| "start": 374, | |
| "end": 416, | |
| "text": "(Graesser, McNamara, Louwerse & Cai, 2004)", | |
| "ref_id": "BIBREF20" | |
| }, | |
| { | |
| "start": 630, | |
| "end": 644, | |
| "text": "(Hinton, 1986)", | |
| "ref_id": "BIBREF22" | |
| }, | |
| { | |
| "start": 645, | |
| "end": 676, | |
| "text": "(Liu, Chen, Tseng & Chen, 2015)", | |
| "ref_id": "BIBREF33" | |
| }, | |
| { | |
| "start": 706, | |
| "end": 738, | |
| "text": "(Tseng, Sung, Chen & Lee, 2016a)", | |
| "ref_id": null | |
| }, | |
| { | |
| "start": 748, | |
| "end": 781, | |
| "text": "(Tseng, Hung, Sung & Chen, 2016b)", | |
| "ref_id": null | |
| } | |
| ], | |
| "ref_spans": [ | |
| { | |
| "start": 417, | |
| "end": 425, | |
| "text": "Figure 1", | |
| "ref_id": null | |
| } | |
| ], | |
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| "section": "\u76f8\u95dc\u7814\u7a76 (Related Work)", | |
| "sec_num": "2." | |
| }, | |
| { | |
| "text": "\u3002Truran \u7b49\u4eba\u5247\u662f\u5229\u7528 \u66fe\u539a\u5f37 \u7b49 \u6f5b\u5728\u8a9e\u610f\u5206\u6790\u6280\u8853\u4f86\u7814\u7a76\u91ab\u5b78\u81e8\u5e8a\u6587\u7ae0\u7684\u53ef\u8b80\u6027(Truran, Georg, Cavazza & Zhou, 2010)\u3002 Fran\u00e7ois \u548c Miltsakaki \u5229\u7528\u6f5b\u5728\u8a9e\u610f\u5206\u6790\u53bb\u8a08\u7b97\u8a5e\u5f59\u4e4b\u9593\u7684\u51dd\u805a\u6027\u4f86\u7576\u6210\u53ef\u8b80\u6027\u6a21\u578b\u7684 \u8a9e\u610f\u6307\u6a19\uff0c\u4ee5\u5206\u985e\u6cd5\u6587\u70ba\u7b2c\u4e8c\u5b78\u7fd2\u8a9e\u8a00\u7684\u66f8\u672c\u53ef\u8b80\u6027(Fran\u00e7ois & Miltsakaki, 2012)\u3002 Kireyev \u548c Landauer \u4f7f\u7528\u6f5b\u5728\u8a9e\u610f\u5206\u6790\u4f86\u89c0\u5bdf\u5b57\u7684\u6210\u719f\u5ea6(Word Maturity)\uff0c\u4ee5\u4f30\u6e2c\u51fa\u8a5e \u5f59\u7684\u5e74\u7d1a\u96e3\u5ea6(Kireyev & Landauer, 2011)\u3002Chang \u7b49\u4eba\u5229\u7528\u6f5b\u5728\u8a9e\u610f\u5206\u6790\u65bc\u793e\u6703\u79d1\u548c\u81ea\u7136 \u79d1\u7684\u6559\u79d1\u66f8\uff0c\u5229\u7528\u76f8\u4f3c\u5ea6\u7684\u505a\u6cd5\u4f86\u6b78\u7d0d\u51fa\u6bcf\u500b\u5e74\u7d1a\u7684\u7279\u5b9a\u77e5\u8b58\u6982\u5ff5(Chang, Sung & Lee, 2013)\u3002 Term-Document Matrix (M by N matrix) M N SVD U M K \u03a3 K K V T K N \u5716 1. \u6f5b\u5728\u8a9e\u610f\u5206\u6790\u904b\u7528\u5947\u7570\u503c\u5206\u89e3\u62bd\u53d6\u6f5b\u5728\u8a9e\u610f\u7a7a\u9593 [", | |
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| "section": "\u76f8\u95dc\u7814\u7a76 (Related Work)", | |
| "sec_num": "2." | |
| }, | |
| { | |
| "text": "\u3002Bengio \u5728 2003 \u5e74\u63d0\u51fa\u56de\u994b\u5f0f\u985e\u795e\u7d93\u7db2\u8def\u8a9e\u8a00\u6a21\u578b(Feed-forward Neural Network Language Model (FFNNLM)\u7684\u8a13\u7df4\u67b6\u69cb\uff0c\u5f9e\u6587\u4ef6\u4e2d\u8a5e\u5f59\u524d\u5f8c\u76f8\u9130\u7684\u95dc\u4fc2\u4f86\u6c42\u53d6\u8a5e \u5411\u91cf\u8868\u793a(Bengio, Ducharme, Vincent & Jauvin, 2003)\u3002\u800c\u8fd1\u671f Google \u6240\u767c\u8868\u7684 Word2vec \u5247\u53ef\u8996\u70ba FFNNLM \u7684\u5f8c\u7e7c\u65b9\u6cd5(Mikolov, Chen, Corrado & Dean, 2013)\u3002\u7136\u800c\u8ddf FFNNLM \u67b6\u69cb\u4e0d\u4e00\u6a23\u7684\u662f\uff0cWord2vec \u53bb\u9664\u4e86 FFNNLM \u5728\u8a13\u7df4\u6642\u6700\u8017\u6642\u7684\u975e\u7dda\u6027\u96b1\u85cf\u5c64\uff0c\u50c5\u4fdd\u7559 \u8f38\u5165\u5c64\u3001\u6295\u5f71\u5c64\u548c\u8f38\u51fa\u5c64\uff0c\u4f7f\u5176\u67b6\u69cb\u66f4\u52a0\u7c21\u55ae\u3002Word2vec \u63d0\u4f9b\u4e86\u4e8c\u7a2e\u8a13\u7df4\u65b9\u5f0f\uff0c\u5206\u5225\u662f \u9023\u7e8c\u8a5e\u888b\u6a21\u578b(Continuous Bag-of-words Model, CBOW)\u53ca\u7565\u8a5e\u6a21\u578b(Skip-gram Model, Skip-gram)\u3002\u9023\u7e8c\u8a5e\u888b\u6a21\u578b\u4e3b\u8981\u7684\u7cbe\u795e\u662f\u7531\u76ee\u6a19\u8a5e\u4e4b\u5916\u7684\u524d\u5f8c\u6587\u4f86\u9810\u6e2c\u76ee\u6a19\u8a5e\u7684\u6a5f\u7387\uff1b\u800c \u7565\u8a5e\u6a21\u578b\u7684\u8a13\u7df4\u65b9\u5f0f\u6b63\u597d\u76f8\u53cd\uff0c\u5b83\u662f\u7531\u76ee\u6a19\u8a5e\u672c\u8eab\u4f86\u53bb\u9810\u6e2c\u524d\u5f8c\u6587\u7684\u6a5f\u7387\uff0c\u4e8c\u7a2e\u8a13\u7df4\u6a21 \u578b\u793a\u610f\u5716\u5982\u5716 2(a)\u53ca\u5716 2(b)\u6240\u793a\u3002\u5728", | |
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| "eq_spans": [], | |
| "section": "\u76f8\u95dc\u7814\u7a76 (Related Work)", | |
| "sec_num": "2." | |
| }, | |
| { | |
| "text": "Projection Output ", | |
| "cite_spans": [], | |
| "ref_spans": [], | |
| "eq_spans": [], | |
| "section": "Input", | |
| "sec_num": null | |
| }, | |
| { | |
| "text": "EQUATION", | |
| "cite_spans": [], | |
| "ref_spans": [], | |
| "eq_spans": [ | |
| { | |
| "start": 0, | |
| "end": 8, | |
| "text": "EQUATION", | |
| "ref_id": "EQREF", | |
| "raw_str": "W(t-2) W(t-1) W(t+1) W(t+2) Sum W(t) (a) Input Projection Output W(t-2) W(t-1) W(t+1) W(t+2) SUM W(t) (b) \u5716 2.", | |
| "eq_num": "(" | |
| } | |
| ], | |
| "section": "Input", | |
| "sec_num": null | |
| }, | |
| { | |
| "text": "\u672c\u8ad6\u6587\u5206\u5225\u63a1\u7528\u5377\u7a4d\u795e\u7d93\u7db2\u8def\u6216\u5feb\u901f\u6587\u672c\u4f86\u8a13\u7df4\u53ef\u8b80\u6027\u6a21\u578b\uff0c\u4e26\u8207\u904e\u53bb\u7684\u7814\u7a76\u9032\u884c\u6bd4\u8f03\u3002 \u53ef\u8b80\u6027\u6a21\u578b\u6e96\u78ba\u7387\u5982\u8868 2\uff0c\u800c\u932f\u8aa4\u77e9\u9663\u5206\u5225\u5982\u8868 3 \u53ca\u8868 4 \u6240\u793a\u3002\u9664\u4e86\u5448\u73fe\u6e96\u78ba\u7387\u4e4b\u5916\uff0c \u672c\u7814\u7a76\u4e5f\u653e\u5bec\u4e0a\u3001\u4e0b\u4e00\u500b\u5e74\u7d1a\u7684\u6a19\u6e96\u4f86\u7d71\u8a08\u51fa\u9130\u8fd1\u6e96\u78ba\u7387\uff0c\u4ee5\u89c0\u5bdf\u53ef\u8b80\u6027\u6a21\u578b\u932f\u8aa4\u9810\u6e2c \u7684\u7a0b\u5ea6\u662f\u5426\u56b4\u91cd\u3002\u6211\u5011\u53ef\u4ee5\u767c\u73fe\u4e0d\u8ad6\u662f\u4ee5\u985e\u795e\u7d93\u7db2\u8def\u6216\u662f\u5feb\u901f\u6587\u672c\u4f86\u8a13\u7df4\u53ef\u8b80\u6027\u6a21\u578b\uff0c \u5176 \u6e96 \u78ba \u7387 \u548c \u9130 \u8fd1 \u6e96 \u78ba \u7387 \u7686 \u6bd4 \u652f \u5411 \u91cf \u6a5f (Support Vector Machine, SVM) (Vapnik & Chervonenkis, 1974 ", | |
| "cite_spans": [ | |
| { | |
| "start": 224, | |
| "end": 252, | |
| "text": "(Vapnik & Chervonenkis, 1974", | |
| "ref_id": "BIBREF43" | |
| } | |
| ], | |
| "ref_spans": [], | |
| "eq_spans": [], | |
| "section": "4.3\u5be6\u9a57\u7d50\u679c (Results)", | |
| "sec_num": null | |
| } | |
| ], | |
| "back_matter": [], | |
| "bib_entries": { | |
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| "uris": null, | |
| "text": "a)\u9023\u7e8c\u8a5e\u888b\u6a21\u578b\u8a13\u7df4\u6f14\u7b97\u6cd5\u3002(b)\u7565\u8a5e\u6a21\u578b\u8a13\u7df4\u6f14\u7b97\u6cd5\u3002 [Figure 2. (a) The continuous bag-of-words model; (b) The skip-gram model.] \u7531\u4e0a\u8ff0\u53ef\u77e5\u7814\u7a76\u53ef\u77e5\u4e0d\u8ad6\u662f\u6f5b\u5728\u8a9e\u610f\u5206\u6790\u6216\u662f Word2vec\uff0c\u90fd\u53ef\u4ee5\u5728\u4e0d\u9700\u9818\u57df\u5c08\u5bb6\u7684 \u4ecb\u5165\u4e4b\u4e0b\uff0c\u4f9d\u64da\u5176\u6f14\u7b97\u6cd5\u81ea\u52d5\u5f9e\u6587\u672c\u4e2d\u62bd\u53d6\u51fa\u53ef\u8b80\u6027\u6a21\u578b\u6240\u9700\u8981\u7684\u7279\u5fb5\u3002\u8fd1\u5e74\u4f86\u8868\u793a\u5b78 \u7fd2\u6cd5\u4ecd\u84ec\u52c3\u767c\u5c55\uff0c\u56e0\u6b64\uff0c\u672c\u8ad6\u6587\u5c07\u5617\u8a66\u4ee5\u5377\u7a4d\u795e\u7d93\u7db2\u8def(Convolutional Neural Network, CNN) (LeCun, 1989)\u6216\u5feb\u901f\u6587\u672c(festText) (Joulin, Grave, Bojanowski & Mikolov, 2016)\u7b49 \u4e0d\u540c\u7684\u8868\u793a\u5b78\u7fd2\u6cd5\u4f86\u81ea\u52d5\u62bd\u53d6\u6587\u672c\u7279\u5fb5\u7684\u6280\u8853\uff0c\u8a13\u7df4\u51fa\u4e00\u500b\u80fd\u5920\u5206\u6790\u8de8\u9818\u57df\u6587\u4ef6\u7684\u53ef\u8b80 \u6027\u6a21\u578b\u3002", | |
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| "text": "Word2Vec \u4e2d\u4e0d\u8ad6\u662f\u9023\u7e8c\u8a5e\u888b\u6a21\u578b\u9084\u662f\u7565\u8a5e\u6a21\u578b\uff0c \u5728\u8f38\u51fa\u5c64\u90fd\u53ef\u4ee5\u63a1\u7528 Hierarchical Softmax \u6216\u662f Negative Sampling \u5169\u7a2e\u6a21\u5f0f\u4f86\u589e\u9032\u8a13\u7df4\u7684 \u6548\u80fd\u3002\u5176\u4e2d Hierarchical Softmax \u6307\u7684\u662f\u5c07\u8a13\u7df4\u8cc7\u6599\u4e2d\u4e0d\u540c\u8a5e\u5f59\u90fd\u5efa\u7f6e\u970d\u592b\u66fc\u6a39(Huffman tree)\u4e0a\uff0c\u4f7f\u5f97\u6839\u7bc0\u9ede(root)\u5230\u6bcf\u500b\u8a5e\u5f59\u90fd\u662f\u552f\u4e00\u7684\u8def\u5f91\uff0c\u63a5\u8457\u5728\u8a13\u7df4\u7684\u904e\u7a0b\u4e2d\uff0c\u4e0d\u65b7\u5f97\u66f4 \u7d93\u5c07 Word2vec \u61c9\u7528\u65bc\u53ef\u8b80\u6027\u6a21\u578b\uff0c\u4f8b\u5982 Liu \u7b49\u4eba\u4fbf\u5c07 Word2vec \u7576\u6210\u53ef\u8b80\u6027\u6a21\u578b\u88e1\u5176\u4e2d \u7684\u4e00\u500b\u7279\u5fb5\uff0c\u4ee5\u5206\u6790\u4e2d\u3001\u5c0f\u5b78\u570b\u6587\u79d1\u6559\u79d1\u66f8\u53ca\u512a\u826f\u8ab2\u5916\u8b80\u7269\u7684\u53ef\u8b80\u6027", | |
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| "content": "<table><tr><td>\u63a2\u7a76\u4f7f\u7528\u57fa\u65bc\u985e\u795e\u7d93\u7db2\u8def\u4e4b\u7279\u5fb5\u65bc\u6587\u672c\u53ef\u8b80\u6027\u5206\u985e</td><td>35</td></tr><tr><td colspan=\"2\">\u65b0\u970d\u592b\u66fc\u6a39\u4e0a\u6bcf\u500b\u7bc0\u9ede\u6240\u5c0d\u61c9\u7684\u6b0a\u91cd\u5916\uff0c\u4e5f\u9010\u6b65\u66f4\u65b0\u8a5e\u5f59\u6240\u5c0d\u61c9\u7684\u5411\u91cf\u3002\u800c Negative</td></tr><tr><td colspan=\"2\">Sampling \u5247\u662f\u6368\u68c4\u4e86\u970d\u592b\u66fc\u6a39\u7684\u4f5c\u6cd5\uff1b\u5728\u8a13\u7df4\u524d\u9664\u4e86\u539f\u672c\u7684\u6b63\u4f8b\u7684\u6a23\u672c\u5916\uff0c\u9084\u984d\u5916\u9078\u4e86</td></tr><tr><td colspan=\"2\">\u6578\u500b\u8ca0\u4f8b\u7684\u6a23\u672c\uff0c\u5728\u8a13\u7df4\u7684\u904e\u7a0b\u4e2d\u4e0d\u65b7\u66f4\u65b0\u6b0a\u91cd\uff0c\u4f7f\u5f97\u6b63\u4f8b\u6a23\u672c\u7684\u6a5f\u7387\u6700\u5927\u5316\u5916\uff0c\u4e5f\u540c</td></tr><tr><td colspan=\"2\">\u6642\u964d\u4f4e\u4e86\u8ca0\u4f8b\u6a23\u672c\u7684\u6a5f\u7387\uff0c\u8b93\u8a5e\u5f59\u6240\u5c0d\u61c9\u7684\u5411\u91cf\u53ef\u4ee5\u9010\u6b65\u7372\u5f97\u4fee\u6b63\u3002\u5728\u76ee\u524d\u4e5f\u6709\u5b78\u8005\u5df2</td></tr></table>", | |
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| "content": "<table><tr><td colspan=\"8\">\u63a2\u7a76\u4f7f\u7528\u57fa\u65bc\u985e\u795e\u7d93\u7db2\u8def\u4e4b\u7279\u5fb5\u65bc\u6587\u672c\u53ef\u8b80\u6027\u5206\u985e \u63a2\u7a76\u4f7f\u7528\u57fa\u65bc\u985e\u795e\u7d93\u7db2\u8def\u4e4b\u7279\u5fb5\u65bc\u6587\u672c\u53ef\u8b80\u6027\u5206\u985e</td><td>39 \u66fe\u539a\u5f37 \u7b49 41</td></tr><tr><td colspan=\"9\">Word2vec \u7686\u53ef\u4ee5\u6709\u6548\u7684\u8868\u5fb5\u4e0d\u540c\u9818\u57df\u7684\u6587\u672c\u4f86\u7576\u6210\u53ef\u8b80\u6027\u7279\u5fb5\uff0c\u4f7f\u5f97\u8a13\u7df4\u51fa\u4f86\u7684\u53ef\u8b80\u6027 \u6a21\u578b\u53ef\u4ee5\u5177\u6709\u9818\u57df\u4e00\u822c\u5316\u7684\u80fd\u529b\u3002 \u8868 2. \u57fa\u65bc\u5377\u7a4d\u795e\u7d93\u7db2\u8def\u53ca\u5feb\u901f\u6587\u672c\u4e4b\u53ef\u8b80\u6027\u6a21\u578b\u6548\u80fd\u6bd4\u8f03 \u9069\u7528\u5e74\u7d1a \u9069\u7528\u9818\u57df \u53ef\u8b80\u6027\u7279\u5fae \u5206\u985e\u5668 \u6e96\u78ba\u7387 \u9130\u8fd1 \u6e96\u78ba\u7387 1-12 \u5e74\u7d1a \u570b\u8a9e\u79d1\u3001\u793e\u6703\u79d1\u3001 \u81ea\u7136\u79d1\u53ca\u9ad4\u80b2\u548c \u5065\u5eb7\u6559\u80b2\u5171\u8a08 6,230 \u7bc7 \u5377\u7a4d\u795e\u7d93\u7db2\u8def \u985e\u795e\u7d93\u7db2\u8def 67.62% 86.76% \u8868 4. \u5feb\u901f\u6587\u672c\u53ef\u8b80\u6027\u6a21\u578b\u4e4b\u932f\u8aa4\u77e9\u9663 [Table 4. Confusion Matrices of the fastText Readability Model.] \u6a21\u578b\u9810\u4f30\u5e74\u7d1a \u6e96\u78ba\u7387(%) 1 2 3 4 5 6 7 8 9 10 11 12 \u5be6 \u969b \u5e74 1 87 47 12 3 0 0 0 0 0 0 0 0 58.39 2 46 97 43 6 0 0 0 0 0 0 0 0 \u53ef\u8b80\u6027\u7279\u5fb5 \u8868 5\u9069\u7528\u5e74\u7d1a \u9069\u7528\u9818\u57df \u5206\u985e\u5668-\u985e \u795e\u7d93\u7db2\u8def Word2vec \u5377\u7a4d\u985e\u795e\u7d93\u7db2\u8def (Tseng et al., 2016b) 50.52 3 28 15 219 52 12 6 1 0 0 0 1 0 65.57 \u5c64\u6578\u91cf \u6e96\u78ba\u7387(%) \u9130\u8fd1\u6e96\u78ba\u7387(%) \u6e96\u78ba\u7387 \u9130\u8fd1\u6e96\u78ba\u7387 (%) (%) \u7d1a 4 10 5 62 209 33 25 10 1 1 0 0 0 58.71 \u570b\u8a9e\u3001\u793e 1 67.62 86.76 66.95 85.26 (\u4e00\u5c64) \u5feb\u901f\u6587\u672c 69.63% 86.01% Word2vec \u652f\u5411\u91cf\u6a5f 61.33% 82.2% 5 2 5 28 43 184 75 27 3 0 2 0 1 49.73 6 0 4 22 23 79 188 18 5 13 7 2 2 1-12 \u5e74\u7d1a \u6703\u3001\u81ea\u7136\u3001 2 67.09 85.99 68.59 86.11 \u9ad4\u80b2\u548c\u5065 51.79 7 1 1 3 2 19 22 560 26 5 28 2 8 82.72 \u5eb7\u6559\u80b2\u5171 3 66.5 86.31 68.33 85.54 \u8a08 6,230 \u7bc7 (Tseng et al., 2016b) Word2vec 8 10 0 0 1 12 21 28 571 12 32 7 13 80.76 \u985e\u795e\u7d93\u7db2\u8def (\u4e00\u5c64) 66.95% 9 10 0 0 0 8 18 24 11 461 36 16 11 5. \u7d50\u8ad6 (Conclusions and Future work) 77.48 85.26% 10 3 4 0 1 7 11 47 39 13 555 95 57 66.71 \u904e\u53bb\u53ef\u8b80\u6027\u6a21\u578b\u6240\u63a1\u7528\u7684\u7279\u5fb5\u5927\u591a\u9700\u8981\u5c08\u5bb6\u53bb\u8a2d\u8a08\uff0c\u6709\u8457\u8017\u6642\u8cbb\u529b\u7b49\u554f\u984c\u3002\u6709\u9451\u65bc\u6b64\uff0c (Tseng et al., 2016b) 11 0 1 0 0 4 4 16 20 24 113 619 65 \u672c\u8ad6\u6587\u57fa\u65bc\u8868\u793a\u5b78\u7fd2\u6f14\u7b97\u6cd5\uff0c\u63d0\u51fa\u4ee5\u5377\u7a4d\u985e\u795e\u7d93\u7db2\u8def\u6216\u5feb\u901f\u6587\u672c\u4f86\u81ea\u52d5\u62bd\u53d6\u6587\u672c\u7684\u7279\u5fb5 71.48 \u8868 3. \u5377\u7a4d\u795e\u7d93\u7db2\u8def\u53ef\u8b80\u6027\u6a21\u578b\u4e4b\u932f\u8aa4\u77e9\u9663 [Table 3. Confusion Matrices of the Convolutional Neural Network based Readability 12 1 3 0 0 1 4 11 6 4 84 87 588 \u53bb\u8a13\u7df4\u53ef\u8b80\u6027\u6a21\u578b\uff0c\u4e26\u4ee5\u5be6\u8b49\u8b49\u660e\u5176\u6548\u80fd\u8207\u5177\u9818\u57df\u4e00\u822c\u5316\u7684\u80fd\u529b\u3002\u9664\u6b64\u4e4b\u5916\uff0c\u4ee5\u672c\u7814\u7a76 74.52 \u7684\u5be6\u9a57\u6750\u6599\u800c\u8a00\uff0c\u8207\u652f\u5411\u91cf\u6a5f\u540c\u5c6c\u65bc\u6dfa\u5c64\u7d50\u69cb\u7684\u6a5f\u68b0\u5b78\u7fd2\u6f14\u7b97\u6cd5\uff1a\u5feb\u901f\u6587\u672c\uff0c\u5176\u6548\u80fd\u4e26 Model.] \u6b64\u5916\uff0c\u672c\u8ad6\u6587\u4e5f\u91dd\u5c0d\u5377\u7a4d\u985e\u795e\u7d93\u7db2\u8def\u7684\u53ef\u8b80\u6027\u6a21\u578b\u53bb\u52a0\u6df1\u985e\u795e\u7d93\u7db2\u8def\u7684\u5c64\u6578\uff0c\u4ee5\u8ddf \u4e0d\u8f38\u7d66\u6df1\u5c64\u7d50\u69cb\u7684\u6a5f\u68b0\u5b78\u7fd2\u6f14\u7b97\u6cd5\u3002\u91dd\u5c0d\u6b64\u9ede\u767c\u73fe\uff0c\u672c\u7814\u7a76\u672a\u4f86\u5c07\u6703\u7d0d\u5165\u66f4\u591a\u7684\u8a13\u7df4\u8cc7 \u6a21\u578b\u9810\u4f30\u5e74\u7d1a \u904e\u53bb\u5b78\u8005\u7684\u7d50\u679c\u9032\u884c\u6bd4\u8f03\u3002\u5176\u7d50\u679c\u5982\u8868 5 \u6240\u793a\uff0c\u672c\u7814\u7a76\u767c\u73fe\uff0c\u4ee5\u5377\u7a4d\u985e\u795e\u7d93\u7db2\u8def\u70ba\u7279\u5fb5 \u6599\u53ca\u4e0d\u540c\u6df1\u3001\u6dfa\u5c64\u7d50\u69cb\u7684\u6a5f\u68b0\u5b78\u7fd2\u6f14\u7b97\u6cd5\u4f86\u52a0\u4ee5\u63a2\u8a0e\u5c0d\u65bc\u53ef\u8b80\u6027\u6a21\u578b\u7684\u5f71\u97ff\u3002 \u6e96\u78ba\u7387(%) 1 2 3 4 5 6 7 8 9 10 11 12 \u5be6 \u969b \u5e74 \u7d1a 1 74 52 21 0 1 0 0 0 0 0 0 1 49.66 2 42 92 42 13 2 0 0 0 1 0 0 0 47.92 3 6 36 197 63 17 11 1 2 1 0 0 0 58.98 4 4 13 51 223 40 20 5 0 0 0 0 0 \u7684\u53ef\u8b80\u6027\u6a21\u578b\uff0c\u5176\u6548\u80fd\u4e26\u672a\u96a8\u8457\u985e\u795e\u7d93\u5206\u985e\u5668\u7684\u5c64\u6578\u589e\u52a0\u800c\u4e0a\u5347\uff0c\u4e14\u6700\u4f73\u7684\u6e96\u78ba\u7387\u4f4e\u65bc \u9664\u6b64\u4e4b\u5916\uff0c\u672c\u7814\u7a76\u4e5f\u767c\u73fe\u4e0d\u540c\u67b6\u69cb\u7684\u53ef\u8b80\u6027\u6a21\u578b\u6240\u5448\u73fe\u51fa\u4f86\u7684\u7d50\u679c\u6709\u5f88\u5927\u7684\u5dee\u7570\uff0c Word2vec \u70ba\u7279\u5fb5\u7684\u53ef\u8b80\u6027\u6a21\u578b 0.97%\u3002\u7136\u800c\uff0c\u5176\u9130\u8fd1\u6e96\u78ba\u7387\u537b\u53cd\u800c\u9ad8\u904e 0.65%\u3002\u4ee5\u6574\u9ad4 \u5982\u5feb\u901f\u6587\u672c\u6e96\u78ba\u7387\u96d6\u7136\u662f\u6700\u9ad8\u7684\uff0c\u4f46\u5f9e\u8868 3 \u548c\u8868 4 \u7684\u6bd4\u8f03\u53ef\u4ee5\u767c\u73fe\uff0c\u5feb\u901f\u6587\u672c\u5c0d\u65bc\u67d0\u4e9b \u800c\u8a00\uff0c\u4ee5 Word2vec \u70ba\u7279\u5fb5\u7684\u53ef\u8b80\u6027\u6a21\u578b\u5176\u6e96\u78ba\u7387\u662f\u6bd4\u8f03\u9ad8\u7684\uff0c\u4f46\u4ee5\u5377\u7a4d\u985e\u795e\u7d93\u7db2\u8def\u70ba \u5e74\u7d1a\u7684\u6587\u672c\u5728\u9810\u6e2c\u932f\u8aa4\u6642\uff0c\u5176\u932f\u8aa4\u8aa4\u5dee\u7684\u7a0b\u5ea6\u975e\u5e38\u56b4\u91cd\uff1b\u53cd\u89c0\u5377\u7a4d\u985e\u795e\u7d93\u7db2\u8def\u9810\u6e2c\u932f\u8aa4 \u7279\u5fb5\u7684\u53ef\u8b80\u6027\u6a21\u578b\u5176\u9130\u8fd1\u6e96\u78ba\u7387\u662f\u8f03\u9ad8\u7684\u3002\u6700\u5f8c\u7d9c\u5408\u8868 2 \u548c\u8868 5 \u800c\u8a00\uff0c\u6211\u5011\u53ef\u4ee5\u767c\u73fe\u5feb \u8aa4\u5dee\u7684\u7a0b\u5ea6\u5c31\u76f8\u5c0d\u96c6\u4e2d\u3002\u800c\u91dd\u5c0d\u6a21\u578b\u9810\u6e2c\u90e8\u5206\u6587\u672c\u7522\u751f\u56b4\u91cd\u7684\u8aa4\u5dee\uff0c\u672c\u7814\u7a76\u8a8d\u70ba\u53ef\u80fd\u7684 \u901f\u6587\u672c\u7684\u6e96\u78ba\u7387\u4ecd\u662f\u6240\u6709\u53ef\u8b80\u6027\u6a21\u578b\u4e2d\u6700\u9ad8\u7684\uff0c\u4f46\u9130\u8fd1\u6e96\u78ba\u7387\u537b\u4e5f\u662f\u6700\u4f4e\u7684\u3002 \u539f\u56e0\u662f\uff1a\u5c0d\u65bc\u9ad4\u80b2\u548c\u5065\u5eb7\u6559\u80b2\u9019\u500b\u9818\u57df\u7684\u6559\u79d1\u66f8\u800c\u8a00\uff0c\u70ba\u4e86\u8b93\u570b\u5c0f\u4f4e\u5e74\u7d1a\u7684\u5e7c\u7ae5\u53ef\u4ee5\u76e1 62.64 \u65e9\u8a8d\u8b58\u8207\u81ea\u5df1\u5207\u8eab\u76f8\u95dc\u7684\u77e5\u8b58\uff0c\u5982\uff1a\u8eab\u9ad4\u69cb\u9020\u3001\u8eab\u9ad4\u81ea\u4e3b\u6b0a\u53ca\u751f\u6d3b\u74b0\u5883\u3001\u75be\u75c5\u2026\u7b49\u7b49\u8b70</td></tr><tr><td colspan=\"9\">5 \u984c\u3002\u96d6\u7136\u9063\u8a5e\u7528\u5b57\u65e9\u5c31\u8d85\u904e\u8a72\u5e74\u7d1a\u7684\u8b58\u5b57\u96e3\u5ea6(\u4ee5\u570b\u6587\u79d1\u76f8\u61c9\u5e74\u7d1a\u8ab2\u6587\u6240\u6559\u6388\u7684\u751f\u5b57\u800c 0 1 22 54 165 88 29 4 2 3 1 1 44.59</td></tr><tr><td colspan=\"9\">6 \u8a00)\uff0c\u4f46\u7d93\u7531\u8001\u5e2b\u7684\u4ecb\u7d39\u53ca\u5716\u7247\u548c\u6ce8\u97f3\u7684\u8f14\u52a9\uff0c\u4f7f\u5f97\u5b78\u751f\u662f\u53ef\u4ee5\u7406\u89e3\u6587\u672c\u7684\u5167\u5bb9\u3002\u76f8\u8f03\u4e4b 0 2 7 34 95 176 18 6 10 11 1 3 48.48</td></tr><tr><td colspan=\"9\">7 \u4e0b\uff0c\u8868\u5fb5\u5b78\u7fd2\u6cd5\u55ae\u7d14\u5f9e\u6587\u5b57\u6240\u7372\u5f97\u7684\u8cc7\u8a0a\u5c31\u76f8\u7576\u6709\u9650\uff0c\u56e0\u6b64\u7576\u4e0a\u8ff0\u9019\u4e9b\u4f4e\u5e74\u7d1a\u7684\u6587\u672c\u7576 2 0 1 2 16 26 526 36 26 39 1 2 77.70</td></tr><tr><td colspan=\"9\">8 \u6210\u8a13\u7df4\u8cc7\u6599\u6642\uff0c\u4e00\u4e9b\u9ad8\u5e74\u7d1a\u7684\u6e2c\u8a66\u8cc7\u6599\u5982\u679c\u7528\u5b57\u662f\u7c21\u55ae\u6642(\u5982\uff1a\u767d\u8a71\u6587\u3001\u4ecb\u7d39\u9ad4\u80b2\u5668\u6750\u3001 0 0 0 2 3 29 42 540 31 43 12 5 76.38 9 1 0 0 0 1 23 29 29 454 41 15 2 \u4ecb\u7d39\u904b\u52d5\u898f\u5247\u2026\u7b49\u7b49)\uff0c\u9019\u4e9b\u6587\u672c\u5f88\u5bb9\u6613\u88ab\u8aa4\u5224\u6210\u570b\u5c0f\u4f4e\u5e74\u7d1a\u5c31\u53ef\u4ee5\u95b1\u8b80\u3002\u56e0\u6b64\u5728\u672a\u4f86\u7684 76.30 \u7814\u7a76\u4e2d\uff0c\u9664\u4e86\u6574\u5408\u4e0d\u540c\u985e\u578b\u7684\u985e\u795e\u7d93\u7db2\u8def\u6a21\u578b\u7684\u512a\u9ede\u4f86\u4fc3\u4f7f\u53ef\u8b80\u6027\u6a21\u578b\u5728\u9810\u6e2c\u932f\u8aa4\u6642\uff0c</td></tr><tr><td colspan=\"9\">10 5 0 \u5176\u8aa4\u5dee\u4e5f\u80fd\u5920\u76e1\u53ef\u80fd\u7684\u5f80\u9069\u8b80\u5e74\u7d1a\u96c6\u4e2d\u5916\uff1b\u4e5f\u5c07\u7d0d\u5165\u66f4\u591a\u7684\u7279\u5fb5\u4ee5\u8f14\u52a9\u76ee\u524d\u53ef\u8b80\u6027\u6a21\u578b 0 0 5 15 26 23 19 586 92 61 70.43</td></tr><tr><td>11 0 0 \u4e0d\u8db3\u7684\u5730\u65b9\u3002</td><td>0</td><td>0</td><td>2</td><td>7</td><td>7</td><td>9</td><td>9 142 630 60</td><td>72.75</td></tr><tr><td>12 4 0</td><td>0</td><td>0</td><td>0</td><td>5</td><td>8</td><td>4</td><td>3 122 93 550</td><td>69.71</td></tr><tr><td/><td colspan=\"8\">)\u9084\u597d\u3002\u800c\u6211\u5011\u4e5f\u53ef\u4ee5\u767c\u73fe\u4e0d\u8ad6\u662f\u63a1\u7528\u5377\u7a4d\u795e\u7d93\u7db2\u8def\u3001\u5feb\u901f\u6587\u672c\u53ca</td></tr></table>", | |
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