| { |
| "paper_id": "O13-1001", |
| "header": { |
| "generated_with": "S2ORC 1.0.0", |
| "date_generated": "2023-01-19T08:04:06.594348Z" |
| }, |
| "title": "", |
| "authors": [], |
| "year": "", |
| "venue": null, |
| "identifiers": {}, |
| "abstract": "", |
| "pdf_parse": { |
| "paper_id": "O13-1001", |
| "_pdf_hash": "", |
| "abstract": [], |
| "body_text": [ |
| { |
| "text": "EQUATION", |
| "cite_spans": [], |
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| "start": 0, |
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| "text": "EQUATION", |
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| "raw_str": "1\u3001\u6587\u4ef6\u76f8\u4f3c\u5ea6\u91cf\u503c \u6211\u5011\u53ef\u4ee5\u628a\u8a9e\u97f3\u6587\u4ef6\u6458\u8981\u4efb\u52d9\u8996\u70ba\u662f\u8cc7\u8a0a\u6aa2\u7d22\u7684\u554f\u984c\u3002\u4e00\u822c\u4f86\u8aaa\uff0c\u8cc7\u8a0a\u6aa2\u7d22(Information Retrieval, IR)\u65e8\u5728\u5c0b\u627e\u76f8\u95dc\u6587\u4ef6(Relevant Document)\u4f86\u56de\u61c9\u4f7f\u7528\u8005\u6240\u9001\u51fa\u7684\u67e5\u8a62(Query) \u6216\u8cc7\u8a0a\u9700\u6c42(Information Need)\u3002\u540c\u6a23\u5730\uff0c\u5728\u5f9e\u4e8b\u8a9e\u97f3\u6587\u4ef6\u6458\u8981\u6642\uff0c\u6211\u5011\u53ef\u5c07\u6bcf\u4e00\u7bc7\u88ab\u6458 \u8981\u6587\u4ef6\u8996\u70ba\u662f\u67e5\u8a62\uff0c\u800c\u6587\u4ef6\u4e2d\u7684\u8a9e\u53e5(Sentence)\u8996\u70ba\u5019\u9078\u8cc7\u8a0a\u55ae\u5143(Candidate Information Unit)\uff1b\u64da\u6b64\uff0c\u6211\u5011\u53ef\u4ee5\u5047\u8a2d\u5728\u88ab\u6458\u8981\u6587\u4ef6\u4e2d\uff0c\u8207\u5176\u6108\u76f8\u95dc\u7684\u8a9e\u53e5\u6108\u6709\u53ef\u80fd\u662f\u53ef\u7528\u4f86\u4ee3 \u8868\u6587\u4ef6\u4e3b\u65e8\u6216\u4e3b\u984c\u4e4b\u6458\u8981\u8a9e\u53e5\u3002 \u7576\u7d66\u4e88\u4e00\u7bc7\u88ab\u6458\u8981\u6587\u4ef6 D \u6642\uff0c\u6587\u4ef6\u4e2d\u6bcf\u4e00\u8a9e\u53e5 S \u7684\u4e8b\u5f8c\u6a5f\uf961 ) | ( D S P \u53ef\u4ee5\u7528\u4f86\u8868\u793a \u8a9e\u53e5 S \u5c0d\u65bc\u6587\u4ef6 D \u7684\u91cd\u8981\u6027\u3002\u7576\u4f7f\u7528\u8a9e\u8a00\u6a21\u578b\u4f86\u8a08\u7b97 ) | ( D S P \u6642\uff0c\u6211\u5011\u900f\u904e\u8c9d\u6c0f\u5b9a\u7406 (Bayes' Theorem)\u5c07 ) | ( D S P \u5c55\u958b\u6210[5]\uff1a ) ( ) ( ) | ( ) | ( D P S P S D P D S P \uf03d (1) \u5176\u4e2d ) (D P \u662f\u6587\u4ef6 D \u7684\u4e8b\u524d\u6a5f\uf961\uff0c\u7531\u65bc ) (D P \u4e0d\u5f71\u97ff\u8a9e\u53e5\u7684\u6392\u5e8f\u7d50\u679c\uff0c\u6545\u53ef\u7701\u7565\u4e0d\u8a0e\u8ad6\uff1b \u53e6\u4e00\u65b9\u9762\uff0c ) (S P \u662f\u8a9e\u53e5 S \u7684\u4e8b\u524d\u6a5f\u7387\uff0c\u53ef\u4ee5\u4f7f\u7528\u5404\u5f0f\u975e\u76e3\u7763\u5f0f\u65b9\u6cd5\u6216\u76e3\u7763\u5f0f\u65b9\u6cd5\u4f86\u6c42 \u5f97[5]\u3002\u672c\u8ad6\u6587\u7684\u7814\u7a76\u5047\u8a2d\u8a9e\u53e5\u7684\u4e8b\u524d\u6a5f\uf961\u70ba\u4e00\u500b\u5747\u52fb\u5206\u5e03(Uniform Distribution)\uff0c\u6240\u4ee5 ) (S P \u4ea6\u53ef\u7701\u7565\u3002\u6700\u5f8c\uff0c ) | ( S D P \u662f\u8a9e\u53e5 S \u6240\u5f62\u6210\u7684\u8a9e\u8a00\u6a21\u578b\u751f\u6210\u6587\u4ef6 D \u4e4b\u6a5f\uf961(\u6216\u7a31\u4f5c \u6587\u4ef6\u76f8\u4f3c\u5ea6)\uff0c\u53ef\u4ee5\u7528\u4f86\u8868\u793a\u6587\u4ef6 D \u8207\u8a9e\u53e5 S \u4e4b\u9593\u7684\u76f8\u4f3c\u95dc\u4fc2\uff0c\u5982\u679c\u8a9e\u53e5 S \u751f\u6210\u6587\u4ef6 D \u7684 \u6a5f\u7387\u503c\u6108\u9ad8\uff0c\u4ee3\u8868\u8a9e\u53e5 S \u8207\u6587\u4ef6 D \u6108\u70ba\u76f8\u4f3c(\u8a9e\u53e5\u6108\u80fd\u4ee3\u8868\u6587\u4ef6 D )\uff0c\u5373\u6108\u6709\u53ef\u80fd\u662f\u6458\u8981 \u8a9e\u53e5\u3002\u6211\u5011\u53ef\u4ee5\u66f4\u9032\u4e00\u6b65\u5730\u5047\u8a2d\u6587\u4ef6 D \u4e2d\u8a5e\u8207\u8a5e\u4e4b\u9593\u662f\u7368\uf9f7\u7684\uff0c\u4e26\u4e14\u4e0d\u8003\u616e\u6bcf\u4e00\u500b\u8a5e\u5728 \u6587\u4ef6 D \u4e2d\u767c\u751f\u7684\u9806\u5e8f\u95dc\u4fc2(\u5373\u8a5e\u888b\u5047\u8a2d(Bag-of-Word Assumption))\uff0c\u5247\u8a9e\u53e5 S \u751f\u6210\u6587\u4ef6 D \u7684\u6587\u4ef6\u76f8\u4f3c\u5ea6\u91cf\u503c(Document Likelihood Measure, DLM) ) | ( S D P \u53ef\u62c6\u89e3\u6210\u6587\u4ef6 D \u4e2d\u6bcf\u4e00 \u7684\u8a5e w \u500b\u5225\u767c\u751f\u7684\u689d\u4ef6\u6a5f\uf961\u4e4b\u9023\u4e58\u7a4d\uff1a \uf0d5 \uf0ce \uf03d D w D w C S w P S D P ) , ( ) | ( ) | (", |
| "eq_num": "(2)" |
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| "section": "", |
| "sec_num": null |
| }, |
| { |
| "text": "EQUATION", |
| "cite_spans": [], |
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| "start": 0, |
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| "text": "EQUATION", |
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| "raw_str": "\u6b64\u7a2e\u65b9\u6cd5\u662f\u70ba\u8a9e\u53e5 S \u5efa\uf9f7\u4e00\u500b\u8a9e\u53e5\u6a21\u578b(Sentence Model) ) | ( S w P \uff0c w \u662f\u51fa\u73fe\u5728\u6587\u4ef6 D \u4e2d \u7684\u8a5e\uff0c ) , ( D w C \u662f\u8a5e w \u51fa\u73fe\u5728\u6587\u4ef6 D \u4e2d\u7684\u6b21\u6578\u3002\u5176\u4e2d\uff0c\u6211\u5011\u53ef\u5229\u7528\u6700\u5927\u5316\u76f8\u4f3c\u5ea6\u4f30\u6e2c (Maximum Likelihood Estimation, MLE)\u7684\u65b9\u5f0f\u4f86\u5efa\u7acb\u6bcf\u4e00\u500b\u8a9e\u53e5\u7684\u8a9e\u53e5\u6a21\u578b\uff1a | | ) , ( ) | ( S S w C S w P \uf03d (3) \u5728(3)\u4e2d\uff0c ) , ( S w C \u8868\u793a\u8a5e w \u5728\u8a9e\u53e5 S \u4e2d\u51fa\u73fe\u7684\u6b21\u6578\uff0c S \u5247\u8868\u793a\u8a9e\u53e5 S \u7684\u7e3d\u8a5e\u6578\u3002\u503c\u5f97\u6ce8 \u610f\u7684\u662f\uff0c\u7531\u65bc\u8a9e\u53e5 S \u901a\u5e38\u50c5\u7531\u5c11\u6578\u5b57\u8a5e\u6240\u7d44\u6210\uff0c\u56e0\u6b64\u5bb9\u6613\u906d\u9047\u8cc7\u6599\u7a00\u758f(Data Sparseness) \u7684\u554f\u984c\uff0c\u9019\u6703\u4f7f\u5f97\u8a9e\u53e5\u6a21\u578b\u4f7f\u7528\u6700\u5927\u5316\u76f8\u4f3c\u5ea6\u4f30\u6e2c\u6642\uff0c\u4e0d\u50c5\u53ef\u80fd\u7121\u6cd5\u6e96\u78ba\u5730\u4f30\u6e2c\u6bcf\u4e00\u500b \u8a5e\u5728\u8a9e\u53e5\u4e2d\u771f\u6b63\u7684\u6a5f\uf961\u5206\u4f48\uff0c\u4e5f\u53ef\u80fd\u56e0\u70ba\u67d0\u4e9b\u8a5e\u7684\u689d\u4ef6\u6a5f\u7387\u503c\u70ba\u96f6\uff0c\u5c0e\u81f4\u8a9e\u53e5 S \u7522\u751f\u6587 \u4ef6 D \u7684\u6a5f\uf961\u503c\u70ba\u96f6\u3002\u70ba\u4e86\u6e1b\u8f15\u4e0a\u8ff0\u7684\u73fe\u8c61\uff0c\u672c\uf941\u6587\u4f7f\u7528 Jelinek-Mercer \u5e73\u6ed1\u5316(Smoothing) \u6280\u8853\u85c9\u7531\u4f7f\u7528\u4ee5\u5927\u91cf\u6587\u5b57\u8a9e\u6599\u8a13\u7df4\u800c\u6210\u7684\u80cc\u666f\u55ae\u9023\u8a9e\u8a00\u6a21\u578b(Background Unigram Language Model)\u4f86\u8abf\u9069\u8a9e\u53e5\u6a21\u578b[35]\uff0c\u6545 ) | ( S D P \u53ef\u9032\u4e00\u6b65\u5730\u8868\u793a\u6210\uff1a \uf0d5 \uf0ce \uf0d7 \uf02d \uf02b \uf0d7 \uf03d D w D w C B w P S w P S D P ) , ( )] | ( ) 1 ( ) | ( [ ) | ( \uf06c \uf06c (4) \u5176\u4e2d\uff0c ) | ( B w P \u662f\u8a5e w \u5728\u80cc\u666f\u55ae\u9023\u8a9e\u8a00\u6a21\u578b B \u4e2d\u4e4b\u6a5f\uf961\u503c\u3002 2\u3001\u5eab\u723e\u8c9d\u514b-\u840a\u4f2f\u52d2\u96e2\u6563\u5ea6\u91cf\u503c \u8a9e\u8a00\u6a21\u578b\u4f7f\u7528\u65bc\u6587\u4ef6\u6458\u8981\u7684\u7814\u7a76\u4e2d\uff0c\u9664\u4e86\u53ef\u88ab\u7528\u65bc\u8a08\u7b97\u8a9e\u53e5\u751f\u6210\u6587\u4ef6\u7684\u53ef\u80fd\u6027\u5916\uff0c\u53e6\u4e00 \u7a2e\u65b9\u5f0f\u70ba\u85c9\u7531\u5eab\u723e\u8c9d\u514b-\u840a\u4f2f\u52d2\u96e2\u6563\u5ea6\u91cf\u503c(Kullback-Leibler Divergence Measure, KL)\uff0c \u4f86\u8a55\u4f30\u6587\u4ef6\u4e2d\u6bcf\u4e00\u500b\u8a9e\u53e5\u7684\u91cd\u8981\u6027\u3002\u7576\u4f7f\u7528\u5eab\u723e\u8c9d\u514b-\u840a\u4f2f\u52d2\u96e2\u6563\u5ea6\u91cf\u503c\u65bc\u6458\u8981\u4efb\u52d9 \u4e2d\uff0c\u88ab\u6458\u8981\u6587\u4ef6 D \u548c D \u4e2d\u7684\u6bcf\u4e00\u500b\u8a9e\u53e5 S \u90fd\u5c07\u5206\u5225\u88ab\u63cf\u8ff0\u70ba\u4e00\u500b\u55ae\u9023\u8a9e\u8a00\u6a21\u578b\uff1b\u7576\u76f8\u5c0d \u65bc\u88ab\u6458\u8981\u6587\u4ef6 D \u7684\u6587\u4ef6\u6a21\u578b(Document Model)\uff0c\u8a9e\u53e5\u6a21\u578b\u7684\u96e2\u6563\u5ea6\u91cf\u503c\u6108\u5c0f\u6642\uff0c\u5247\u4ee3\u8868 \u8a9e\u53e5\u8207\u6587\u4ef6\u6108\u76f8\u95dc\uff0c\u4ea6\u5373\u8a9e\u53e5 S \u6108\u91cd\u8981\u3002\u5728\u6b64\u6458\u8981\u67b6\u69cb\u4e0b\uff0c\u6392\u5e8f\u8a9e\u53e5\u91cd\u8981\u6027\u7684\u516c\u5f0f\u5982\u4e0b [18]\uff1a \uf0e5 \uf0ce \uf03d V w S w P D w P D w P S D KL ) | ( ) | ( log ) | ( ) || (", |
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| "text": "EQUATION", |
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| "text": "EQUATION", |
| "ref_id": "EQREF", |
| "raw_str": "\u5176\u4e2d\uff0cV (Vocabulary)\u8868\u793a\u4e00\u500b\u7531\u8a9e\u8a00\u88e1\u6240\u6709\u53ef\u80fd\u7684\u8a9e\u5f59\u6240\u5f62\u6210\u7684\u96c6\u5408\u3002\u672c\u8ad6\u6587\u7684\u7814\u7a76 \u4e2d\uff0c\u6587\u4ef6\u6a21\u578b ) | ( D w P \u7684\u5efa\u7acb\u65b9\u5f0f\u8207\u8a9e\u53e5\u6a21\u578b\u76f8\u540c(\u53c3\u7167\u5f0f(3))\u3002\u7576\u6211\u5011\u66f4\u9032\u4e00\u6b65\u5730\u5c0d(5) \u4f5c\u5206\u6790\u6642\uff0c\u53ef\u4ee5\u767c\u73fe\u7576\u6587\u4ef6\u6a21\u578b\u50c5\u4f7f\u7528\u6700\u5927\u5316\u76f8\u4f3c\u5ea6\u4f30\u6e2c(MLE)\u7684\u524d\u63d0\u4e0b\uff0c\u63a1\u7528\u5eab\u723e\u8c9d \u514b-\u840a\u4f2f\u52d2\u96e2\u6563\u5ea6\u91cf\u503c\u6240\u5f97\u5230\u7684\u8a9e\u53e5\u6392\u5e8f\u5c07\u8207\u4f7f\u7528\u6587\u4ef6\u53ef\u80fd\u6027(Document Likelihood)\u6e2c \u91cf\u65b9\u5f0f(\u5373\u6587\u4ef6\u76f8\u4f3c\u5ea6\u91cf\u503c)\u6240\u5f97\u5230\u7684\u7d50\u679c\u662f\u76f8\u540c\u7684\uff0c\u5176\u63a8\u5c0e\u5982\u4e0b[6]\uff1a ) | ( ) | ( log ) | ( log ) , ( ) | ( log | | ) , ( ) | ( log ) | ( ) || ( S D P S D P S w P D w C S w P D D w C S w P D w P S D KL rank V w rank V w V w rank \uf03d \uf03d \uf03d \uf03d \uf03d \uf02d \uf0e5 \uf0e5 \uf0e5 \uf0ce \uf0ce \uf0ce (6) \u7531\u65bc\u4f7f\u7528\u5eab\u723e\u8c9d\u514b-\u840a\u4f2f\u52d2\u96e2\u6563\u5ea6\u91cf\u503c\u6642\uff0c\u4e0d\u50c5\u8a9e\u53e5\u88ab\u8868\u793a\u6210\u8a9e\u53e5\u6a21\u578b\uff0c\u6bcf\u4e00\u7bc7\u88ab\u6458\u8981 \u6587\u4ef6 D \u4ea6\u88ab\u8996\u70ba\u4e00\u500b\u6587\u4ef6(\u6a5f\uf961)\u6a21\u578b\uff0c\u800c\u6587\u4ef6\u6a21\u578b\u5728\u7d93\u7531\u5404\u5f0f\u8a9e\u8a00\u6a21\u578b\u8abf\u9069\u8207\u5e73\u6ed1\u5316\u7684 \u6280\u5de7\u4e0b\uff0c\u53ef\u4ee5\u6709\u7cfb\u7d71\u5730\u3001\u9069\u7576\u5730\u8abf\u9069\u6587\u4ef6\u6a21\u578b\u7684\u6a5f\uf961\u5206\u4f48\uff1b\u56e0\u6b64\u76f8\u8f03\u65bc\u6587\u4ef6\u76f8\u4f3c\u5ea6\u91cf\u503c (DLM)\u53ea\u80fd\u91dd\u5c0d\u8a9e\u53e5\u6a21\u578b\u9032\u884c\u8abf\u9069\uff0c\u5eab\u723e\u8c9d\u514b-\u840a\u4f2f\u52d2\u96e2\u6563\u5ea6\u91cf\u503c(KL)\u80fd\u900f\u904e\u4e0d\u540c\u6a21\u578b \u53c3\u6578\u4f30\u6e2c\u6280\u8853\u7684\u4f7f\u7528\u800c\u7372\u5f97\u66f4\u4f73\u7684\u81ea\u52d5\u6458\u8981\u6548\u80fd\u3002 3\u3001\u660e\u78ba\u5ea6\u91cf\u503c \u6587\u4ef6\u76f8\u4f3c\u5ea6\u91cf\u503c(DLM)\u8207\u5eab\u723e\u8c9d\u514b-\u840a\u4f2f\u52d2\u96e2\u6563\u5ea6\u91cf\u503c(KL)\u7686\u8457\u91cd\u65bc\u63a2\u8a0e\u8a9e\u53e5\u8207\u6587\u4ef6\u4e4b \u9593\u7684\u76f8\u4f3c\u5ea6\uff0c\u4f46\u5728\u9078\u53d6\u9069\u7576\u7684\u8a9e\u53e5\u4f5c\u70ba\u6458\u8981\u4e4b\u4efb\u52d9\u4e0a\uff0c\u6211\u5011\u8a8d\u70ba\u4ea6\u53ef\u984d\u5916\u5730\u8003\u91cf\u7531\u5176\u5b83 \u4e0d\u540c\u89d2\u5ea6\u51fa\u767c\u6240\u64f7\u53d6\u4e4b\u7dda\u7d22\uff1b\u8b6c\u5982\uff0c\u63a2\u8a0e\u8a9e\u53e5\u672c\u8eab\u6240\u860a\u542b\u7684\u8a5e\u5f59\u4f7f\u7528\u8cc7\u8a0a\uff0c\u4ee5\u53ca\u8a9e\u53e5\u8207 \u975e\u76f8\u95dc(Non-relevance)\u8cc7\u8a0a(\u5728\u9019\u88e1\u662f\u6307\u88ab\u6458\u8981\u6587\u4ef6\u7684\u975e\u76f8\u95dc\u8cc7\u8a0a)\u9593\u7684\u95dc\u4fc2\u3002\u57fa\u65bc\u6b64\u6982 \u5ff5\uff0c\u672c\u8ad6\u6587\u9996\u5148\u63d0\u51fa\u4f7f\u7528\u660e\u78ba\u5ea6(Clarity)[11]\u91cf\u503c\u4f86\u8f14\u52a9\u5eab\u723e\u8c9d\u514b-\u840a\u4f2f\u52d2\u96e2\u6563\u5ea6\u91cf\u503c\u9032 \u884c\u6458\u8981\u8a9e\u53e5\u9078\u53d6\u3002\u540c\u6642\uff0c\u6211\u5011\u4ea6\u5c07\u6df1\u5165\u5730\u63a2\u8a0e\u9019\u5169\u7a2e\u4e0d\u540c\u7684\u91cf\u503c(\u4e00\u70ba\u8a9e\u53e5 S \u8207\u6587\u4ef6 D \u7684 \u5eab\u723e\u8c9d\u514b-\u840a\u4f2f\u52d2\u96e2\u6563\u5ea6\uff0c\u53e6\u4e00\u70ba\u8a9e\u53e5 S \u7684\u660e\u78ba\u5ea6)\u5c0d\u65bc\u9078\u53d6\u91cd\u8981\u4e14\u5177\u4ee3\u8868\u6027\u4e4b\u8a9e\u53e5\u7684\u5be6 \u969b\u5f71\u97ff\u3002 \u9996\u5148\uff0c\u6211\u5011\u5c07\u6bcf\u4e00\u7bc7\u88ab\u6458\u8981\u6587\u4ef6 D \u4e2d\u8a9e\u53e5 S \u7684\u660e\u78ba\u5ea6\u91cf\u503c\u5b9a\u7fa9\u5982\u4e0b\uff1a ) ( ) || ( ) ( S H S N CE S Clarity D def \uf02d \uf03d (7) \u5176\u4e2d ) || ( S N CE D \u70ba\u8a9e\u53e5 S \u8207\u88ab\u6458\u8981\u6587\u4ef6 D \u7684\u975e\u76f8\u95dc\u8cc7\u8a0a D N \u4e4b\u9593\u7684\u4ea4\u4e92\u4e82\u5ea6(Cross Entropy, CE)\uff1a \uf0e5 \uf0ce \uf02d \uf03d V w D D w|S P w|N P S N CE ) ( ) ( log ) || (", |
| "eq_num": "(8)" |
| } |
| ], |
| "section": "", |
| "sec_num": null |
| }, |
| { |
| "text": "\u6211\u5011\u8a8d\u70ba\u6bcf\u4e00\u7bc7\u88ab\u6458\u8981\u6587\u4ef6 D \u4e2d\u53ef\u540c\u6642\u64f7\u53d6\u51fa\u5169\u7a2e\u4e0d\u540c\u9762\u5411\u7684\u8cc7\u8a0a\uff0c\u5206\u5225\u662f\u76f8\u95dc (Relevance)\u8207\u975e\u76f8\u95dc(Non-relevance)\u8cc7\u8a0a\u3002\u6211\u5011\u5c07\u6587\u4ef6 D \u4e2d\u7684\u76f8\u95dc\u8cc7\u8a0a\u5b9a\u7fa9\u70ba\u662f\u6587\u4ef6\u6240 \u6b32\u8868\u9054\u7684\u4e3b\u65e8\u6216\u4e3b\u984c\u8cc7\u8a0a\uff1b\u76f8\u53cd\u5730\uff0c\u6587\u4ef6 D \u7684\u975e\u76f8\u95dc\u8cc7\u8a0a\u5247\u662f\u8207\u8a72\u6587\u4ef6\u5167\u5bb9\u5b8c\u5168\u6c92\u6709\u95dc \u806f\u3001\u751a\u81f3\u662f\u80cc\u9053\u800c\u99b3\u7684\u4e3b\u65e8\u6216\u4e3b\u984c\u8cc7\u8a0a\u3002\u56e0\u6b64\uff0c\u6458\u8981\u8a9e\u53e5\u6a21\u578b\u61c9\u8207\u7531\u76f8\u95dc\u8cc7\u8a0a\u6240\u4f30\u6e2c\u7684 \u6a21\u578b(\u5982\u6587\u4ef6\u6a21\u578b)\u6108\u76f8\u4f3c(\u63a5\u8fd1)\uff0c\u800c\u8207\u975e\u76f8\u95dc\u8cc7\u8a0a\u6240\u4f30\u6e2c\u7684\u6a21\u578b\u6108\u4e0d\u76f8\u4f3c(\u9060\u96e2)\u3002\u7576\u5047\u8a2d \u5c0d\u65bc\u6bcf\u4e00\u7bc7\u88ab\u6458\u8981\u6587\u4ef6\u800c\u8a00\uff0c\u8a9e\u6599\u5eab\u4e2d\u7d55\u5927\u90e8\u5206\u7684\u6587\u4ef6\u90fd\u8207\u5176\u4e3b\u65e8\u6216\u5167\u5bb9\u4e0d\u76f8\u95dc\u6642\uff0c\u5247 \u6211\u5011\u53ef\u85c9\u7531\u8a9e\u6599\u5eab\u4e2d\u5927\u91cf\u6587\u4ef6\u6240\u4f30\u6e2c\u800c\u5f97\u7684\u80cc\u666f\u55ae\u9023\u8a9e\u8a00\u6a21\u578b(\u53c3\u7167\u5f0f(4)\u8207\u5176\u8aaa\u660e)\u4f86 \u8fd1 \u4f3c \u6bcf \u4e00 \u7bc7 \u88ab \u6458 \u8981 \u6587 \u4ef6 D \u4e4b \u975e \u76f8 \u95dc \u8cc7 \u8a0a D N \u6240 \u5c0d \u61c9 \u7684 \u6a21 \u578b ) ( D w|N P \u3002 \u7c21 \u8a00 \u4e4b \uff0c ) || ( S N CE D \u65e8\u5728\u63cf\u8ff0\u8a9e\u53e5 S \u8207\u88ab\u6458\u8981\u6587\u4ef6 D \u7684\u975e\u76f8\u95dc\u8cc7\u8a0a D N \u4e4b\u9593\u7684\u76f8\u4f3c\u95dc\u4fc2\uff0c\u53ef\u8996\u70ba \u662f\u8a9e\u53e5 S \u7684\u4e00\u7a2e\u5916\u5728\u8cc7\u8a0a(Extrinsic Information)\u3002\u82e5\u8a9e\u53e5 S \u8207\u88ab\u6458\u8981\u6587\u4ef6\u7684\u975e\u76f8\u95dc\u8cc7\u8a0a D N \u7684\u4ea4\u4e92\u4e82\u5ea6\u503c\u6108\u5927(\u4ea6\u5373\u8a9e\u53e5 S \u8207\u88ab\u6458\u8981\u6587\u4ef6 D \u7684\u975e\u76f8\u95dc\u8cc7\u8a0a\u4e4b\u7528\u5b57\u9063\u8a5e\u662f\u5927\u76f8\u5f91", |
| "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": "\u5ead\u7684) \uff0c\u5247\u8868\u793a\u8a9e\u53e5 S \u8207\u6587\u4ef6 D \u7684\u975e\u76f8\u95dc\u8cc7\u8a0a D N \u6108\u4e0d\u76f8\u4f3c\uff1b\u53cd\u4e4b\uff0c\u82e5\u8a9e\u53e5 S \u8207\u88ab\u6458\u8981\u6587 \u4ef6\u7684\u975e\u76f8\u95dc\u8cc7\u8a0a D N \u7684\u4ea4\u4e92\u4e82\u5ea6\u503c\u6108\u5c0f(\u4ea6\u5373\u8a9e\u53e5 S \u8207 D N \u7684\u7528\u5b57\u9063\u8a5e\u662f\u5dee\u4e0d\u591a\u7684) \uff0c\u5247 \u8a9e\u53e5 S \u8207\u88ab\u6458\u8981\u6587\u4ef6\u7684\u975e\u76f8\u95dc\u8cc7\u8a0a D N \u6108\u76f8\u4f3c\u3002 \u5728\u5f0f(7)\u660e\u78ba\u5ea6\u91cf\u503c\u4e2d\u7684 ) (S H \u70ba\u8a9e\u53e5 S \u4e4b\u672c\u8eab\u7684\u8cc7\u8a0a\u8907\u96dc\u5ea6(Sentence Entropy, SE)\uff1a \uf0e5 \uf0ce \uf02d \uf03d V w S w P S w P S H ) | ( log ) | ( ) (", |
| "eq_num": "(9)" |
| } |
| ], |
| "section": "", |
| "sec_num": null |
| }, |
| { |
| "text": "EQUATION", |
| "cite_spans": [], |
| "ref_spans": [], |
| "eq_spans": [ |
| { |
| "start": 0, |
| "end": 8, |
| "text": "EQUATION", |
| "ref_id": "EQREF", |
| "raw_str": ") (S H \u662f\u63cf\u8ff0\u8a9e\u53e5\u672c\u8eab\u4f7f\u7528\u8a5e\u5f59\u4e4b\u96c6\u4e2d\u6027\uff0c\u56e0\u6b64\u8a9e\u53e5 S \u672c\u8eab\u7684\u8cc7\u8a0a\u8907\u96dc\u5ea6\u53ef\u8996\u70ba\u662f\u8a9e\u53e5 \u672c\u8eab\u7684\u4e00\u7a2e\u5167\u5728\u8cc7\u8a0a(Intrinsic Information)\u3002\u7576\u8a9e\u53e5\u8907\u96dc\u5ea6\u503c ) (S H \u6108\u5c0f\u6642\uff0c\u8868\u793a\u8a9e\u53e5\u6240 \u4f7f\u7528\u7684\u4e0d\u540c\u8a5e\u5f59\u4e4b\u500b\u6578\u6108\u5c11\u6216\u6108\u96c6\u4e2d\uff0c\u8a9e\u53e5 S \u6240\u5448\u73fe\u7684\u4e3b\u984c\u4e5f\u6108\u805a\u7126\uff0c\u5373\u8a9e\u53e5 S \u6108\u5177\u6709 \u7368\u7279\u6027(Specificity)\uff1b\u53cd\u4e4b\uff0c\u7576\u8a9e\u53e5\u8907\u96dc\u5ea6\u503c ) (S H \u6108\u5927\u6642\uff0c\u8868\u793a\u8a9e\u53e5 S \u6240\u4f7f\u7528\u7684\u4e0d\u540c\u8a5e \u5f59\u4e4b\u500b\u6578\u6108\u591a\u6216\u6108\u767c\u6563\uff0c\u4e14\u5404\u500b\u8a5e\u5f59\u51fa\u73fe\u7684\u983b\u7387\u76f8\u8fd1\uff0c\u4e5f\u5c31\u662f\u8a9e\u53e5\u4e2d\u8f03\u7121\u7279\u5225\u5f37\u8abf\u7684\u8a5e \u5f59\uff0c\u6240\u4ee5\u76f8\u8f03\u4e4b\u4e0b\uff0c\u8a9e\u53e5 S \u6240\u860a\u542b\u7684\u8cc7\u8a0a\u53ef\u80fd\u8f03\u8907\u96dc\uff0c\u6bd4\u8f03\u4e0d\u5177\u7368\u7279\u6027\u3002\u7d9c\u89c0\u4ee5\u4e0a\u5206\u6790\uff0c \u82e5\u8a9e\u53e5 S \u7684\u660e\u78ba\u5ea6\u6108\u9ad8\uff0c\u8868\u793a\u8a9e\u53e5 S \u8207\u88ab\u6458\u8981\u6587\u4ef6 D \u7684\u975e\u76f8\u95dc\u8cc7\u8a0a D N \u4e4b\u9593\u7684\u4ea4\u4e92\u4e82\u5ea6 \u6108\u5927\u4e14\u8a9e\u53e5 S \u672c\u8eab\u7684\u8a5e\u5f59\u4f7f\u7528\u8907\u96dc\u5ea6\u6108\u5c0f\uff1b\u63db\u53e5\u8a71\u8aaa\uff0c\u5373\u6b64\u8a9e\u53e5\u6240\u860a\u542b\u7684\u8cc7\u8a0a\u4e0d\u50c5\u4e0d\u540c \u65bc\u88ab\u6458\u8981\u6587\u4ef6 D \u7684\u975e\u76f8\u95dc\u8cc7\u8a0a\uff0c\u4e26\u4e14\u6240\u6b32\u8868\u9054\u7684\u4e3b\u984c\u5167\u5bb9\u662f\u8f03\u70ba\u660e\u78ba\u4e14\u55ae\u7d14\u7684\u3002 \u7531\u65bc\u8a9e\u53e5\u7684\u660e\u78ba\u5ea6\u662f\u63cf\u8ff0\u8a9e\u53e5\u8207\u6587\u4ef6\u4e4b\u975e\u76f8\u95dc\u8cc7\u8a0a D N \u9593\u7684\u95dc\u4fc2\u4ee5\u53ca\u8a9e\u53e5\u672c\u8eab\u7684 \u8cc7\u8a0a\uff0c\u6211\u5011\u9032\u4e00\u6b65\u7684\u5c07\u8a9e\u53e5\u8207\u6587\u4ef6\u9593\u76f8\u4f3c\u5ea6\u7684\u8cc7\u8a0a\u8207\u660e\u78ba\u5ea6\u76f8\u7d50\u5408\uff0c\u505a\u70ba\u6700\u7d42\u8a9e\u53e5\u91cd\u8981 \u6027\u6392\u5e8f\u4e4b\u4f9d\u64da\uff1a ) ( ) || ( S Clarity S D KL \uf02b \uf02d (10) \u5eab\u723e\u8c9d\u514b-\u840a\u4f2f\u52d2\u96e2\u6563\u5ea6\u91cf\u503c\u6108\u5c0f\uff0c\u8868\u793a\u8a9e\u53e5\u8207\u88ab\u6458\u8981\u6587\u4ef6\u7684\u76f8\u4f3c\u5ea6\u61c9\u5c07\u6703\u6108\u5927\uff1b\u8a9e\u53e5 \u660e\u78ba\u5ea6\u91cf\u503c\u6108\u5927\uff0c\u5247\u6108\u6709\u53ef\u80fd\u8868\u793a\u8a9e\u53e5\u4e0d\u50c5\u5177\u6709\u7368\u7279\u6027\u4e14\u80fd\u660e\u78ba\u5448\u73fe\u88ab\u6458\u8981\u6587\u4ef6\u4e4b\u4e3b \u984c\u3002\u7d9c\u5408\u9019\u5169\u500b\u9762\u5411\uff0c\u6211\u5011\u671f\u671b\u53ef\u4ee5\u6311\u9078\u51fa\u8207\u88ab\u6458\u8981\u6587\u4ef6\u76f8\u4f3c\u5ea6\u9ad8\u4e26\u4e14\u8a00\u7c21\u610f\u8cc5\u7684\u8a9e\u53e5 \u4f86\u5f62\u6210\u6458\u8981\u3002\u518d\u8005\uff0c\u56e0\u660e\u78ba\u5ea6\u91cf\u503c\u53ef\u5340\u5206\u70ba\u5169\u500b\u90e8\u5206\uff0c\u4e00\u70ba\u8a9e\u53e5 S \u4e4b\u672c\u8eab\u7684\u8cc7\u8a0a\u8907\u96dc\u5ea6\uff0c \u53e6\u4e00\u70ba\u8a9e\u53e5 S \u8207\u88ab\u6458\u8981\u6587\u4ef6 D \u7684\u975e\u76f8\u95dc\u8cc7\u8a0a D N \u4e4b\u9593\u7684\u4ea4\u4e92\u4e82\u5ea6\uff0c\u5728\u5be6\u9a57\u4e2d\u6211\u5011\u5c07\u66f4\u9032 \u4e00\u6b65\u5730\u63a2\u8a0e\u5eab\u723e\u8c9d\u514b-\u840a\u4f2f\u52d2\u96e2\u6563\u5ea6\u91cf\u503c\u5206\u5225\u8207\u9019\u5169\u7a2e\u6210\u5206\u76f8\u7d50\u5408\u4e4b\u6458\u8981\u6210\u6548\uff1a H(S) S D KL \uf02d \uf02d ) || (", |
| "eq_num": "(11)" |
| } |
| ], |
| "section": "", |
| "sec_num": null |
| }, |
| { |
| "text": "-\u840a\u4f2f\u52d2\u96e2\u6563\u5ea6\u91cf\u503c\u3001\u660e\u78ba\u5ea6\u91cf\u503c\u4ee5\u53ca\u95dc\u806f\u6a21\u578b\uff0c\u662f\u7531\u56db\u500b\u9762\u5411\u4f86\u6311\u9078\u91cd\u8981\u7684\u6458\u8981\u8a9e\u53e5\uff0c \u4e00\u70ba\u8a9e\u53e5\u8207\u6587\u4ef6\u4e4b\u76f8\u4f3c\u5ea6( ) || ( S D KL )\uff0c\u4e8c\u662f\u8a9e\u53e5\u672c\u8eab\u8cc7\u8a0a\u8907\u96dc\u5ea6( ) (S H )\uff0c\u7b2c\u4e09\u662f\u8a9e\u53e5 \u8207\u88ab\u6458\u8981\u6587\u4ef6\u7684\u975e\u76f8\u95dc\u8cc7\u8a0a\u4e4b\u4ea4\u4e92\u4e82\u5ea6( ) || ( S N CE D )\uff0c\u6700\u5f8c\u70ba\u8a9e\u53e5\u8207\u95dc\u806f\u6587\u4ef6\u4e4b\u76f8\u95dc\u8cc7 \u8a0a( ) | ( RM S w P )\uff0c\u6b64\u5be6\u9a57\u7d50\u679c\u4ea6\u986f\u793a\uff0c\u9019\u56db\u500b\u9762\u5411\u4e4b\u8cc7\u8a0a\u53ef\u4ee5\u76f8\u8f14\u76f8\u6210\u7684\u4f7f\u7528\uff0c\u9054\u5230\u6700 \u4f73\u7684\u6458\u8981\u6210\u6548\u3002 \u5728 \u95dc \u806f \u6a21 \u578b \u7684 \u76f8\u95dc \u5be6 \u9a57 \u4e2d \uff0c \u8a9e \u97f3 \u8fa8 \u8b58\u932f \u8aa4 \u4e5f \u662f \u5f71 \u97ff \u6458 \u8981 \u6548\u80fd \u975e \u5e38 \u56b4 \u91cd \uff0c \u5728 KL+Clarity+RM \u7684\u6578\u64da\u4e2d\uff0cSD \u6bd4 TD \u5287\u70c8\u4e0b\u964d\u4e86", |
| "cite_spans": [], |
| "ref_spans": [], |
| "eq_spans": [], |
| "section": "", |
| "sec_num": null |
| }, |
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| "text": "Proceedings of the Twenty-Fifth Conference on Computational Linguistics and Speech Processing(ROCLING 2013)", |
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| "section": "", |
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| "first": "J.-J", |
| "middle": [], |
| "last": "Zhang", |
| "suffix": "" |
| }, |
| { |
| "first": "H.-Y", |
| "middle": [], |
| "last": "Chan", |
| "suffix": "" |
| }, |
| { |
| "first": "P", |
| "middle": [], |
| "last": "Fung", |
| "suffix": "" |
| } |
| ], |
| "year": 2010, |
| "venue": "IEEE Transactions on Audio, Speech and Language Processing", |
| "volume": "18", |
| "issue": "6", |
| "pages": "1147--1157", |
| "other_ids": {}, |
| "num": null, |
| "urls": [], |
| "raw_text": "J.-J. Zhang, H.-Y. Chan and P. Fung, Extractive Speech Summarization using Shallow Rhetorical Structure Modeling, IEEE Transactions on Audio, Speech and Language Processing, Vol. 18, No. 6, pp. 1147-1157, 2010", |
| "links": null |
| } |
| }, |
| "ref_entries": { |
| "FIGREF0": { |
| "text": "\u81f4\u8b1d \u672c\u8ad6\u6587\u4e4b\u7814\u7a76\u627f\u8499\u6559\u80b2\u90e8-\u570b\u7acb\u81fa\u7063\u5e2b\u7bc4\u5927\u5b78\u9081\u5411\u9802\u5c16\u5927\u5b78\u8a08\u756b(102J1A0800)\u8207\u884c\u653f\u9662 \u570b \u5bb6 \u79d1 \u5b78 \u59d4 \u54e1 \u6703 \u7814 \u7a76 \u8a08 \u756b (NSC 101-2221-E-003-024-MY3 \u3001 NSC 101-2511-S-003-057-MY3\u3001NSC 101-2511-S-003-047-MY3 \u548c NSC 102-2221-E-003-014-MY3)\u4e4b\u7d93\u8cbb\u652f\u6301\uff0c\u8b39\u6b64\u81f4\u8b1d\u3002 \u5716\u4e09\u3001SVM \u8207\u5176\u4ed6\u975e\u76e3\u7763\u5f0f\u6458\u8981\u65b9\u6cd5\u4e4b\u6bd4\u8f03 (Rouge2) Proceedings of the Twenty-Fifth Conference on Computational Linguistics and Speech Processing (ROCLING 2013)", |
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| "type_str": "figure", |
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| "text": "\u5e38\u64b0 \u5beb \u6458\u8981\u7684 \u5f62 \u5f0f\u3002 \u7136\u800c \u62bd \u8c61\u5f0f\u6458 \u8981 \u9700\u8981 \u8907\u96dc \u7684 \u81ea\u7136\u8a9e \u8a00 \u8655\u7406 (Natural Language Processing, NLP)\u6280\u8853\uff0c\u5982\u8cc7\u8a0a\u64f7\u53d6(Information Extraction)\u3001\u5c0d\u8a71\u7406\u89e3(Discourse Understanding)\u53ca\u81ea\u7136\u8a9e\u8a00\u751f\u6210(Natural Language Generation)\u7b49[26][34]\uff0c\u56e0\u6b64\uff0c\u8fd1\uf98e\u4f86\u7bc0 \uf93f\u5f0f\u6458\u8981\u4e4b\u7814\u7a76\u4ecd\u70ba\u4e3b\u6d41\u3002 4. \u7528\u9014\uff1a\u4f9d\u6458\u8981\u7528\u9014\u53ef\u5206\u70ba\u4e00\u822c\u6027(Generic)\u6458\u8981\u8207\u4ee5\u67e5\u8a62\u70ba\u57fa\u790e(Query-focused)\u7684 \u6458\u8981\u3002\u524d\u8005\u662f\u5f9e\u6574\u7bc7\u6587\u4ef6\u4e2d\u8403\u53d6\u51fa\u80fd\u5920\u7a81\u986f\u6574\u7bc7\u6587\u4ef6\u5168\u9762\u6027\u4e3b\u984c\u8cc7\u8a0a\u7684\u8a9e\u53e5\uff0c\u671f\u671b\u6458\u8981 \u7522\u751f\u7684\u5167\u5bb9\u53ef\u4ee5\u6db5\u84cb\u6574\u7bc7\u6587\u4ef6\u6240\u6709\u91cd\u8981\u7684\u4e3b\u984c\uff1b\u5f8c\u8005\u900f\u904e\u4f7f\u7528\u8005\u6216\u7279\u5b9a\u7684\u67e5\u8a62\u4f86\u7522\u751f\u8207 \u67e5\u8a62\u76f8\u95dc\u7684\u6458\u8981\u3002 5. \u6a21\u578b\u6280\u8853\uff1a\u7c21\u55ae\u5206\u6210\u4e09\u5927\u985e\uff0c(i)\u4ee5\u7c21\u55ae\u7684\u8a9e\u5f59(Lexical)\u8207\u7d50\u69cb(Structural)\u7279\u5fb5\u505a \u70ba\u5224\u65b7\u6458\u8981\u8a9e\u53e5\u7684\u6a21\u578b\u6280\u8853[38]\uff0c(ii)\u76e3\u7763\u5f0f\u6a5f\u5668\u5b78\u7fd2(Supervised Machine Learning)\u4ee5\u53ca (iii)\u975e\u76e3\u7763\u5f0f\u6a5f\u5668\u5b78\u7fd2(Unsupervised Machine Learning)[20]\u4e4b\u6a21\u578b\u6280\u8853\u3002\u96d6\u7136\u975e\u76e3\u7763\u5f0f\u6a5f", |
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| "content": "<table><tr><td>\uf93f\u97f3\u7b49[25]\uff0c\uf901\u662f\u986f\u5f97\u975e\u5e38\u91cd\u8981\u3002\u5176\u95dc\u9375\u539f\u56e0\u5728\u65bc\u591a\u5a92\u9ad4\u5f71\u97f3\u5167\u5bb9\u5f80\u5f80\u9577\u9054\u6578\u5206\u9418\u6216\u6578 \u6563\u5ea6\u4e4b\u975e\u76e3\u7763\u5f0f\u6a21\u578b\u6280\u8853\u904b\u7528\u5728\u8cc7\u8a0a\u6aa2\u7d22\u7814\u7a76\u4e0a\u5df2\u6709\u975e\u5e38\u597d\u7684\u6210\u679c[18]\uff0c\u4e26\u5df2\u521d\u6b65\u88ab\u61c9 \u7279\u5fb5\u6240\u5f62\u6210\u7684\u7279\u5fb5\u5411\u91cf\u5c07\u88ab\u7528\u4f86\u505a\u70ba\u76e3\u7763\u5f0f\u6458\u8981\u6a21\u578b\u5224\u65b7\u8a9e\u53e5\u662f\u5426\u5c6c\u65bc\u6458\u8981\u8a9e\u53e5\u7684\u4f9d</td></tr><tr><td>\u5c0f\u6642\uff0c\u4f7f\u7528\u8005\u4e0d\u6613\u65bc\u700f\u89bd\u548c\u67e5\u8a62\uff0c\u800c\u5fc5\u9808\u8010\u5fc3\u5730\u95b1\u8b80\u6216\u807d\u5b8c\u6574\u4efd\u591a\u5a92\u9ad4\u5f71\u97f3\u5167\u5bb9\uff0c\u624d\u80fd \u7528\u65bc\u8a9e\u97f3\u6587\u4ef6\u6458\u8981\u4e4b\u7814\u7a76\u4e0a[36]\uff0c\u672c\u8ad6\u6587\u5c07\u5ef6\u7e8c\u6b64\u4e00\u7814\u7a76\u4e3b\u8ef8\u4e14\u63d0\u51fa\u5169\u500b\u7814\u7a76\u8ca2\u737b\u3002\u5176 \u64da[17]\u3002</td></tr><tr><td>\u7406\u89e3\u5176\u4e2d\u6240\u63cf\u8ff0\u7684\u8a9e\u610f\u8207\u4e3b\u984c\uff0c\u9019\u9055\u53cd\u4eba\u5011\u8b1b\u6c42\u65b9\uf965\u3001\u6709\u6548\uf961\u7684\u8cc7\u8a0a\u7372\u53d6\u65b9\u5f0f\u3002 \u4e00\u70ba\u521d\u6b65\u63a2\u7a76\u4f7f\u7528\u8a9e\u53e5\u660e\u78ba\u5ea6(Clarity)[11]\u5728\u8a9e\u97f3\u6587\u4ef6\u6458\u8981\u4efb\u52d9\u4e2d\u4e4b\u6548\u7528\uff0c\u4e26\u540c\u6642\u6aa2\u8996\u660e \u6b64\u5916\uff0c\u6587\u5b57\u6587\u4ef6\u6240\u8981\u5f37\u8abf\u7684\u662f\u600e\u9ebc\u8aaa(What-is-said)\uff0c\u800c\u8a9e\u97f3\u6587\u4ef6\u64c1\u6709\u8a31\u591a\u7d14\u6587\u5b57\u6587</td></tr><tr><td>\u96d6\u7136\u5c0d\u65bc\u542b\u6709\u8a9e\u97f3\u8a0a\u865f\u7684\u591a\u5a92\u9ad4\u5f71\u97f3\uff0c\u6211\u5011\u53ef\u900f\u904e\u81ea\u52d5\u8a9e\u97f3\u8fa8\u8b58(Automatic Speech \u78ba\u5ea6\u7684\u5167\u90e8\u7d44\u6210\u6210\u4efd (\u5373\u8a9e\u53e5\u8207\u88ab\u6458\u8981\u6587\u4ef6\u4e4b\u975e\u76f8\u95dc\u8cc7\u8a0a\u7684\u4ea4\u4e92\u4e82\u5ea6\u548c\u8a9e\u53e5\u672c\u8eab\u8cc7\u8a0a\u8907 \u4ef6\u6240\u6c92\u6709\u7684\u8cc7\u8a0a\uff0c\u901a\u5e38\u9664\u4e86\u600e\u9ebc\u8aaa\uff0c\u66f4\u5f37\u8abf\u7684\u662f\u5982\u4f55\u8aaa(How-is-said)[27]\uff0c\u660e\u986f\u5730\uff0c\u8a9e</td></tr><tr><td>Recognition, ASR)\u6280\u8853\u81ea\u52d5\u5730\u5c07\u5176\u8f49\u63db\u6210\u6613\u65bc\u700f\u89bd\u7684\u6587\u5b57\u5167\u5bb9\uff0c\u518d\u85c9\u7531\u6587\u5b57\u6587\u4ef6\u6458\u8981\u7684 \u96dc\u5ea6) \uff1b\u85c9\u7531\u660e\u78ba\u5ea6\u7684\u8f14\u52a9\u4f86\u91cd\u65b0\u8a6e\u91cb\u5982\u4f55\u80fd\u5728\u81ea\u52d5\u6458\u8981\u4efb\u52d9\u4e2d\u9069\u7576\u5730\u6311\u9078\u91cd\u8981\u4e14\u5177\u4ee3 \u97f3\u662f\u591a\u5a92\u9ad4\u5167\u6db5\u4e2d\u6700\u5177\u8cc7\u8a0a\u7684\u6210\u5206\u4e4b\u4e00\uff0c\u4e5f\u56e0\u6b64\u8a9e\u97f3\u6587\u4ef6\u6458\u8981\u7684\u76f8\u95dc\u7814\u7a76\u901a\u5e38\u5f9e\u591a\u5a92\u9ad4</td></tr><tr><td>\u6280\u8853\u4f86\u505a\u8655\u7406\uff0c\u4ee5\u9054\u5230\u6458\u8981\u591a\u5a92\u9ad4\u5f71\u97f3\u6216\u5176\u5b83\u8a9e\u97f3\u6587\u4ef6\u4e4b\u76ee\u7684\u3002\u4f46\u5c31\u73fe\u968e\u6bb5\u8a9e\u97f3\u8fa8\u8b58\u6280 \u8868\u6027\u7684\u8a9e\u53e5\u3002\u5176\u4e8c\u70ba\u6709\u9451\u65bc\u95dc\u806f\u6027(Relevance)\u7684\u6982\u5ff5\u5728\u8cc7\u8a0a\u6aa2\u7d22\u9818\u57df\u4e2d\u5df2\u6709\u4e0d\u932f\u7684\u767c\u5c55 \u8a9e\u97f3\u8a0a\u865f\u4e2d\u8403\u53d6\u8c50\u5bcc\u7684\u97fb\u5f8b\u8cc7\u8a0a(Prosodic Information)\u4f86\u5224\u65b7\u8a9e\u53e5\u7684\u91cd\u8981\u6027\uff0c\u5982\uff1a\u97f3\u8abf</td></tr><tr><td>\u8853\u7684\u767c\u5c55\uff0c\u8a9e\u97f3\u6587\u4ef6\u7d93\u8a9e\u97f3\u8fa8\u8b58\u5f8c\u81ea\u52d5\u8f49\u5beb\u6210\u6587\u5b57\u7684\u7d50\u679c\uff0c\u4e0d\u50c5\u5b58\u5728\u8fa8\u8b58\u932f\u8aa4\u7684\u554f\u984c\uff0c \u6210\u679c[14]\uff0c\u672c\u8ad6\u6587\u5617\u8a66\u7d50\u5408\u95dc\u806f\u6027\u8cc7\u8a0a\u4f86\u91cd\u65b0\u4f30\u6e2c\u4e26\u5efa\u7acb\u8a9e\u53e5\u7684\u8a9e\u8a00\u6a21\u578b\uff0c\u4f7f\u5176\u5f97\u4ee5\u66f4 (Intonation)\u3001\u97f3\u9ad8(Pitch)\u3001\u97f3\u5f37(Power)\u3001\u8a9e\u8005\u767c\u8072\u6301\u7e8c\u6642\u9593(Duration)\u3001\u8a9e\u8005\u8aaa\u8a71\u901f\uf961</td></tr><tr><td>\u4e5f\u7f3a\u4e4f\u7ae0\u7bc0\u8207\u6a19\u9ede\u7b26\u865f\uff0c\u4f7f\u5f97\u8a9e\u53e5\u908a\u754c\u5b9a\u7fa9\u4e0d\u6e05\u695a\u800c\u5931\u53bb\u6587\u4ef6\u7684\u7d50\u69cb\u8cc7\u8a0a\uff1b\u9664\u6b64\u4e4b\u5916\uff0c \u7cbe\u6e96\u5730\u4ee3\u8868\u8a9e\u53e5\u7684\u8a9e\u610f\u5167\u5bb9\uff0c\u671f\u671b\u53ef\u589e\u9032\u81ea\u52d5\u6458\u8981\u4e4b\u6548\u80fd\u3002 (Rate)\u3001\u8a9e\u8005(Speaker)\u3001\u60c5\u611f(Emotion)\u548c\u8aaa\u8a71\u6642\u5834\u666f(Environment)\u7b49\u8cc7\u8a0a\uff0c\u9019\u4e9b\u90fd\u662f\u5f9e</td></tr><tr><td>\u8a9e\u97f3\u6587\u4ef6\u901a\u5e38\u542b\u6709\u8a31\u591a\u53e3\u8a9e\u8a9e\u52a9\u8a5e\u3001\u9072\u7591\u3001\u91cd\u8986\u7b49\u5167\u5bb9\uff0c\u9019\u90fd\u4f7f\u5f97\u8a9e\u97f3\u6587\u4ef6\u6458\u8981\u6280\u8853\u7684 \u672c\u8ad6\u6587\u5f8c\u7e8c\u5b89\u6392\u5982\u4e0b\uff1a\u7b2c\u4e8c\u7ae0\u627c\u8981\u5730\u4ecb\u7d39\u73fe\u4eca\u81ea\u52d5\u6458\u8981\u6a21\u578b\u6280\u8853\u7684\u76f8\u95dc\u7814\u7a76\u8207\u767c \u4e8b\u8a9e\u97f3\u6587\u4ef6\u6458\u8981\u6642\u53ef\u4ee5\u5584\u52a0\u5229\u7528\u7684\u8a9e\u53e5\u7279\u5fb5\u8cc7\u8a0a[20]\u3002</td></tr><tr><td>\u767c\u5c55\u9762\uf9f6\uf901\u591a\u7684\u6311\u6230\u3002 \u5c55\uff1b\u7b2c\u4e09\u7ae0\u9996\u5148\u4ecb\u7d39\u4f7f\u7528\u8a9e\u8a00\u6a21\u578b\u65bc\u7bc0\u9304\u5f0f\u8a9e\u97f3\u6458\u8981\u4efb\u52d9\u4e4b\u539f\u7406\uff0c\u7136\u5f8c\u95e1\u8ff0\u5982\u4f55\u5c07\u660e\u78ba</td></tr><tr><td>\u4e00\u822c\u4f86\u8aaa\uff0c\u81ea\u52d5\u6458\u8981\u7814\u7a76\u53ef\u5f9e\u8a31\u591a\u4e0d\u540c\u9762\u76f8\u4f86\u9032\u884c\u63a2\u8a0e\uff0c\u5305\u62ec\u4e86\u4f86\u6e90\u3001\u9700\u6c42\u3001\u65b9\u5f0f\u3001 \u5ea6\u904b\u7528\u81f3\u6458\u8981\u8a9e\u53e5\u4e4b\u6311\u9078\uff0c\u4e26\u4e14\u8aaa\u660e\u5982\u4f55\u85c9\u52a9\u8a9e\u53e5\u95dc\u806f\u6027\u8cc7\u8a0a\u4f86\u6539\u9032\u8a9e\u53e5\u6a21\u578b\u4e4b\u4f30\u6e2c\uff0c \u4e09\u3001\u4f7f\u7528\u8a9e\u8a00\u6a21\u578b\u65bc\u8a9e\u97f3\u6587\u4ef6\u6458\u8981</td></tr><tr><td>\u7528\u9014\u4ee5\u53ca\u6a21\u578b\u6280\u8853\uff0c\u4ee5\u4e0b\u5c07\u7c21\u8ff0\u5404\u500b\u4e0d\u540c\u9762\u76f8\u7684\u76f8\u95dc\u8b70\u984c[22]\uff1a \u4f7f\u5176\u5f97\u4ee5\u66f4\u7cbe\u6e96\u5730\u4ee3\u8868\u8a9e\u53e5\u7684\u8a9e\u610f\u5167\u5bb9\uff1b\u7b2c\u56db\u7ae0\u4ecb\u7d39\u5be6\u9a57\u8a9e\u6599\u8207\u8a2d\u5b9a\u4ee5\u53ca\u6458\u8981\u8a55\u4f30\u4e4b\u65b9 \u8a9e\u8a00\u6a21\u578b\u7684\u7814\u7a76\u8207\u767c\u5c55\u6700\u65e9\u662f\u6e90\u81ea\u65bc\u8a9e\u97f3\u8fa8\u8b58\u53ca\u81ea\u7136\u8a9e\u8a00\u8655\u7406\u3002\u8a9e\u8a00\u6a21\u578b\u65e8\u5728\u63cf\u8ff0\u8a9e\u8a00</td></tr><tr><td>1. \u4f86\u6e90\uff1a\u6839\u64da\u6587\u4ef6\u4f86\u6e90\uff0c\u53ef\u4ee5\u5206\u70ba\u55ae\u4e00\u6587\u4ef6\u6458\u8981\u8207\u591a\u91cd\u6587\u4ef6\u6458\u8981[3]\uff1b\u55ae\u4e00\u6587\u4ef6\u6458 \u6cd5\uff1b\u7b2c\u4e94\u7ae0\u8aaa\u660e\u5be6\u9a57\u7d50\u679c\u53ca\u5176\u5206\u6790\uff1b\u6700\u5f8c\uff0c\u7b2c\u516d\u7ae0\u70ba\u7d50\u8ad6\u8207\u672a\u4f86\u7814\u7a76\u65b9\u5411\u3002 \u4e2d\u7684\u6240\u6709\u8a5e\u5f59\u4e4b\u9593\u5171\u540c\u51fa\u73fe\u8207\u76f8\u9130\u8cc7\u8a0a\u7684\u95dc\u4fc2\u3002\u5176\u5047\u8a2d\u4eba\u985e\u8a9e\u8a00\u751f\u6210(Human Language</td></tr><tr><td>\u8981\u662f\u4f9d\u64da\u4e8b\u5148\u5b9a\u7fa9\u597d\u7684\u6458\u8981\u6bd4\u4f8b\uff0c\u9078\u53d6\u80fd\u5920\u4ee3\u8868\u6587\u4ef6\u7684\u53e5\u5b50\u7576\u4f5c\u6458\u8981\uff1b\u800c\u591a\u91cd\u6587\u4ef6\u6458\u8981 \u4e8c\u3001\u81ea\u52d5\u6458\u8981\u6a21\u578b\u6280\u8853 Generation)\u662f\u4e00\u500b\u96a8\u6a5f\u904e\u7a0b\uff0c\u800c\u8a9e\u8a00\u6a21\u578b\u5c31\u662f\u5728\u6a21\u64ec\u5982\u4f55\u7531\u8a5e\u5f59\u69cb\u6210\u7247\u8a9e\u3001\u8a9e\u53e5\u3001\u6bb5\u843d</td></tr><tr><td>\u662f\u6536\u96c6\u591a\u7bc7\u76f8\u4f3c\u7684\u6587\u4ef6\uff0c\u9700\u8981\u79fb\u9664\u6587\u4ef6\u9593\u5f7c\u6b64\u5197\u9918\u6027(Redundancy)\u7684\u8cc7\u8a0a[4]\uff0c\u8003\u616e\u6587\u4ef6 \u6216 \u8005 \u6587 \u4ef6 \u4e4b \u904e \u7a0b \u7684 \u6a5f \u7387 \u6a21 \u578b \uff0c \u6545 \u53c8 \u7a31 \u70ba \u751f \u6210 \u5f0f \u8a9e \u8a00 \u6a21 \u578b (Generative Language</td></tr><tr><td>\u63cf\u8ff0\u4e8b\u4ef6\u767c\u751f\u7684\u5148\u5f8c\u9806\u5e8f(Causality)[12]\uff0c\u4e26\u4e14\u78ba\u8a8d\u6587\u4ef6\u4e4b\u9593\u7684\u56e0\u679c\u95dc\u4fc2\uff0c\u7d93\u7531\u9019\u4e9b\u8cc7 \u672c\u8ad6\u6587\u5c07\u904e\u53bb\u6458\u8981\u7814\u7a76\u6240\u9678\u7e8c\u767c\u5c55\u51fa\u7684\u81ea\u52d5\u6458\u8981\u6a21\u578b\u6280\u8853\u5927\u7565\u5730\u6b78\u7d0d\u6210\u4e09\u5927\u985e[22]\uff1a Modeling)[36]\u3002\u6700\u7c21\u55ae\u7684\u8a9e\u8a00\u6a21\u578b\u70ba\u55ae\u9023\u8a9e\u8a00\u6a21\u578b(Unigram Language Model, ULM)\uff0c\u5b83</td></tr><tr><td>\u8a0a\u5e0c\u671b\u80fd\u7522\u751f\u6709\u9023\u8cab\u6027\u7684\u6587\u4ef6\u6458\u8981\u3002 1. \u4ee5\u7c21\u55ae\u8a5e\u5f59\u8207\u7d50\u69cb\u7279\u5fb5\u70ba\u57fa\u790e\u4e4b\u81ea\u52d5\u6458\u8981\u6a21\u578b\u6280\u8853\uff1a\u5728 1950 \u5e74\u4ee3\uff0c\u6709\u5b78\u8005\u63d0\u51fa \u4e0d\u8003\u616e\u8a5e\u5f59\u4e4b\u9593\u7684\u9806\u5e8f\u95dc\u4fc2\uff0c\u53ea\u500b\u5225\u8003\u616e\u6bcf\u4e00\u500b\u8a5e\u672c\u8eab\u51fa\u73fe\u7684\u6a5f\u7387\u3002\u8f03\u70ba\u8907\u96dc\u4e14\u5e38\u88ab\u4f7f</td></tr><tr><td>2. \u9700\u6c42\uff1a\u4f9d\u64da\u4f7f\u7528\u8005\u9700\u6c42\u4e0d\u540c\uff0c\u6458\u8981\u5167\u5bb9\u53ef\u5340\u5206\u70ba\u5177\u6709\u8cc7\u8a0a\u6027(Informative)\u3001\u6307\u793a \u4f7f\u7528\u8a5e\u983b(Frequency)\u4f86\u8a55\u91cf\u6bcf\u4e00\u500b\u8a5e\u7684\u91cd\u8981\u6027\u8207\u8a08\u7b97\u6587\u4ef6\u4e2d\u6bcf\u4e00\u500b\u8a9e\u53e5\u7684\u986f\u8457\u6027 \u7528\u7684\u8a9e\u8a00\u6a21\u578b\u70ba N-\u9023\u8a9e\u8a00\u6a21\u578b\uff0c\u901a\u5e38 N \u70ba 2 \u6216 3(\u5373\u4e8c\u9023\u6216\u4e09\u9023\u8a9e\u8a00\u6a21\u578b) \uff0c\u5176\u8003\u616e\u5169</td></tr><tr><td>\u6027(Indicative)\u3001\u4ee5\u53ca\u8a55\uf941\u6027(Critical)\u3002\u5177\u6709\u8cc7\u8a0a\u6027\u7684\u6458\u8981\u662f\u7528\u4f86\u8868\u9054\u6587\u4ef6\u63cf\u8ff0\u7684\u4e3b\u65e8\u5167 (Significance Factor)[21]\u3002\u5728\u5be6\u4f5c\u4e0a\uff0c\u53ef\u4ee5\u5c0d\u6bcf\u4e00\u500b\u8a5e\u9032\u884c\u8a5e\u5e79\u5206\u6790(Stemming)\uff0c\u5c07\u5176\u9084 \u500b\u8a5e\u5f59\u6216\u4e09\u500b\u8a5e\u5f59\u4e4b\u9593\u7684\u5171\u540c\u51fa\u73fe\u8207\u7dca\u9023\u7684\u9806\u5e8f\u95dc\u4fc2\u3002\u503c\u5f97\u4e00\u63d0\u7684\u662f\uff0c\u55ae\u9023\u8a9e\u8a00\u6a21\u578b\u548c</td></tr><tr><td>\u5bb9\u8207\u6838\u5fc3\u8cc7\u8a0a\uff1b\u5177\u6307\u793a\u6027\u7684\u6458\u8981\u662f\u5e0c\u671b\u5c07\u6587\u4ef6\u4e2d\u7684\u4e3b\u984c\u5167\u5bb9\u505a\u7c21\u55ae\u7684\u63cf\u8ff0\uff0c\u4e26\u5c07\u6587\u4ef6\u5206 \u539f\u6210\u8a5e\u6839(Root Form)\uff0c\u540c\u6642\u79fb\u9664\u505c\u7528\u8a5e(Stop Word)\u7684\u5f71\u97ff\u4e26\u8a08\u7b97\u5be6\u8a5e(Content Word)\u7684 N-\u9023\u8a9e\u8a00\u6a21\u578b\u7684\u4e3b\u8981\u512a\u9ede\u4e4b\u4e00\u662f\uff1a\u5b83\u5011\u50c5\u9700\u4f7f\u7528\u8a13\u7df4\u8a9e\u6599\u4f86\u4f30\u6e2c\u6bcf\u4e00\u500b\u8a5e\u672c\u8eab\u51fa\u73fe\u7684\u6a5f</td></tr><tr><td>\u6210\u4e0d\u540c\u7684\u4e3b\u984c\uff0c\u4f8b\u5982\uff1a\u653f\u6cbb\u6027\u3001\u5b78\u8853\u6027\u3001\u9ad4\u80b2\u6027\u548c\u5a1b\u6a02\u6027\u6587\u4ef6\uff0c\u56e0\u6b64\u6240\u7522\u751f\u7684\u6458\u8981\u4e0d\u8981 \u91cd\u8981\u6027\u7b49\uff0c\u6700\u5f8c\u5c07\u8a9e\u53e5\u4f9d\u5176\u986f\u8457\u5206\u6578\u9032\u884c\u6392\u5e8f(\u7531\u9ad8\u81f3\u4f4e)\uff0c\u518d\u6839\u64da\u7279\u5b9a\u7684\u6458\u8981\u6bd4\u4f8b\u4f86\u9032 \u7387\u5206\u4f48\uff0c\u6216\u8005\u8a5e\u5f59\u4e4b\u9593\u7684\u5171\u540c\u51fa\u73fe\u8207\u9130\u8fd1\u95dc\u4fc2\u7684\u6a5f\u7387\u5206\u4f48\uff0c\u4e26\u4e0d\u9700\u8981\u984d\u5916\u7684\u4eba\u5de5\u6a19\u8a18\u8cc7</td></tr><tr><td>\u6c42\u50b3\u9054\u8a73\u7d30\u7684\u539f\u59cb\u6587\u4ef6\u5167\u5bb9\uff1b\u5177\u8a55\uf941\u6027\u7684\u6458\u8981\u63d0\u4f9b\u6587\u4ef6\u6b63\u9762\u8207\u53cd\u9762\u7684\u89c0\u9ede(Positive and \u884c\u7bc0\uf93f\u5f0f\u6458\u8981\u7684\u7522\u751f\u3002\u5f8c\u4f86\uff0c\u6709\u5b78\u8005\u5229\u7528\u81ea\u7136\u8a9e\u8a00\u5206\u6790(Natural Language Analysis)\u6280\u8853 \u8a0a\uff0c\u56e0\u6b64\u8a9e\u8a00\u6a21\u578b\u662f\u5c6c\u65bc\u57fa\u65bc\u975e\u76e3\u7763\u5f0f\u6a5f\u5668\u5b78\u7fd2\u4e4b\u6a21\u578b\u6280\u8853\u3002</td></tr><tr><td>Negative Sentiments)[9]\u3002 \u5c0d\u6587\u4ef6\u7d50\u69cb\u9032\u884c\u5256\u6790\uff0c\u6839\u64da\u6587\u6cd5\u7d50\u69cb(Grammar Structure)\u8207\u8a9e\u8a00\u6a5f\u5236(Linguistic Devices) \u5728\u904e\u53bb\u5e7e\uf98e\u4e2d\uff0c\u8a9e\u8a00\u6a21\u578b\u5728\u8cc7\u8a0a\u6aa2\u7d22\u4efb\u52d9\u4e2d\u5df2\u88ab\u5ee3\u6cdb\u5730\u61c9\u7528\u4e14\u6709\u4e0d\u932f\u7684\u5be6\u52d9\u6210\u6548</td></tr><tr><td>3. \u65b9\u5f0f\uff1a\u53ef\u6982\u5206\u70ba\u4e8c\u5927\u985e\uff0c\u7bc0\uf93f\u5f0f(Extractive)\u6458\u8981\u8207\u62bd\u8c61\u5f0f(Abstractive)\u6458\u8981(\u6216\u91cd \u4f86\u6c7a\u5b9a\u4e0d\u540c\u8a9e\u6bb5\u7684\u51dd\u805a\u95dc\u4fc2(Cohesion)\uff0c\u4f8b\u5982\uff1a\u9996\u8a9e\u91cd\u8907(Anaphora)\u3001\u7701\u7565(Ellipsis)\u3001\u7d50 [36]\uff1b\u4f46\u5c31\u6211\u5011\u6240\u77e5\uff0c\u5728\u8a9e\u97f3\u6587\u4ef6\u6458\u8981\u7684\u4efb\u52d9\u4e0a\uff0c\u95dc\u65bc\u4f7f\u7528\u8a9e\u8a00\u6a21\u578b\u7684\u7814\u7a76\u662f\u76f8\u5c0d\u8f03\u5c11</td></tr><tr><td>\u5beb\u5f0f\u6458\u8981)\u3002\u524d\u8005\u4e3b\u8981\u662f\u4f9d\u64da\u7279\u5b9a\u7684\u6458\u8981\u6bd4\u4f8b\uff0c\u5f9e\u6700\u539f\u59cb\u7684\u6587\u4ef6\u4e2d\u9078\u53d6\u91cd\u8981\u7684\u8a9e\u53e5\u4f86\u7d44 \u5408 (Conjunction) \uff0c \u6216 \u540c \u7fa9 \u8a5e (Synonymy) \u3001 \u4e0a \u7fa9 \u8a5e (Hypernym) \u7b49 \u8a9e \u5f59 \u95dc \u4fc2 (Lexical \u7684\u3002\u672c\u8ad6\u6587\u5c07\u85c9\u7531\u8a9e\u8a00\u6a21\u578b\u7684\u4f7f\u7528\u4f86\u9032\u884c\u6458\u8981\u8a9e\u53e5\u9078\u53d6\uff0c\u5176\u57fa\u672c\u65b9\u6cd5\u4e3b\u8981\u53ef\u5206\u70ba\u5169\u7a2e\uff0c</td></tr><tr><td>\u6210\u6458\u8981\uff1b\u800c\u5f8c\u8005\u662f\u5728\u5b8c\u5168\u7406\u89e3\u6587\u4ef6\u5167\u5bb9\u4e4b\u5f8c\uff0c\u91cd\u65b0\u64b0\u5beb\u7522\u751f\u6458\u8981\u4f86\u4ee3\u8868\u539f\u59cb\u6587\u4ef6\u7684\u5167 Relation)\uff0c\u4e26\u4ee5\u6b64\u7d50\u679c\u9032\u884c\u6587\u4ef6\u81ea\u52d5\u6458\u8981\u3002\u76f8\u95dc\u7814\u7a76\u5305\u62ec\u4f7f\u7528\u8a9e\u5f59\u93c8(Lexical Chain)[1]\u3001 \u7b2c\u4e00\u70ba\u4f7f\u7528\u8a9e\u53e5\u8a9e\u8a00\u6a21\u578b\u751f\u6210\u6587\u4ef6\u7684\u6587\u4ef6\u76f8\u4f3c\u5ea6\u91cf\u503c(Document Likelihood Measure,</td></tr><tr><td>\u5bb9\uff0c\u5176\u6240\u4f7f\u7528\u4e4b\u8a9e\u5f59\u6216\u6163\u7528\u8a9e\u4e0d\u4e00\u5b9a\u662f\u5168\u7136\u5730\u4f86\u81ea\u65bc\u539f\u59cb\u6587\u4ef6\uff0c\u6b64\u7a2e\u6458\u8981\u65b9\u5f0f\u662f\u6700\u70ba\u8cbc \u5b8f\u89c0\u8a9e\u6bb5\u7d50\u69cb(Discourse Macro Structure)[30]\u3001\u4fee\u8fad\u7d50\u69cb(Rhetorical Structure)[38]\u7b49\u3002\u53e6 DLM)[5]\uff0c\u7b2c\u4e8c\u70ba\u4f7f\u7528\u5eab\u723e\u8c9d\u514b-\u840a\u4f2f\u52d2\u96e2\u6563\u5ea6\u91cf\u503c(Kullback-Leibler Divergence Measure,</td></tr><tr><td>\u6709\u5b78\u8005\u5728\u5be9\u8996 200 \u7bc7\u79d1\u6280\u6587\u4ef6\u5f8c\uff0c\u767c\u73fe\u6709 85%\u7684\u91cd\u8981\u8a9e\u53e5\u51fa\u73fe\u5728\u6587\u4ef6\u4e2d\u7684\u7b2c\u4e00\u6bb5\uff0c7% \u7684\u91cd\u8981\u8a9e\u53e5\u51fa\u73fe\u5728\u6700\u5f8c\u4e00\u6bb5[2]\u3002\u56e0\u6b64\uff0c\u63d0\u51fa\u4e86\u8a9e\u53e5\u5728\u6587\u4ef6\u4e2d\u7684\u4f4d\u7f6e(Position)\u8cc7\u8a0a\u662f\u9032 2. \u4ee5\u975e\u76e3\u7763\u5f0f\u6a5f\u5668\u5b78\u7fd2\u70ba\u57fa\u790e\u4e4b\u81ea\u52d5\u6458\u8981\u6a21\u578b\u6280\u8853\uff1a\u975e\u76e3\u7763\u5f0f\u6a5f\u5668\u5b78\u7fd2\u901a\u5e38\u5c07\u81ea \u52d5\u6458\u8981\u4efb\u52d9\u8996\u70ba\u5982\u4f55\u6392\u5e8f\u4e26\u6311\u9078\u5177\u4ee3\u8868\u6027\u8a9e\u53e5\u4e4b\u554f\u984c\uff0c\u5176\u65b9\u6cd5\u901a\u5e38\u662f\u8a08\u7b97\u51fa\u4e00\u7a2e\u6458\u8981\u7279 \u5fb5\u4f9b\u8a9e\u53e5\u6392\u5e8f\u4f7f\u7528\uff0c\u5e38\u898b\u7684\u7279\u5fb5\u6709\uff1a\u8a9e\u53e5\u8207\u6587\u4ef6\u76f8\u95dc\u6027[10]\u3001\u8a9e\u53e5\u6240\u5f62\u6210\u7684\u8a9e\u8a00\u6a21\u578b\u751f \u6210\u6587\u4ef6\u4e4b\u6a5f\uf961\u7b49[5]\u3001\u8a9e\u53e5\u9593\u4e4b\u76f8\u95dc\u6027[23][32]\u3001\u6216\u8a9e\u53e5\u8207\u6587\u4ef6\u5728\u6f5b\u85cf\u4e3b\u984c\u7a7a\u9593\u4e2d\u7684\u8ddd\u96e2 \u95dc\u4fc2[17]\u7b49\u3002 3. \u4ee5\u76e3\u7763\u5f0f\u6a5f\u5668\u5b78\u7fd2\u70ba\u57fa\u790e\u4e4b\u81ea\u52d5\u6458\u8981\u6a21\u578b\u6280\u8853\uff1a\u76e3\u7763\u5f0f\u6a5f\u5668\u5b78\u7fd2\u901a\u5e38\u5c07\u81ea\u52d5\u6458 \u8981\u4e4b\u4efb\u52d9\u8996\u70ba\u4e8c\u5143\u5206\u985e\u554f\u984c(Binary Classification)\uff0c\u4ea6\u5373\u5c07\u8a9e\u53e5\u5340\u5206\u70ba\u6458\u8981\u8a9e\u53e5\u6216\u975e\u6458 \u8981\u8a9e\u53e5\u3002\u6211\u5011\u5fc5\u9808\u4e8b\u5148\u6e96\u5099\u597d\u4e00\u4e9b\u8a13\u7df4\u6587\u4ef6\u4ee5\u53ca\u5176\u5c0d\u61c9\u7684\u4eba\u5de5\u6a19\u8a3b\u6458\u8981\u8cc7\u8a0a\uff0c\u7136\u5f8c\u900f\u904e \u5404\u7a2e\u5206\u985e\u5668\u7684\u5b78\u7fd2\u6a5f\u5236\uff0c\u9032\u884c\u5206\u985e\u6a21\u578b\u7684\u8a13\u7df4\u3002\u5c0d\u65bc\u5c1a\u672a\u88ab\u6458\u8981\u4e4b\u6587\u4ef6\uff0c\u6b64\u985e\u65b9\u6cd5\u5c07\u6587 KL)[17][18]\u3002\u6b64\u5916\uff0c\u672c\u7ae0\u7b2c 3 \u5c0f\u7bc0\u6211\u5011\u5c07\u95e1\u8ff0\u5982\u4f55\u984d\u5916\u5730\u8003\u91cf\u4f7f\u7528\u660e\u78ba\u5ea6\u91cf\u503c\u65bc\u8f14\u52a9\u6458 \u8981\u8a9e\u53e5\u4e4b\u9078\u53d6\uff0c\u4e26\u5728\u7b2c 4 \u5c0f\u7bc0\u63d0\u51fa\u4f7f\u7528\u57fa\u65bc\u95dc\u806f\u6027\u8cc7\u8a0a\u4f86\u6539\u9032\u8a9e\u53e5\u6a21\u578b\u4e4b\u4f30\u6e2c\uff0c\u4f7f\u5176\u5f97 \u8fd1 \u4eba \u5011 \u65e5 \u5668\u5b78\u7fd2\u7684\u65b9\u6cd5\u5728\u4e00\u822c\u7684\u60c5\u6cc1\u4e0b\u5176\u6548\u80fd\u6c92\u6709\u76e3\u7763\u5f0f\u6a5f\u5668\u5b78\u7fd2\u65b9\u6cd5\u4f86\u7684\u597d\uff0c\u4f46\u975e\u76e3\u7763\u5f0f\u6a5f\u5668 \u884c\u6458\u8981\u8a9e\u53e5\u9078\u53d6\u6642\u7684\u4e00\u9805\u95dc\u9375\u7dda\u7d22\u3002 \u4ee5\u66f4\u7cbe\u6e96\u7684\u4ee3\u8868\u8a9e\u53e5\u7684\u8a9e\u610f\u5167\u5bb9\u3002</td></tr><tr><td>\u5b78 \u7fd2 \u65b9 \u6cd5 \u4e0d \u9700 \u8981 \u4e8b \u5148 \u6e96 \u5099 \u5927 \u91cf \u4eba \u5de5 \u6a19 \u8a18 \u7684 \u8a13 \u7df4 \u8cc7 \u6599 \uff0c \u4ee5 \u53ca \u5177 \u6709 \u5bb9 \u6613 \u5be6 \u4f5c \u4ef6\u88e1\u7684\u6bcf\u500b\u8a9e\u53e5\u9032\u884c\u4e8c\u5143\u5206\u985e\uff0c\u5373\u53ef\u4f9d\u5176\u7d50\u679c\u7522\u751f\u51fa\u6458\u8981\u3002\u6b64\u985e\u65b9\u6cd5\u8f03\u8457\u540d\u7684\u76f8\u95dc\u7814\u7a76</td></tr><tr><td>(Easy-to-Implement)\u7684\u7279\u6027\uff0c\u4ecd\u5438\u5f15\u8a31\u591a\u5b78\u8005\u9032\u884c\u7814\u7a76\u8207\u767c\u5c55\uff0c\u672c\u8ad6\u6587\u4e3b\u8981\u4e5f\u662f\u63a1\u7528\u975e \u5305\u62ec\u7c21\u55ae\u8c9d\u6c0f\u5206\u985e\u5668(Na\u00efve-Bayes Classifier)[13]\u3001\u9ad8\u65af\u6df7\u5408\u6a21\u578b(Gaussian Mixture Model,</td></tr><tr><td>\u76e3\u7763\u5f0f\u6a5f\u5668\u5b78\u7fd2\u7684\u65b9\u5f0f\u4f86\u5b8c\u6210\u81ea\u52d5\u6458\u8981\u4e4b\u4efb\u52d9\u3002 GMM)[24]\u3001\u96b1\u85cf\u5f0f\u99ac\u53ef\u592b\u6a21\u578b(Hidden Markov Model, HMM)[8]\u3001\u652f\u63f4\u5411\u91cf\u6a5f(Support</td></tr><tr><td>Vector Machines, SVM)\u8207\u689d\u4ef6\u96a8\u6a5f\u5834\u57df(Conditional Random Fields, CRF)[28]\u7b49\u3002\u76e3\u7763\u5f0f \u7d9c\u89c0\u4e0a\u8ff0\u5404\u500b\u9762\u5411\uff0c\u672c\u8ad6\u6587\u4e3b\u8981\u63a2\u7a76\u4e00\u822c\u6027\u3001\u55ae\u4e00\u6587\u4ef6\u7bc0\uf93f\u5f0f\u8a9e\u97f3\u6458\u8981\u554f\u984c\uff0c\u4e26\u767c \u6a21\u578b\u53ef\u540c\u6642\u7d50\u5408\u591a\u7a2e\u6458\u8981\u7279\u5fb5\u4f86\u8868\u793a\u6bcf\u4e00\u8a9e\u53e5(\u901a\u5e38\u662f\u7531\u4e0a\u8ff0\u4ee5\u8a5e\u5f59\u6216\u7d50\u69cb\u70ba\u57fa\u790e\u4e4b\u6458 \u5c55\u548c\u6539\u9032\u975e\u76e3\u7763\u5f0f\u6a5f\u5668\u5b78\u7fd2\u6a21\u578b\u6280\u8853\u3002\u57fa\u65bc\u8fd1\uf98e\u4f86\uff0c\u8a9e\u8a00\u6a21\u578b\u7d50\u5408\u5eab\u723e\u8c9d\u514b-\u840a\u4f2f\u52d2\u96e2 \u8981\u65b9\u6cd5\u3001\u6216\u662f\u5404\u5f0f\u975e\u76e3\u7763\u5f0f\u6458\u8981\u6a21\u578b\u91dd\u5c0d\u8a9e\u53e5\u6240\u8f38\u51fa\u7684\u5206\u6578\u6216\u6a5f\uf961\u503c)\uff0c\u7d9c\u5408\u5404\u7a2e\u6458\u8981</td></tr></table>", |
| "type_str": "table", |
| "num": null |
| }, |
| "TABREF1": { |
| "text": "Proceedings of the Twenty-Fifth Conference on Computational Linguistics and Speech Processing(ROCLING 2013)", |
| "html": null, |
| "content": "<table/>", |
| "type_str": "table", |
| "num": null |
| }, |
| "TABREF2": { |
| "text": "LEAD \u4f86\u5f97\u5f70\u986f\u3002KL \u8207 VSM \u7686\u4f7f\u7528\u6dfa\u5c64\u7684\u8a5e\u5f59(\u8a5e\u983b)\u8cc7\u8a0a\uff0c\u4f46\u7531\u65bc KL \u662f\u8a08\u7b97\u8a9e\u53e5 \u6a21\u578b\u8207\u6587\u4ef6\u6a21\u578b\u4e4b\u9593\u7684\u8ddd\u96e2\u95dc\u4fc2\uff0c\u5c0d\u65bc\u4ee3\u8868\u8a9e\u53e5\u8207\u6587\u4ef6\u7684\u8a9e\u8a00\u6a21\u578b\uff0c\u6211\u5011\u8f03\u5bb9\u6613\u900f\u904e\u5404 \u7a2e\u6280\u8853\u4f86\u9032\u884c\u6a21\u578b\u7684\u4f30\u8a08\u8207\u8abf\u9069\uff0c\u9032\u800c\u7372\u5f97\u8f03\u597d\u7684\u6458\u8981\u6210\u679c\u3002\u53e6\u4e00\u65b9\u9762\uff0c\u5728 SD \u7684\u5be6\u9a57 \u4e2d\uff0cKL \u540c\u6a23\u8f03\u512a\u65bc LS\u3001LEAD \u4e4b\u6458\u8981\u65b9\u6cd5\uff0c\u4f46 VSM \u7684\u7d50\u679c\u5247\u7a0d\u5fae\u8f03 KL \u597d\u4e00\u9ede\uff0c\u6211 \u5011\u8a8d\u70ba\u9019\u53ef\u80fd\u662f\u56e0\u70ba VSM \u6bd4\u8f03\u4e0d\u53d7\u5230\u8a9e\u97f3\u8fa8\u8a8d\u932f\u8aa4\u7684\u5f71\u97ff\u3002 Proceedings of the Twenty-Fifth Conference on Computational Linguistics and Speech Processing (ROCLING 2013)", |
| "html": null, |
| "content": "<table><tr><td>Proceedings of the Twenty-Fifth Conference on Computational Linguistics and Speech Processing (ROCLING 2013) Proceedings of the Twenty-Fifth Conference on Computational Linguistics and Speech Processing (ROCLING 2013)</td></tr><tr><td>\u7684\u76ee\u6a19\u662f\u60f3\u9032\u4e00\u6b65\u5730\u6a21\u578b\u5316\u95dc\u806f\u985e\u5225\u6240\u4ee3\u8868\u7684\u8cc7\u8a0a\uff0c\u85c9\u6b64\u4f86\u8c50\u5bcc\u8a9e\u53e5\u6a21\u578b\u6240\u80fd\u50b3\u9054\u7684\u8a9e \u610f\u5167\u5bb9\u6216\u4e3b\u984c\u7279\u6027\u3002\u7136\u800c\uff0c\u5be6\u969b\u4e0a\u6bcf\u4e00\u8a9e\u53e5 S \u7684\u95dc\u806f\u985e\u5225 S \u8868\u4e00\u3001\u5be6\u9a57\u8a9e\u6599\u7d71\u8a08\u8cc7\u8a0a \u8868\u4e8c\u3001\u57fa\u790e\u5be6\u9a57\u7d50\u679c \u8868\u4e09\u3001\u8003\u91cf\u660e\u78ba\u5ea6\u91cf\u503c\u4e4b\u5be6\u9a57\u7d50\u679c R \u662f\u975e\u5e38\u96e3\u4ee5\u6c42\u5f97\u7684\uff1b\u70ba\u6b64\uff0c \u6211\u5011\u900f\u904e\u865b\u64ec\u95dc\u806f\u56de\u994b(Pseudo Relevant Feedback, PRF)\u4f86\u5c0b\u627e\u8207\u95dc\u806f\u985e\u5225\u53ef\u80fd\u76f8\u95dc\u7684 \u4e00\u4e9b\u6587\u4ef6\uff0c\u4e26\u85c9\u7531\u9019\u4e9b\u6587\u4ef6\u4f86\u8fd1\u4f3c\u95dc\u806f\u985e\u5225\u3002\u66f4\u660e\u78ba\u5730\uff0c\u5728\u5be6\u4f5c\u4e0a\u6211\u5011\u9996\u5148\u628a\u6bcf\u4e00\u8a9e\u53e5 S \u7576\u4f5c\u67e5\u8a62(Query)\uff0c\u4ee3\u8868\u4e00\u500b\u8cc7\u8a0a\u9700\u6c42(Information Need)\uff0c\u8f38\u5165\u5230\u4e00\u500b\u8cc7\u8a0a\u6aa2\u7d22\u7cfb\u7d71 \u4e2d\uff0c\u627e\u51fa\u4e00\u4e9b\u8207\u8a9e\u53e5 S \u76f8\u95dc\u7684\u95dc\u806f\u6587\u4ef6 } , , { 1 Top M D D \uf04b \uf03d D \uff0c\u7a31\u4e4b\u70ba\u865b\u64ec\u95dc\u806f\u6587\u4ef6(Pseudo Relevant Documents)\uff0c\u7528\u4ee5\u4ee3\u8868\u95dc\u806f\u985e\u5225 S R \u3002\u63a5\u8457\uff0c\u900f\u904e\u6aa2\u8996\u8a5e\u5f59 w \u8207\u8a9e\u53e5 S \u5728\u9019\u4e9b\u865b \u8a13\u7df4\u96c6 \u6e2c\u8a66\u96c6 \u8a9e\u6599\u6642\u9593 2001/11/07-2002/01/22 F-score (%) F-score (%) 2002/01/23-2002/08/22 \u6587\u4ef6\u500b\u6578 185 ROUGE-1 ROUGE-2 ROUGE-L ROUGE-1 ROUGE-2 ROUGE-L 20 \u6587\u4ef6\u5e73\u5747\u6301\u7e8c\u5e7e\u79d2 129.4 LS 22.5 09.8 18.3 KL 41.1 29.8 36.1 141.2 \u6587\u4ef6\u5e73\u5747\u8a5e\u500b\u6578 326.0 290.3 \u6587\u4ef6\u5e73\u5747\u8a9e\u53e5\u500b\u6578 20.0 23.3 LEAD 31.0 19.4 27.6 KL+CE 41.1 29.9 36.2 TD VSM 34.7 22.8 TD 29.0 KL+SE 44.0 32.6 38.6</td></tr><tr><td>\u64ec\u95dc\u806f\u6587\u4ef6\u4e2d\u540c\u6642\u51fa\u73fe\u4e4b\u95dc\u4fc2\uff0c\u53ef\u8a08\u7b97\u51fa\u8a5e\u5f59\u8207\u8a9e\u53e5\u7684\u806f\u5408\u6a5f\u7387[14]\uff1a \uf0e5 \uf03d \uf0ce Top ) ( ) | , ( ) , ( RM D j D j j D P D S w P S w P \u7576\u6211\u5011\u9032\u4e00\u6b65\u5730\u5047\u8a2d\u5728\u7d66\u5b9a\u67d0\u4e00\u7bc7\u865b\u64ec\u95dc\u806f\u6587\u4ef6\u6642\uff0c\u8a5e\u5f59\u8207\u8a9e\u53e5\u662f\u7368\u7acb\u7684\uff0c\u4e26\u4e14\u8a9e\u53e5\u5167 (13) \u6587\u4ef6\u5e73\u5747\u5b57\u932f\u8aa4\uf961 (Character Error Rate, CER) 28.8% KL 41.1 29.8 36.1 KL+Clarity 44.7 33.5 39.3 29.8% \u6587\u4ef6\u5e73\u5747\u8a5e\u932f\u8aa4\uf961 KL+SE 39.6 25.3 34.7 (Word Error Rate, WER) 38.0% 39.4% VSM 34.2 18.9 28.7 SD SD LS 18.1 04.4 13.8 LEAD 25.5 11.7 KL+CE 36.4 21.9 31.2 22.1 KL 36.4 21.0 30.7 \u5716\u4e00\u3001KL \u8207 LEAD \u4e4b\u5e73\u5747\u6b63\u78ba\u7387\u5747\u503c\u6bd4\u8f03</td></tr><tr><td>KL \u7684\u8a5e\u5f59\u4e5f\u662f\u7368\u7acb\u4e14\u4e0d\u8003\u616e\u5176\u5148\u5f8c\u6b21\u5e8f(\u5373\u6240\u8b02\u7684\u8a5e\u888b\u5047\u8a2d)\uff0c\u5247\u900f\u904e\u865b\u64ec\u95dc\u806f\u56de\u994b\u6240\u4f30\u6e2c 36.4 21.0 30.7 \u7684\u8a9e\u53e5\u6a21\u578b\u70ba\uff1a 2001 \u5230 2002 \uf98e\u7684\u4e2d\u592e\u793e\u65b0\u805e\u6587\u5b57\u8a9e\u6599(Central News Agency, CNA)\uff0c\u4e26\u4e14\u4ee5 SRI \u8a9e\u8a00\u6a21 KL+Clarity 40.3 26.1 35.4</td></tr><tr><td>\u578b\u5de5\u5177[29]\u8a13\u7df4\u51fa\u7d93\u5e73\u6ed1\u5316\u7684\u55ae\u9023\u8a9e\u8a00\u6a21\u578b\uff0c\u6211\u5011\u5047\u8a2d\u6b64\u55ae\u9023\u8a9e\u8a00\u6a21\u578b\u70ba\u660e\u78ba\u5ea6\u4e2d\u7684\u975e \u542b\u6709\u6700\u9577\u8a9e\u53e5\u6458\u8981(Longest Sentence, LS)\u3001\u9996\u53e5\u6458\u8981(LEAD)[27]\u4ee5\u53ca\u5411\u91cf\u7a7a\u9593\u6a21\u578b</td></tr><tr><td>) ( ' D P ) j D j ( D P ) | (Vector Space Model, VSM)[36]\u3002\u4e00\u822c\u4f86\u8aaa\uff0c\u6587\u4ef6\u4e2d\u9577\u53e5\u53ef\u80fd\u860a\u542b\u6709\u8f03\u8c50\u5bcc\u7684\u4e3b\u984c\u8cc7\u8a0a\uff0c | ' ' ( ) ( ) | ' ( ) | ( ' ' ' ' RM Top ' Top D S w j j D S w j D w P w P D w P S w P j j \uf0e5 \uf0d5 \uf0e5 \uf0d5 \uf03d \uf0ce \uf0ce \uf0ce \uf0ce D D (14) \u53ef\u4ee5\u7372\u5f97\u66f4\u597d\u7684\u6458\u8981\u6548\u679c\uff0c\u9019\u662f\u56e0\u70ba\u5eab\u723e\u8c9d\u514b-\u840a\u4f2f\u52d2\u96e2\u6563\u5ea6\u91cf\u503c\u6108\u5c0f\uff0c\u8868\u793a\u8a9e\u53e5\u8207\u88ab \u76f8\u95dc\u8cc7\u8a0a\u4e4b\u4f86\u6e90\u3002\u53e6\u5916\uff0c\u672c\u8ad6\u6587\u8490\u96c6 2002 \uf98e\u4e2d\u592e\u901a\u8a0a\u793e\u7684 101,268 \u5247\u540c\u6642\u671f\u65b0\u805e\u6587\u5b57 \u6458\u8981\u6587\u4ef6\u7684\u76f8\u4f3c\u5ea6\u61c9\u5c07\u6703\u6108\u5927\uff1b\u8a9e\u53e5\u660e\u78ba\u5ea6\u91cf\u503c\u6108\u5927\uff0c\u5247\u6108\u6709\u53ef\u80fd\u8868\u793a\u8a9e\u53e5\u4e0d\u50c5\u5177\u6709 \u56e0\u6b64\u4f9d\u64da\u6587\u4ef6\u4e2d\u8a9e\u53e5\u9577\u5ea6\u505a\u6392\u5e8f\u5f8c\uff0c\u4f9d\u5e8f\u9078\u53d6\u6700\u9577\u8a9e\u53e5\u505a\u70ba\u6458\u8981\u7d50\u679c\u662f\u4e00\u7a2e\u7c21\u55ae\u7684\u6458\u8981 \u6587\u4ef6\u505a\u70ba\u5efa\uf9f7\u95dc\u806f\u6a21\u578b\u6642\u7684\u6aa2\u7d22\u6a19\u7684[6]\u3002 \u65b9\u6cd5\u3002\u9664\u6b64\u4e4b\u5916\uff0c\u4e5f\u6709\u5b78\u8005\u7814\u7a76\u767c\u73fe\uff0c\u6587\u4ef6\u5e38\u4ee5\u958b\u9580\u898b\u5c71\u6cd5\u7684\u65b9\u5f0f\u4f86\u63d0\u9ede\u51fa\u4e3b\u984c\uff0c\u56e0\u6b64 \u7368\u7279\u6027\u4e14\u80fd\u660e\u78ba\u5448\u73fe\u88ab\u6458\u8981\u6587\u4ef6\u4e4b\u4e3b\u984c\uff0c\u7d9c\u5408\u9019\u5169\u500b\u9762\u5411\u5f8c\uff0c\u53ef\u6311\u9078\u51fa\u8207\u88ab\u6458\u8981\u6587\u4ef6</td></tr><tr><td>\u6211\u5011\u7a31\u4e4b\u70ba\u95dc\u806f\u6a21\u578b(Relevance Model, RM)\u3002\u95dc\u806f\u6a21\u578b\u7684\u512a\u9ede\u5728\u65bc\u85c9\u7531\u865b\u64ec\u95dc\u806f\u6587\u4ef6\u7684 \u6587\u4ef6\u958b\u982d\u7684\u524d\u5e7e\u500b\u8a9e\u53e5\u7d93\u5e38\u662f\u5177\u4ee3\u8868\u6027\u7684\u8a9e\u53e5\uff0c\u9996\u53e5\u6458\u8981\u5373\u662f\u4ee5\u6b64\u6982\u5ff5\u51fa\u767c\uff0c\u9078\u53d6\u524d\u5e7e \u76f8\u4f3c\u5ea6\u9ad8\u4e26\u4e14\u8a00\u7c21\u610f\u8cc5\u7684\u8a9e\u53e5\u4f86\u5f62\u6210\u6458\u8981\u3002\u6211\u5011\u540c\u6642\u5206\u6790\u4e86 KL\u3001KL+Clarity \u53ca\u4eba\u5de5\u6240 2\u3001\u8a55\u4f30\u65b9\u6cd5 \u9078\u7684\u5e73\u5747\u6458\u8981\u8a9e\u53e5\u9577\u5ea6\u5206\u5225\u70ba 22.7\u300120.3 \u4ee5\u53ca 17.2 \u500b\u8a5e\u5f59\uff0c\u7531\u6b64\u53ef\u77e5 KL+Clarity \u6240\u9078 \u53e5\u8a9e\u53e5\u4f86\u5f62\u6210\u6574\u500b\u6587\u4ef6\u7684\u6458\u8981\u3002\u6700\u9577\u8a9e\u53e5\u6458\u8981(LS)\u53ca\u9996\u53e5\u6458\u8981(LEAD)\u90fd\u50c5\u9069\u7528\u5728\u4e00\u90e8 \u8cc7\u8a0a\uff0c\u53ef\u4ee5\u66f4\u6e05\u695a\u5730\u77e5\u9053\u8a9e\u53e5\u6240\u860a\u542b\u7684\u8cc7\u8a0a\u3001\u6240\u6b32\u8868\u9054\u7684\u5167\u6db5\uff0c\u6240\u4ee5\u76f8\u8f03\u65bc\u50b3\u7d71\u4f7f\u7528\u6700 \uf0e5 \uf0d7 \uf02d \uf02b \uf0d7 \uf03d \uf0ceV w S w P S w P D w P D w P S D KL ) | ( ) 1 ( ) | ( ) | log ) | ( ) || ( RM \uf067 \u6458\u8981\u65b9\u6cd5\u7686\u63a1\u7528\u53ec\u56de\uf961\u5c0e\u5411\u7684\u8981\u9ede\u8a55\u4f30\u505a\u70ba\u6587\u4ef6\u6458\u8981\u7684\u8a55\u4f30\u65b9\u5f0f\uff0c\u672c\u8ad6\u6587\u4ea6\u63a1\u7528\u6b64\u7a2e\u8a55 \u53ca VSM \u7b49\u975e\u76e3\u7763\u5f0f\u6458\u8981\u65b9\u6cd5\u4f86\u5f97\u597d\u4e9b\uff1b\u56e0 LS \u8207 LEAD \u50c5\u9069\u7528\u65bc\u7279\u6b8a\u6587\u7ae0\u7d50\u69cb\u4e0a\uff0c\u6240 \u8a0a\u8907\u96dc\u5ea6\u5728\u6458\u8981\u8a9e\u53e5\u7684\u9078\u53d6\u4e0a\u662f\u76f8\u7576\u91cd\u8981\u7684\uff0c\u56e0\u70ba\u5b83\u53ef\u4ee5\u8868\u73fe\u51fa\u8a9e\u53e5\u672c\u8eab\u7684\u7368\u7279\u6027\uff0c\u4f7f \u6b63\u78ba\u7387\u5747\u503c\uff0c\u7531\u5716\u4e8c\u4e2d\u53ef\u89c0\u5bdf\u5230 KL+Clarity \u7684\u5e73\u5747\u6b63\u78ba\u7387\u5747\u503c\u5728\u6e2c\u8a66\u96c6\u7684\u5927\u591a\u6578\u6587 \uf067 (15) Gisting Evaluation, ROUGE)[19]\u3002\u7531\u65bc\u4e3b\u89c0\u4eba\u70ba\u8a55\u4f30\u975e\u5e38\u8017\u6642\u8017\u529b\uff0c\u6240\u4ee5\u76ee\u524d\u591a\u6578\u81ea\u52d5 \u8868\u4e8c\u70ba\u672c\u8ad6\u6587\u4e4b\u57fa\u790e\u5be6\u9a57\u7d50\u679c\u3002\u9996\u5148\uff0c\u5728 TD \u7684\u5be6\u9a57\u4e2d\uff0cKL \u7684\u6458\u8981\u6548\u679c\u6bd4 LS\u3001LEAD \u9032\u884c\u63a2\u8a0e\u3002\u5be6\u9a57\u7d50\u679c\u986f\u793a KL+SE \u6703\u6bd4 KL \u4ee5\u53ca KL+CE \u4f86\u5f97\u597d\uff0c\u9019\u500b\u7d50\u679c\u8aaa\u660e\u4e86\u8a9e\u53e5\u8cc7 \u5728\u9019\u5e7e\u7bc7\u6587\u4ef6\u53ef\u7372\u5f97\u4e00\u5b9a\u7a0b\u5ea6\u7684\u6458\u8981\u7d50\u679c\u3002\u63a5\u8457\uff0c\u6211\u5011\u6bd4\u8f03 KL \u8207 KL+Clarity \u4e4b\u5e73\u5747 ( \u8207\u6e2c\u8a66\u8005\u6240\u6311\u9078\u51fa\u7684\u53e5\u5b50\u8a08\u7b97\u53ec\u56de\uf961\u5c0e\u5411\u7684\u8981\u9ede\u8a55\u4f30(Recall-Oriented Understudy for (11))\u4ee5\u53ca\u5916\u5728\u8cc7\u8a0a-\u8a9e\u53e5\u8207\u88ab\u6458\u8981\u6587\u4ef6\u7684\u975e\u76f8\u95dc\u8cc7\u8a0a\u4e4b\u4ea4\u4e92\u4e82\u5ea6(KL+CE\uff0c\u53c3\u7167\u5f0f(12)) \u6211\u5011\u89c0\u5bdf\u90a3\u5e7e\u7bc7\u6587\u4ef6\u5f8c\u767c\u73fe\u5176\u6587\u7ae0\u7d50\u69cb\u662f\u4ee5\u958b\u9580\u898b\u5c71\u6cd5\u7684\u5f62\u5f0f\u4f86\u5448\u73fe\uff0c\u56e0\u6b64 LEAD \u8d8a\u6709\u6a5f\u6703\u6210\u70ba\u6b64\u6587\u4ef6\u7684\u6458\u8981[36]\u3002 \u7167\u5f0f(5))\u53ef\u9032\u4e00\u6b65\u5730\u8868\u793a\u6210\uff1a \u4f4d\u6e2c\u8a66\u8005\u4f9d\u64da\u4e8b\u5148\u5b9a\u7fa9\u597d\u7684\u6458\u8981\u6bd4\u4f8b\u6311\u9078\u51fa\u9069\u5408\u7684\u6458\u8981\u8a9e\u53e5\uff0c\u7cfb\u7d71\u6240\u7522\u751f\u7684\u6458\u8981\u53e5\u5b50\u5c07 \u53e6\u5916\uff0c\u672c\u8ad6\u6587\u4ea6\u91dd\u5c0d\u660e\u78ba\u5ea6\u91cf\u503c\u4e2d\u7684\u5167\u5728\u8cc7\u8a0a-\u8a9e\u53e5\u8cc7\u8a0a\u8907\u96dc\u5ea6(KL+SE\uff0c\u53c3\u7167\u5f0f \u5176\u5e73\u5747\u6b63\u78ba\u7387\u5747\u503c\u90fd\u5927\u65bc LEAD\uff0c\u552f\u6709\u5c11\u6578\u5e7e\u7bc7\u6587\u4ef6\u7684\u5e73\u5747\u6b63\u78ba\u7387\u5747\u503c\u4f4e\u65bc LEAD\uff0c \u6b0a\u91cd\u503c\uff0c\u6587\u4ef6\u8207\u8a9e\u53e5\u9593\u7684\u95dc\u806f\u6027\u662f\u85c9\u7531\u9918\u5f26\u76f8\u4f3c\u5ea6\u91cf\u503c\u4f86\u4f30\u6e2c\uff0c\u7576\u8a9e\u53e5\u5206\u6578\u8f03\u9ad8\u6642\uff0c\u5247 \u7684\u6210\u6548\u3002\u904b\u7528\u6b64\u4e00\u95dc\u806f\u6a21\u578b\u4f86\u8abf\u9069\u8a9e\u53e5\u6a21\u578b\u6642\uff0c\u5eab\u723e\u8c9d\u514b-\u840a\u4f2f\u52d2\u96e2\u6563\u5ea6\u91cf\u503c\u7684\u516c\u5f0f(\u53c3 \u5e7e\u4f4d\u6e2c\u8a66\u4eba\u54e1\u4f86\u70ba\u7cfb\u7d71\u6240\u7522\u751f\u7684\u6458\u8981\u505a\u8a55\u4f30\uff0c\u7d66\u5206\u7684\u7bc4\u570d\u70ba 1-5 \u5206\uff0c\u5f8c\u8005\u5247\u662f\u9810\u5148\u8acb\u5e7e \u6587\u4ef6\u548c\u8a9e\u53e5\u5206\u5225\u8996\u70ba\u4e00\u500b\u5411\u91cf\uff0c\u4e26\u4f7f\u7528\u8a5e\u983b-\u53cd\u6587\u4ef6\u983b(TF-IDF)\u7279\u5fb5\u4f86\u8a08\u7b97\u6bcf\u4e00\u7dad\u5ea6\u7684 KL \u6703\u6bd4\u8f03\u504f\u597d\u7c21\u77ed\u7684\u8a9e\u53e5\u3002 \u5927\u5316\u76f8\u4f3c\u5ea6\u4f30\u6e2c\u7684\u8a9e\u53e5\u6a21\u578b\uff0c\u53ef\u66f4\u6e96\u78ba\u5730\u8868\u9054\u8a9e\u53e5\u7684\u8a9e\u610f\u5167\u5bb9\u6216\u4e3b\u984c\u7279\u6027\uff0c\u4ee5\u63d0\u5347\u6458\u8981 \u81ea\u52d5\u6458\u8981\u7684\u8a55\u4f30\u65b9\u6cd5\u4e3b\u8981\u6709\u5169\u7a2e\uff0c\u4e00\u70ba\u4e3b\u89c0\u4eba\u70ba\u8a55\u4f30\uff0c\u53e6\u4e00\u70ba\u5ba2\u89c0\u81ea\u52d5\u8a55\u4f30\uff1b\u524d\u8005\u70ba\u8acb \u5206\u5177\u6709\u7279\u6b8a\u7d50\u69cb\u7684\u6587\u4ef6\u4e0a\uff0c\u56e0\u6b64\u5b83\u5011\u7684\u7f3a\u9ede\u5c31\u662f\u6709\u5176\u4fb7\u9650\u6027\u3002\u53e6\u5916\uff0c\u5411\u91cf\u7a7a\u9593\u6a21\u578b\u662f\u628a \u7684\u6458\u8981\u8a9e\u53e5\u6703\u6bd4\u8f03\u63a5\u8fd1\u4eba\u5de5\u6240\u6311\u9078\u6458\u8981\u8a9e\u53e5\u4e4b\u9577\u5ea6\uff0c\u4e5f\u53ef\u770b\u51fa\u4f7f\u7528 KL+Clarity \u76f8\u5c0d\u65bc \u5716\u4e8c\u3001KL \u8207\u660e\u78ba\u5ea6\u4e4b\u5e73\u5747\u6b63\u78ba\u7387\u5747\u503c\u6bd4\u8f03</td></tr><tr><td>\u5176\u4e2d \u4f30\u65b9\u5f0f\u3002ROUGE \u65b9\u6cd5\u662f\u8a08\u7b97\u81ea\u52d5\u6458\u8981\u7d50\u679c\u8207\u4eba\u5de5\u6458\u8981\u4e4b\u9593\u7684\u91cd\u758a\u55ae\u4f4d\u5143(Units)\u6578\u76ee\u5360 1 0 \uf03c \uf0a3 \uf067 \uff0c\u7576 0 \uf03d \uf067 \u4ee3\u8868\u4f7f\u7528\u95dc\u806f\u6a21\u578b\u53d6\u4ee3\u539f\u672c\u7684\u8a9e\u53e5\u6a21\u578b\u3002 \u4ee5\u82e5\u88ab\u6458\u8981\u6587\u4ef6\u4e0d\u5177\u6709\u67d0\u7a2e\u7279\u6b8a\u7684\u6587\u7ae0\u7d50\u69cb\uff0c\u5176\u6458\u8981\u6548\u80fd\u5c31\u6703\u6709\u9650\u3002\u76f8\u8f03\u4e4b\u4e0b\uff0cKL \u662f \u8a9e\u53e5\u66f4\u80fd\u5448\u73fe\u6587\u4ef6\u6240\u8981\u8868\u9054\u7684\u4e3b\u984c\u3002\u7136\u800c\uff0cKL+CE \u7684\u5be6\u9a57\u7d50\u679c\u4e0d\u8ad6\u5728 TD \u6216 SD \u4e2d\u7686\u8207 \u4ef6\u4e2d\u7686\u6703\u9ad8\u65bc KL\uff0c\u53ea\u6709\u5c11\u6578\u5e7e\u7bc7\u6587\u4ef6(\u7b2c 15\u300116 \u53ca 17 \u7bc7)\u6703\u4f4e\u65bc KL \u7684\u5e73\u5747\u6b63\u78ba\u7387\u5747</td></tr><tr><td>) \u76ee\u524d\u5c0d\u65bc\u975e\u76f8\u95dc\u8cc7\u8a0a\u7684\u53d6\u5f97\u8207\u4f30\u6e2c\u4ecd\u662f\u4e00\u500b\u503c\u5f97\u8a0e\u8ad6\u7684\u8b70\u984c[33]\uff0c\u5728\u672c\u8ad6\u6587\u5f8c\u7e8c\u4e4b || ( ) || ( S N CE S D KL D \uf02b \uf02d (12) \u5be6\u9a57\u4e2d\uff0c\u5c07\u521d\u6b65\u4f7f\u7528\u80cc\u666f\u55ae\u9023\u8a9e\u8a00\u6a21\u578b\u4f86\u4f5c\u70ba\u6bcf\u4e00\u7bc7\u88ab\u6458\u8981\u6587\u4ef6 D \u7684\u975e\u76f8\u95dc\u8cc7\u8a0a\u6240\u5c0d\u61c9 \u7684\u8a9e\u8a00\u6a21\u578b[7]\u3002\u53e6\u4e00\u65b9\u9762\uff0c\u660e\u78ba\u5ea6\u4e4b\u6982\u5ff5\u4e5f\u5e38\u88ab\u7528\u65bc\u8cc7\u8a0a\u6aa2\u7d22\u9818\u57df\u4e2d\uff0c\u5176\u76ee\u7684\u662f\u70ba\u4e86 \u8981\u9810\u6e2c\u6aa2\u7d22\u5b57\u4e32(Query)\u4e4b\u96e3\u6613\u5ea6\u800c\u884d\u751f\u51fa\u4f86\u7684\u6982\u5ff5[31]\uff0c\u672c\u8ad6\u6587\u662f\u9996\u6b21\u4f7f\u7528\u660e\u78ba\u5ea6\u4e4b\u6982 \u5ff5\u7528\u65bc(\u8a9e\u97f3)\u6587\u4ef6\u6458\u8981\u4efb\u52d9\u4e2d\u3002 \u53c3\u8003\u6458\u8981(Reference Summary)\u9577\u5ea6(\u55ae\u4f4d\u5143\u7e3d\u500b\u6578)\u7684\u6bd4\u4f8b\u3002\u4f30\u8a08\u7684\u55ae\u4f4d\u53ef\u4ee5\u662f N-\u9023\u8a5e \u8f03\u5177\u4e00\u822c\u6027\u7684\u6458\u8981\u65b9\u6cd5\uff0c\u56e0\u6b64\u6bd4\u8f03\u4e0d\u6703\u53d7\u9650\u65bc\u6587\u7ae0\u7684\u7d50\u69cb\u4e4b\u5f71\u97ff\uff0c\u6545\u6458\u8981\u6548\u80fd\u6bd4 LS \u4ee5 \u503c\u3002\u6211\u5011\u8a8d\u70ba\u5176\u539f\u56e0\u662f\u53ef\u80fd\u662f\u56e0\u70ba\u672c\u8ad6\u6587\u4f7f\u7528\u80cc\u666f\u55ae\u9023\u8a9e\u8a00\u6a21\u578b\u4f86\u4f5c\u70ba\u6240\u6709\u88ab\u6458\u8981\u6587 KL \u76f8\u5dee\u4e0d\u591a\uff0c\u5c0d\u6b64\u6211\u5011\u8a8d\u70ba\u53ef\u80fd\u7684\u539f\u56e0\u662f\u56e0\u70ba\u672c\u8ad6\u6587\u4f7f\u7528\u80cc\u666f\u55ae\u9023\u8a9e\u8a00\u6a21\u578b\u4f86\u4f5c\u70ba\u88ab \u56db\u3001\u5be6\u9a57\u8a9e\u6599\u53ca\u8a55\u4f30\u65b9\u6cd5 1\u3001\u5be6\u9a57\u8a9e\u6599 \u672c \uf941 \u6587 \u5be6 \u9a57 \u8a9e \u6599 \u5eab \u70ba \u516c \u8996 \u65b0 \u805e \u8a9e \u6599 (Mandarin Chinese Broadcast News Corpus, MATBN)\uff0c\u662f\u7531\u4e2d\u592e\u7814\u7a76\u9662\u8cc7\u8a0a\u79d1\u5b78\u7814\u7a76\u6240\u8017\u6642\u4e09\uf98e\u8207\u516c\u5171\u96fb\u8996\u53f0\u5408\u4f5c\uf93f\u88fd\u4e26\u6574\u7406\u7684\u4e2d \u6587\u65b0\u805e\u8a9e\u6599\uff0c\u5176\uf93f\u88fd\u5167\u5bb9\u70ba\u6bcf\u5929\u4e00\u500b\u5c0f\u6642\u7684\u516c\u8996\u665a\u9593\u65b0\u805e\u6df1\u5ea6\u5831\u5c0e\u3002\u6211\u5011\u62bd\u53d6\u5176\u4e2d\u7531 2001 \uf98e 11 \u6708\u5230 2002 \uf98e 8 \u6708\u7e3d\u5171 205 \u5247\u65b0\u805e\u5831\u5c0e\uff0c\u5340\u5206\u6210\u8a13\u7df4\u96c6(\u5171 185 \u5247\u65b0\u805e)\u4ee5\u53ca\u6e2c \u8a66\u96c6(\u5171 20 \u5247\u65b0\u805e)\u5169\u90e8\u5206\uff0c\u5176\u8a73\u7d30\u7684\u7d71\u8a08\u8cc7\u8a0a\u5982\u8868\u4e00\u6240\u793a\u3002\u5168\u90e8 205 \u5247\u8a9e\u97f3\u6587\u4ef6\u9577\u5ea6 (N-gram)\u3001\u8a5e\u5e8f\u5217(Word Sequences)\uff0c\u5982\uff1a\u6700\u9577\u76f8\u540c\u8a5e\u5e8f\u5217\u6216\u8a5e\u6210\u5c0d(Word Pairs)\u3002\u7531\u65bc \u6b64\u65b9\u6cd5\u662f\u63a1\u7528\u55ae\u4f4d\u5143\u6bd4\u5c0d\u7684\u65b9\u5f0f\uff0c\u4e0d\u6703\u7522\u751f\u8a9e\u53e5\u908a\u754c\u5b9a\u7fa9\u7684\u554f\u984c\uff0c\u4e26\u4e14\u9069\u5408\u65bc\u591a\u4efd\u4eba\u5de5 \u6458 \u8981 \u7684 \u8a55 \u4f30 \u3002 \u5176 \u8a55 \u4f30 \u7684 \u5206 \u6578 \u6709 \u4e09 \u7a2e \uff0c ROUGE-1(Unigram) \u3001 ROUGE-2(Bigram) \u548c ROUGE-L(Longest Common Subsequence)\u5206\u6578\uff0cROUGE-1 \u662f\u8a55\u4f30\u81ea\u52d5\u6458\u8981\u7684\u8a0a\u606f\u91cf\uff0c ROUGE-2 \u662f\u8a55\u4f30\u81ea\u52d5\u6458\u8981\u7684\u6d41\u66a2\u6027\uff0cROUGE-L \u662f\u6700\u9577\u5171\u540c\u5b57\uf905\uff0c\u672c\uf941\u6587\u5e0c\u671b\u89c0\u5bdf\u6458 \u8981\u7684\u6d41\u66a2\u6027\uff0c\u56e0\u6b64\uff0c\u5be6\u9a57\u6578\u64da\u4e3b\u8981\u662f\u4ee5 ROUGE-2 \u5206\u6578\u70ba\u4e3b\u3002\u672c\u8ad6\u6587\u6240\u8a2d\u5b9a\u7684\u6458\u8981\u6bd4\u4f8b \u6458\u8981\u6587\u4ef6\u7684\u975e\u76f8\u95dc\u8cc7\u8a0a\u7684\u5c0d\u61c9\u6a21\u578b\uff0c\u56e0\u6b64\u5728\u55ae\u7368\u4f7f\u7528\u7684\u60c5\u6cc1\u4e0b\u6210\u6548\u4e0d\u5f70\u3002\u5be6\u969b\u4e0a\uff0c\u6bcf\u4e00 \u7bc7\u88ab\u6458\u8981\u6587\u4ef6\u7684\u975e\u76f8\u95dc\u8cc7\u8a0a\u61c9\u8a72\u90fd\u8981\u6709\u6240\u4e0d\u540c\uff0c\u4f46\u6211\u5011\u5728\u6458\u8981\u5be6\u9a57\u4e2d\u5148\u7c21\u55ae\u5047\u8a2d\u6bcf\u4e00\u7bc7 \u88ab\u6458\u8981\u6587\u4ef6\u7684\u975e\u76f8\u95dc\u8cc7\u8a0a\u90fd\u662f\u540c\u4e00\u500b(\u5373\u80cc\u666f\u55ae\u9023\u8a9e\u8a00\u6a21\u578b)\uff0c\u5982\u4f55\u70ba\u6bcf\u4e00\u7bc7\u88ab\u6458\u8981\u6587\u4ef6 \u5efa\u7acb\u5176\u771f\u6b63\u7684\u975e\u76f8\u95dc\u8cc7\u8a0a\u6a21\u578b\u5c07\u662f\u6211\u5011\u672a\u4f86\u91cd\u8981\u7684\u7814\u7a76\u8ab2\u984c\u3002\u7e3d\u7d50\u800c\u8a00\uff0c\u660e\u78ba\u5ea6\u91cf\u503c\u9664 \u4e86\u8003\u616e\u8a9e\u53e5\u672c\u8eab\u7684\u8907\u96dc\u5ea6\u8cc7\u8a0a\u5916\uff0c\u4e5f\u8003\u91cf\u5230\u4f7f\u7528\u88ab\u6458\u8981\u6587\u4ef6\u7684\u975e\u76f8\u95dc\u8cc7\u8a0a\u4f86\u5e6b\u52a9\u9078\u53d6\u6458 \u8981\u8a9e\u53e5\uff0c\u6240\u4ee5\u7d50\u5408\u4f7f\u7528\u660e\u78ba\u5ea6\u91cf\u503c\u4e4b\u65b9\u6cd5\u4f86\u8f14\u52a9\u6311\u9078\u91cd\u8981\u4e14\u5177\u4ee3\u8868\u6027\u7684\u8a9e\u53e5\u5c0d\u6458\u8981\u6548\u80fd \u4ef6\u7684\u975e\u76f8\u95dc\u8cc7\u8a0a\u7684\u5c0d\u61c9\u6a21\u578b\uff0c\u800c\u9019\u5e7e\u7bc7\u6587\u4ef6\u7684\u975e\u76f8\u95dc\u8cc7\u8a0a\u53ef\u80fd\u8207\u80cc\u666f\u55ae\u9023\u8a9e\u8a00\u6a21\u578b\u8f03 \u4e0d\u76f8\u8fd1\uff0c\u56e0\u6b64\u9020\u6210\u5176\u6458\u8981\u7d50\u679c\u4e0d\u5982\u9810\u671f\u3002 4\u3001\u8003\u91cf\u95dc\u806f\u6a21\u578b\u4e4b\u5be6\u9a57 \u4f7f\u7528\u95dc\u806f\u6a21\u578b\u65bc\u8a9e\u53e5\u6a21\u578b\u4e4b\u5efa\u7acb\u6642\uff0c\u9700\u8981\u505a\u4e00\u6b21\u7684\u8cc7\u8a0a\u6aa2\u7d22\u4f86\u70ba\u6bcf\u500b\u8a9e\u53e5\u627e\u51fa\u865b\u64ec\u95dc\u806f \u6587\u4ef6\uff0c\u672c\u8ad6\u6587\u63a1\u7528\u6587\u4ef6\u76f8\u4f3c\u5ea6\u91cf\u503c ) || ( D S P [36]\uff0c\u7531\u540c\u6642\u671f\u7684\u65b0\u805e\u6587\u5b57\u6587\u4ef6(\u5171 101,268 \u53ca \u901a\u5e38\u8a9e\u97f3\u6587\u4ef6\u4e3b\u8981\u6703\u6709\u8a9e\u97f3\u8fa8\u8b58\u932f\u8aa4\u548c\u8a9e\u53e5\u908a\u754c\u5075\u6e2c\u932f\u8aa4\u7684\u554f\u984c\uff0c\u4f46\u6211\u5011\u6709\u5148\u7d93\u4eba \u70ba 10%\uff0c\u5176\u5b9a\u7fa9\u70ba\u6458\u8981\u6240\u542b\u8a5e\u5f59\u6578\u5360\u6574\u7bc7\u6587\u4ef6\u8a5e\u5f59\u6578\u7684\u6bd4\u4f8b\uff0c\u4e5f\u5c31\u662f\u4ee5\u8a5e\u5f59\u505a\u70ba\u5224\u65b7\u6458 \u7684\u63d0\u5347\u662f\u975e\u5e38\u6709\u52a9\u76ca\u7684\u3002 \u7bc7)\u4e2d\u70ba\u6bcf\u4e00\u8a9e\u53e5\u9078\u53d6\u51fa 15 \u7bc7\u865b\u64ec\u95dc\u806f\u6587\u4ef6\u4f86\u9032\u884c\u95dc\u806f\u6a21\u578b\u4e4b\u4f30\u6e2c\u8207\u76f8\u95dc\u5be6\u9a57[6]\u3002\u7531\u65bc \u5de5\u5207\u97f3\uff0c\u56e0\u6b64\u6452\u9664\u4e86\u8a9e\u53e5\u908a\u754c\u5075\u6e2c\u932f\u8aa4\u7684\u554f\u984c\uff0c\u85c9\u7531\u6bd4\u8f03 TD \u8207 SD \u4e4b\u5be6\u9a57\u7d50\u679c\uff0c\u6211\u5011 \u8981\u6bd4\u4f8b\u7684\u55ae\u5143\u3002\u5728\u6311\u9078\u6458\u8981\u8a9e\u53e5\u904e\u7a0b\u4e2d\uff0c\u82e5\u9078\u5230\u67d0\u8a9e\u53e5\u4e2d\u7684\u67d0\u500b\u8a5e\u5f59\u6642\u5c31\u5df2\u7d93\u525b\u597d\u9054\u5230 \u53ef\u4ee5\u89c0\u5bdf\u8a9e\u97f3\u8fa8\u8b58\u932f\u8aa4\u7387\u5c0d\u6458\u8981\u7d50\u679c\u7684\u5f71\u97ff\u6027\u3002\u6bd4\u8f03\u5404\u5f0f\u65b9\u6cd5\uff0cSD \u6bd4 TD \u4e0b\u964d\u4e86 \u6587\u4ef6\u4e2d\u7684\u8a9e\u53e5\u901a\u5e38\u76f8\u5c0d\u7c21\u77ed\uff0c\u56e0\u6b64\u7576\u4f7f\u7528\u6700\u5927\u5316\u76f8\u4f3c\u5ea6\u4f30\u6e2c\u5efa\u7acb\u8a9e\u53e5\u6a21\u578b\u6642\uff0c\u5bb9\u6613\u906d\u9047 3\u3001\u8a9e\u53e5\u660e\u78ba\u5ea6\u4e4b\u5206\u6790 \u6458\u8981\u6bd4\u4f8b\uff0c\u70ba\u4e86\u4fdd\u6301\u8a9e\u53e5\u8a9e\u610f\u5b8c\u6574\u6027\uff0c\u6b64\u8a9e\u53e5\u5269\u4e0b\u7684\u8a5e\u5f59\u4e5f\u6703\u88ab\u6311\u9078\u6210\u70ba\u6458\u8981\u3002 1.9%~8.8%\u7684 ROUGE-2 \u6458\u8981\u6548\u80fd\uff0c\u7531\u6b64\u53ef\u77e5\u8a9e\u97f3\u8fa8\u8b58\u932f\u8aa4\u7387\u5c0d\u6458\u8981\u6548\u80fd\u662f\u6709\u986f\u8457\u7684\u5f71 \u8cc7\u6599\u7a00\u758f\u7684\u554f\u984c\uff0c\u4e0d\u5bb9\u6613\u7372\u5f97\u7cbe\u6e96\u7684\u6a21\u578b\uff0c\u6545\u6211\u5011\u671f\u671b\u8003\u616e\u984d\u5916\u7684\u95dc\u806f\u8cc7\u8a0a\u65bc\u8a9e\u97f3\u6587\u4ef6 4\u3001\u4f7f\u7528\u95dc\u806f\u6a21\u578b \u7d04\u70ba 7.5 \u5c0f\u6642\uff0c\u6211\u5011\u5148\u505a\u4eba\u5de5\u5207\u97f3\uff0c\u5207\u51fa\u771f\u6b63\u542b\u6709\u8b1b\u8a71\u5167\u5bb9\u7684\u97f3\u8a0a\u6bb5\u843d\uff0c\u518d\u7d93\u7531\u8a9e\u97f3\u8fa8 \u4e94\u3001\u5be6\u9a57\u7d50\u679c \u97ff\u6027\u3002\u70ba\u4e86\u6e1b\u7de9\u8a9e\u97f3\u8fa8\u8a8d\u932f\u8aa4\u7684\u554f\u984c\uff0c\u5728\u672a\u4f86\u6211\u5011\u5c07\u5617\u8a66\u4f7f\u7528\u97f3\u7bc0(Syllable)\u70ba\u55ae\u4f4d\u4f86\u5efa \u70ba\u4e86\u66f4\u9032\u4e00\u6b65\u3001\u56b4\u683c\u5730\u5206\u6790 KL+Clarity \u7684\u6458\u8981\u80fd\u529b\uff0c\u672c\u5c0f\u7bc0\u4ee5\u5e73\u5747\u6b63\u78ba\u7387\u5747\u503c(Mean \u6458\u8981\uff0c\u4ea6\u5373\u85c9\u7531\u865b\u64ec\u95dc\u806f\u6587\u4ef6\u4f86\u91cd\u65b0\u4f30\u6e2c\u4e26\u5efa\u7acb\u8a9e\u53e5\u7684\u8a9e\u8a00\u6a21\u578b\uff0c\u80fd\u7372\u5f97\u9032\u4e00\u6b65\u5730\u6458\u8981</td></tr><tr><td>\u9664\u4e86\u7d50\u5408\u8a9e\u53e5\u660e\u78ba\u5ea6\u65bc\u8a9e\u97f3\u6587\u4ef6\u6458\u8981\u4e4b\u7814\u7a76\u5916\uff0c\u672c\u8ad6\u6587\u4ea6\u91dd\u5c0d\u8a9e\u53e5\u6a21\u578b\u8abf\u9069\u9032\u884c\u521d\u6b65\u7814 \u8b58\u5668\u81ea\u52d5\u7522\u751f\u51fa\u7684\u8a9e\u97f3\u8fa8\u8b58\u7d50\u679c\u7a31\u4e4b\u70ba\u8a9e\u97f3\u6587\u4ef6(Spoken Document, SD)\uff0c\u56e0\u6b64\u8a9e\u97f3\u6587\u4ef6 \u7acb\u8a9e\u53e5\u4ee5\u53ca\u6587\u4ef6\u6a21\u578b\uff1b\u6216\u5229\u7528\u8a5e\u5716(Word Graph)\u3001\u6df7\u6dc6\u7db2\u8def(Confusion Network)\u4f86\u542b\u62ec Average Precision, MAP)\u4f86\u6bd4\u8f03 KL\u3001LEAD \u548c KL+Clarity \u7684\u6458\u8981\u80fd\u529b\u3002\u76f8\u8f03\u65bc ROUGE \u6210\u6548\u3002\u91cd\u65b0\u4f30\u6e2c\u5f8c\u7684\u95dc\u806f\u6a21\u578b\u5247\u53ef\u8207\u539f\u672c\u7684\u8a9e\u53e5\u6a21\u578b\u76f8\u7d50\u5408\u6216\u53d6\u4ee3\u4e4b\uff0c\u76f8\u7d50\u5408\u7684\u53c3\u6578\u8abf</td></tr><tr><td>\u7a76\u3002\u901a\u5e38\uff0c\u6587\u4ef6\u4e2d\u7684\u8a9e\u53e5\u50c5\u7531\u5c11\u8a31\u7684\u8a5e\u5f59\u6240\u7d44\u6210\uff0c\u7576\u8a9e\u53e5\u6a21\u578b\u4f7f\u7528\u6700\u5927\u5316\u76f8\u4f3c\u5ea6\u4f30\u6e2c\u6642\uff0c \u4e2d\u53ea\u5305\u542b\u6709\u8a9e\u97f3\u8fa8\u8b58\u932f\u8aa4\u4e4b\u96dc\u8a0a\uff1b\u53e6\u4e00\u65b9\u9762\uff0c\u6211\u5011\u5c07\u6b64 205 \u5247\u8a9e\u97f3\u6587\u4ef6\u85c9\u7531\u4eba\u5de5\u807d\u5beb\u7684 \u672c\u8ad6\u6587\u4e3b\u8981\u8457\u91cd\u5728\u975e\u76e3\u7763\u5f0f\u6458\u8981\u65b9\u6cd5\u4e4b\u767c\u5c55\u8207\u6539\u9032\uff0c\u56e0\u6b64\u6240\u6bd4\u8f03\u7684\u5c0d\u8c61\u4ee5\u975e\u76e3\u7763\u5f0f\u6458\u8981 \u66f4\u591a\u7684\u53ef\u80fd\u6b63\u78ba\u5019\u9078\u8a5e\u5f59\u4ee5\u88e8\u76ca\u6a21\u578b\u4f30\u6e2c\uff1b\u66f4\u53ef\u5229\u7528\u97fb\u5f8b\u8cc7\u8a0a(Prosodic Information)\u7b49 \u662f\u8f03\u5bec\u9b06\u5730\u8a08\u7b97\u5169\u8a9e\u53e5\u9593\u8a5e\u5f59\u91cd\u758a\u6578\u76ee\u6bd4\u4f8b\u505a\u70ba\u8a55\u91cf\u6a19\u6e96\uff0c\u5e73\u5747\u6b63\u78ba\u7387\u5747\u503c\u662f\u56b4\u683c\u7684 \u6574\u5728\u672c\u5be6\u9a57\u4e2d\u662f\u63a1\u7528\u7d93\u9a57\u8a2d\u5b9a(Empirical Setting)\u3002\u5be6\u9a57\u7d50\u679c\u5982\u8868\u56db\u6240\u793a\uff0c\u5728 TD \u8207 SD</td></tr><tr><td>\u5bb9\u6613\u906d\u9047\u8cc7\u6599\u7a00\u758f\u7684\u554f\u984c\uff0c\u85c9\u7531\u80cc\u666f\u8a9e\u8a00\u6a21\u578b\u9032\u884c\u8a9e\u53e5\u6a21\u578b\u4e4b\u8abf\u9069\u70ba\u6700\u5e38\u898b\u7684\u65b9\u6cd5\u4e4b\u4e00 (\u53c3\u7167\u5f0f(4))\u3002 \u65b9\u6cd5\u70ba\u4e3b\uff1b\u9664\u6b64\u4e4b\u5916\uff0c\u672c\u8ad6\u6587\u4ea6\u5617\u8a66\u8207\u73fe\u4eca\u6700\u88ab\u5ee3\u70ba\u4f7f\u7528\u7684\u76e3\u7763\u5f0f\u6a5f\u5668\u5b78\u7fd2\u65b9\u6cd5\u505a\u6bd4 \u8072\u5b78\u7dda\u7d22\u4f86\u8f14\u52a9\u6e1b\u7de9\u8a9e\u97f3\u8fa8\u8a8d\u932f\u8aa4\u5c0d\u6458\u8981\u6548\u80fd\u7684\u5f71\u97ff\u3002 \u4ee5\u6bcf\u4e00\u7bc7\u88ab\u6458\u8981\u6587\u4ef6\u6240\u9078\u51fa\u7684\u6458\u8981\u8a9e\u53e5\uff0c\u662f\u5426\u8207\u4eba\u5de5\u53c3\u8003\u6458\u8981\u8a9e\u53e5\u76f8\u540c\u4f5c\u70ba\u8a55\u5206\u6a19 \u4e4b\u6458\u8981\u6210\u6548\u4e0a\uff0c\u4f7f\u7528\u95dc\u806f\u6a21\u578b(KL+RM)\u76f8\u8f03\u65bc KL \u5728 ROUGE-2 \u7684\u7d50\u679c\u4e0a\u80fd\u6709 3.7%\u8207 \u65b9\u5f0f\uff0c\u7522\u751f\u51fa\u6c92\u6709\u8fa8\u8b58\u932f\u8aa4\u7684\u6b63\u78ba\u6587\u5b57\u8a9e\u6599\uff0c\u6211\u5011\u7a31\u4e4b\u70ba\u6587\u5b57\u6587\u4ef6(Text Document, TD)\uff0c\u6bcf\u5247\u6587\u5b57\u6587\u4ef6\u518d\u7d93\u7531\u4e09\u4f4d\u5c08\u5bb6\u6a19\u8a18\u6458\u8981\u8a9e\u53e5\uff0c\u6211\u5011\u5c07\u6b64\u6a19\u8a18\u7684\u4eba\u5de5\u6458\u8981\u505a\u70ba\u8a9e\u97f3 \u6e96\u3002\u672c\u5be6\u9a57\u4e2d\uff0c\u6211\u5011\u4f7f\u7528\u5404\u7a2e\u65b9\u6cd5\u5206\u5225\u8a08\u7b97\u6587\u4ef6\u4e2d\u6bcf\u4e00\u8a9e\u53e5\u5f8c\u4f9d\u64da\u5404\u81ea\u7684\u5206\u6578\u6392\u5e8f\uff0c 3.5%\u7684\u6539\u9032\u3002 \u8f03\uff0c\u5373\u652f\u6301\u5411\u91cf\u6a5f(SVM)[37]\u3002 2\u3001 \u4f7f\u7528\u660e\u78ba\u5ea6\u91cf\u503c\u4e4b\u5be6\u9a57 \u9078\u53d6\u6392\u540d\u524d 3 \u9ad8\u7684\u8a9e\u53e5\u4f86\u8a08\u7b97\u5e73\u5747\u6b63\u78ba\u7387\u5747\u503c(MAP)\uff1b\u53e6\u5916\uff0c\u7531\u65bc SD \u4e2d\u6703\u6709\u8a9e\u97f3\u8fa8 \u63a5\u8457\uff0c\u6211\u5011\u66f4\u9032\u4e00\u6b65\u5730\u7d50\u5408\u672c\u8ad6\u6587\u6240\u63a2\u8a0e\u4e4b\u8a9e\u53e5\u660e\u78ba\u5ea6\u91cf\u503c\u4ee5\u53ca\u95dc\u806f\u6a21\u578b\uff0c\u5be6\u9a57\u7d50 \u96d6\u7136\u6587\u4ef6\u4e2d\u7684\u8a9e\u53e5\u901a\u5e38\u662f\u7c21\u77ed\u7684\uff0c\u4f46\u6211\u5011\u8a8d\u70ba\u6bcf\u4e00\u8a9e\u53e5 S \u7686\u662f\u88ab\u7528\u4f86\u63cf\u8ff0\u4e00\u500b\u6982 \u5ff5\u3001\u60f3\u6cd5\u6216\u4e3b\u984c\uff0c\u6211\u5011\u7a31\u4e4b\u70ba\u8a9e\u53e5\u7684\u95dc\u806f\u985e\u5225(Relevance Class) S R \u3002\u5728\u672c\u8ad6\u6587\u4e2d\uff0c\u6211\u5011 \u6587\u4ef6\u8207\u6587\u5b57\u6587\u4ef6\u7684\u6b63\u78ba\u6458\u8981\u7b54\u6848\u3002\u85c9\u7531\u6bd4\u8f03\u8a9e\u97f3\u6587\u4ef6\u548c\u6587\u5b57\u6587\u4ef6\u7684\u6458\u8981\u6548\u80fd\uff0c\u6211\u5011\u53ef\u4ee5 1\u3001 \u57fa\u790e\u5be6\u9a57 \u8b58\u932f\u8aa4\u7b49\u96dc\u8a0a\u7684\u5e72\u64fe\uff0c\u6545\u6211\u5011\u9078\u64c7 TD \u505a\u70ba\u5206\u6790\u4e4b\u8a9e\u6599\u3002 \u679c\u5982\u8868\u56db\u6240\u793a\u3002\u9996\u5148\uff0c\u7d50\u5408\u95dc\u806f\u6a21\u578b\u8207\u660e\u78ba\u5ea6\u91cf\u503c(KL+Clarity+RM) \u76f8\u8f03\u65bc KL+RM \u5728 \u63a5\u8457\uff0c\u6211\u5011\u63a2\u8a0e\u8a9e\u53e5\u660e\u78ba\u5ea6\u91cf\u503c\u65bc\u8a9e\u97f3\u6587\u4ef6\u6458\u8981\u4e4b\u6210\u6548\u3002\u5be6\u9a57\u7d50\u679c\u5982\u8868\u4e09\u6240\u793a\uff0c\u4f7f \u89c0\u5bdf\u8a9e\u97f3\u8fa8\u8b58\u932f\u8aa4\u5c0d\u65bc\u5404\u7a2e\u6458\u8981\u65b9\u6cd5\u4e4b\u5f71\u97ff\u3002\u672c\u7814\u7a76\u7684\u80cc\u666f\u8a9e\u8a00\u6a21\u578b\u8a13\u7df4\u8a9e\u6599\u53d6\u6750\u81ea \u9996\u5148\uff0c\u6211\u5011\u6bd4\u8f03\u5eab\u723e\u8c9d\u514b-\u840a\u4f2f\u52d2\u96e2\u6563\u5ea6(KL)\u8207\u6578\u500b\u975e\u76e3\u7763\u5f0f\u6458\u8981\u65b9\u6cd5\u4e4b\u6458\u8981\u6210\u6548\uff0c\u5305 \u7528\u8a9e\u53e5\u660e\u78ba\u5ea6\u91cf\u503c(KL+Clarity, \u53c3\u7167\u5f0f(10))\u4f86\u8f14\u52a9\u6311\u9078\u6458\u8981\u8a9e\u53e5\u78ba\u5be6\u8f03\u55ae\u7d14\u4f7f\u7528 KL \u9996\u5148\u6bd4\u8f03 KL \u8207 LEAD \u7684\u5e73\u5747\u6b63\u78ba\u7387\u5747\u503c\uff0c\u5982\u5716\u4e00\u6240\u793a\uff0cKL \u5728\u5927\u90e8\u5206\u7684\u6587\u4ef6\u4e2d TD \u53ca SD \u7684 ROUGE-2 \u7d50\u679c\u5206\u5225\u6709 3.8%\u8207 2.1%\u7684\u9032\u6b65\u7387\u3002\u7e3d\u5408\u800c\u8a00\uff0c\u7d50\u5408\u4e86\u5eab\u723e\u8c9d\u514b</td></tr></table>", |
| "type_str": "table", |
| "num": null |
| }, |
| "TABREF3": { |
| "text": "SD \u7684\u5be6\u9a57\u4e0a(\u5176 ROUGE-2 \u5206\u5225\u70ba 37.8 \u53ca 35.9)\u90fd\u662f\u8868\u73fe\u6700\u597d\u7684\u65b9\u6cd5\uff0c\u9019\u662f", |
| "html": null, |
| "content": "<table><tr><td/><td/><td colspan=\"2\">\u8868\u56db\u3001\u8003\u91cf\u95dc\u806f\u6a21\u578b\u4e4b\u5be6\u9a57\u7d50\u679c</td><td/></tr><tr><td/><td/><td/><td>F-score (%)</td><td/></tr><tr><td/><td/><td>ROUGE-1</td><td>ROUGE-2</td><td>ROUGE-L</td></tr><tr><td/><td>KL+RM</td><td>45.3</td><td>33.5</td><td>40.3</td></tr><tr><td/><td>KL+CE+RM</td><td>45.9</td><td>34.5</td><td>41.2</td></tr><tr><td>TD</td><td/><td/><td/><td/></tr><tr><td/><td>KL+SE+RM</td><td>47.7</td><td>36.4</td><td>42.6</td></tr><tr><td/><td>KL+Clarity+RM</td><td>47.7</td><td>37.3</td><td>42.6</td></tr><tr><td/><td>KL+RM</td><td>39.3</td><td>24.5</td><td>34.1</td></tr><tr><td>SD</td><td>KL+CE+RM</td><td>39.1</td><td>26.2</td><td>34.7</td></tr><tr><td/><td>KL+SE+RM</td><td>40.1</td><td>26.4</td><td>35.2</td></tr><tr><td/><td>KL+Clarity+RM</td><td>40.0</td><td>26.6</td><td>35.4</td></tr><tr><td colspan=\"5\">\u97fb\u5f8b\u7279\u5fb5\u662f\u5f88\u6b63\u78ba\u7684\u8cc7\u8a0a\uff0c\u53c8\u56e0\u70ba\u97fb\u5f8b\u7279\u5fb5\u5c0d\u8a9e\u97f3\u6587\u4ef6\u6458\u8981\u662f\u5177\u6709\u76f8\u7576\u7a0b\u5ea6\u7684\u5e6b\u52a9\uff0c\u6240</td></tr><tr><td colspan=\"3\">\u4ee5 SVM \u5728 SD \u7684\u5be6\u9a57\u4e2d\u624d\u80fd\u8868\u73fe\u5f97\u5982\u6b64\u5091\u51fa\u3002</td><td/><td/></tr><tr><td colspan=\"2\">\u516d\u3001\u7d50\u8ad6\u8207\u672a\u4f86\u65b9\u5411</td><td/><td/><td/></tr><tr><td colspan=\"5\">10.7%\u7684 ROUGE-2 \u6458\u8981\u6548\u80fd\uff0c\u5728\u672a\u4f86 \u672c\u8ad6\u6587\u4e3b\u8981\u6709\u5169\u500b\u8ca2\u737b\uff0c\u7b2c\u4e00\u70ba\u9996\u6b21\u63a2\u7a76\u660e\u78ba\u5ea6\u65bc\u8a9e\u97f3\u6587\u4ef6\u6458\u8981\u4e0a\u4e4b\u6548\u7528\uff0c\u7576\u8207\u5eab\u723e\u8c9d \u7814\u7a76\u4e2d\uff0c\u6211\u5011\u8a8d\u70ba\u53ef\u4ee5\u4ee5\u6b21\u8a5e\u7d22\u5f15(Subword Indexing)\u7684\u65b9\u5f0f\u4f86\u5efa\u7acb\u95dc\u806f\u6a21\u578b\u4ee5\u6e1b\u7de9\u8a9e\u97f3 \u514b-\u840a\u4f2f\u52d2\u96e2\u6563\u5ea6\u76f8\u7d50\u5408\u5f8c\uff0c\u904b\u7528\u65bc\u8a9e\u97f3\u6587\u4ef6\u6458\u8981\u4e0a\u5177\u6709\u52a0\u6210\u4f5c\u7528\u4e4b\u6548\u679c\u3002\u6211\u5011\u4ea6\u540c\u6642 \u8fa8\u8b58\u932f\u8aa4\u4e4b\u5f71\u97ff\u3002 \u6aa2\u8996\u660e\u78ba\u5ea6\u7684\u5167\u90e8\u7d44\u6210\uff0c\u5c07\u4e4b\u5340\u5206\u6210\u5167\u5728\u8cc7\u8a0a(\u8a9e\u53e5\u672c\u8eab\u8cc7\u8a0a\u8907\u96dc\u5ea6)\u53ca\u5916\u5728\u8cc7\u8a0a(\u8a9e\u53e5\u8207</td></tr><tr><td colspan=\"5\">5\u3001\u8207\u76e3\u7763\u5f0f\u6a21\u578b\u4e4b\u6bd4\u8f03 \u88ab\u6458\u8981\u6587\u4ef6\u975e\u76f8\u95dc\u8cc7\u8a0a\u4e4b\u4ea4\u4e92\u4e82\u5ea6)\u5169\u500b\u9762\u5411\uff0c\u4f86\u8a6e\u91cb\u660e\u78ba\u5ea6\u5982\u4f55\u8f14\u52a9\u6311\u9078\u6587\u4ef6\u4e2d\u91cd\u8981</td></tr><tr><td colspan=\"5\">\u4e14\u5177\u4ee3\u8868\u6027\u7684\u6458\u8981\u8a9e\u53e5\u3002\u7b2c\u4e8c\uff0c\u57fa\u65bc\u6240\u8b02\u95dc\u806f\u6027(Relevance)\u7684\u6982\u5ff5\uff0c\u672c\u8ad6\u6587\u5617\u8a66\u4f7f\u7528\u865b</td></tr><tr><td colspan=\"5\">\u9664\u4e86\u5404\u5f0f\u975e\u76e3\u7763\u5f0f\u6458\u8981\u65b9\u6cd5\u5916\uff0c\u672c\u8ad6\u6587\u4ea6\u5617\u8a66\u6bd4\u8f03\u652f\u6301\u5411\u91cf\u6a5f(SVM)\u65bc\u6587\u4ef6\u6458\u8981\u4e4b\u6210 \u64ec\u95dc\u806f\u6587\u4ef6\u4f86\u91cd\u65b0\u4f30\u6e2c\u4e26\u5efa\u7acb\u8a9e\u53e5\u7684\u8a9e\u8a00\u6a21\u578b\uff0c\u4f7f\u5176\u5f97\u4ee5\u66f4\u7cbe\u6e96\u5730\u4ee3\u8868\u8a9e\u53e5\u7684\u8a9e\u610f\u5167</td></tr><tr><td colspan=\"5\">\u6548\u3002\u652f\u6301\u5411\u91cf\u6a5f\u662f\u73fe\u4eca\u5e38\u898b\u7684\u76e3\u7763\u5f0f\u6a5f\u5668\u5b78\u7fd2\u65b9\u6cd5\u4e4b\u4e00\uff0c\u8fd1\u5e74\u4f86\u5df2\u6709\u5b78\u8005\u5c07\u5176\u904b\u7528\u81f3\u6587 \u5bb9\uff0c\u4ee5\u589e\u9032\u81ea\u52d5\u6458\u8981\u7684\u6548\u80fd\u3002\u76f8\u8f03\u65bc\u5176\u5b83\u73fe\u6709\u7684\u975e\u76e3\u7763\u5f0f\u6458\u8981\u65b9\u6cd5\uff0c\u672c\uf941\u6587\u6240\u63d0\u51fa\u4e4b\u6458</td></tr><tr><td colspan=\"5\">\u4ef6\u6458\u8981\u9818\u57df\u4e4b\u4e2d[37]\u3002\u672c\u8ad6\u6587\u4f7f\u7528\u8a13\u7df4\u96c6\u7684 185 \u7bc7\u6587\u4ef6\u9032\u884c\u652f\u6301\u5411\u91cf\u6a5f\u6a21\u578b\u7684\u8a13\u7df4\u8a9e \u8981\u65b9\u6cd5\u6709\u660e\u986f\u7684\u6548\u80fd\u6539\u5584\uff0c\u751a\u81f3\u53ef\u4ee5\u903c\u8fd1\u5e38\u898b\u7684\u76e3\u7763\u5f0f\u6458\u8981\u65b9\u6cd5\u3002</td></tr><tr><td colspan=\"5\">\u6599\uff0c\u6211\u5011\u70ba\u6587\u4ef6\u4e2d\u7684\u6bcf\u4e00\u8a9e\u53e5\u62bd\u53d6 19 \u7dad\u7279\u5fb5[17]\uff0c\u5305\u62ec\u6709\u97fb\u5f8b\u7279\u5fb5(Prosodic Features)\u3001 \u672a\u4f86\uff0c\u6211\u5011\u7684\u7814\u7a76\u5c07\u6709\u4e09\u500b\u4e3b\u8981\u7684\u65b9\u5411\uff1a\u9996\u5148\uff0c\u672c\u8ad6\u6587\u6240\u63d0\u51fa\u4e4b\u8a9e\u53e5\u660e\u78ba\u5ea6\u662f\u7531\u5169</td></tr><tr><td colspan=\"5\">\u8a9e\u5f59\u7279\u5fb5(Lexical Features)\u3001\u7d50\u69cb\u7279\u5fb5(Structural Features)\u4ee5\u53ca\u57fa\u672c\u7684\u6a21\u578b\u7279\u5fb5(Model \u7a2e\u8cc7\u8a0a\u7d44\u5408\u800c\u6210\uff0c\u800c\u9019\u5169\u7a2e\u8cc7\u8a0a\u5728\u6458\u8981\u8a9e\u53e5\u6311\u9078\u6642\u626e\u6f14\u540c\u7b49\u91cd\u8981\u7684\u89d2\u8272\uff0c\u6211\u5011\u5c07\u9032\u4e00\u6b65</td></tr><tr><td colspan=\"5\">Features)\u7b49\u8cc7\u8a0a\uff0c\u5176\u6838\u5fc3\u51fd\u6578\u8a2d\u5b9a\u70ba\u534a\u5f91\u5f0f\u51fd\u6578(Radial Basis Function)\uff0c\u5176\u4e2d SVM \u7684\u53c3 \u7684\u7814\u7a76\u662f\u5426\u53ef\u4ee5\u91dd\u5c0d\u4e0d\u540c\u7684\u6587\u4ef6\u6216\u4e0d\u540c\u7684\u8a9e\u53e5\u7d66\u4e88\u9069\u7576\u7684\u6b0a\u91cd\u8abf\u6574\uff0c\u4ee5\u671f\u7372\u5f97\u66f4\u597d\u7684\u6458</td></tr><tr><td colspan=\"5\">\u6578\u8a2d\u5b9a\u90fd\u662f\u4f7f\u7528\u9810\u8a2d\u503c\u3002 \u8981\u6210\u6548\uff1b\u7b2c\u4e8c\uff0c\u76ee\u524d\u95dc\u806f\u6a21\u578b\u50c5\u904b\u7528\u65bc\u91cd\u5efa\u8a9e\u53e5\u7684\u8a9e\u8a00\u6a21\u578b\uff0c\u6211\u5011\u5c07\u5617\u8a66\u4f7f\u7528\u88ab\u6458\u8981\u6587</td></tr><tr><td colspan=\"5\">\u5be6\u9a57\u7d50\u679c\u5982\u5716\u4e09\u6240\u793a\uff0c\u4e00\u5982\u9810\u671f\u5730\uff0cSVM \u8207\u5176\u4ed6\u5404\u5f0f\u975e\u76e3\u7763\u5f0f\u6a21\u578b\u76f8\u6bd4\u8f03\uff0c\u4e0d\u8ad6 \u4ef6\u7684\u95dc\u806f\u8cc7\u8a0a\u4f86\u91cd\u65b0\u4f30\u6e2c\u4e26\u5efa\u7acb\u6587\u4ef6\u7684\u8a9e\u8a00\u6a21\u578b\uff1b\u6700\u5f8c\uff0c\u6211\u5011\u5e0c\u671b\u5c07\u660e\u78ba\u5ea6\u6b64\u4e00\u6458\u8981\u7279</td></tr><tr><td colspan=\"5\">\u5fb5\u8cc7\u8a0a\u7d50\u5408\u65bc\u76e3\u7763\u5f0f\u6a5f\u5668\u5b78\u7fd2\u65b9\u6cd5(\u5982 CRF \u6216\u6df1\u5ea6\u985e\u795e\u7d93\u7db2\u7d61(Deep Neural Network \u662f\u5728 TD \u6216 \u7531\u65bc\u76e3\u7763\u5f0f\u6a5f\u5668\u5b78\u7fd2\u85c9\u7531\u4f7f\u7528\u4eba\u5de5\u6a19\u6ce8\u7684\u6458\u8981\u53e5\u5b50\u9032\u884c\u6a21\u578b\u4e4b\u8a13\u7df4\uff0c\u5176\u4f7f\u7528\u7684\u8cc7\u8a0a\u8f03\u975e Learning, DNN)\u7b49)\u4e2d\uff0c\u671f\u671b\u8a13\u7df4\u5f8c\u7684\u6a21\u578b\u80fd\u5920\u5728\u6587\u5b57\u6587\u4ef6\u6458\u8981\u6216\u8a9e\u97f3\u6587\u4ef6\u6458\u8981\u4e0a\u7372\u5f97\u66f4</td></tr><tr><td colspan=\"5\">\u76e3\u7763\u5f0f\u6a5f\u5668\u5b78\u7fd2\u65b9\u6cd5\u591a\u4e14\u6b63\u78ba\uff0c\u56e0\u6b64\u5176\u6458\u8981\u7684\u6548\u679c\u4e5f\u8f03\u975e\u76e3\u7763\u5f0f\u6a5f\u5668\u5b78\u7fd2\u4f86\u7684\u597d\u3002\u503c\u5f97 \u597d\u7684\u8868\u73fe\u3002</td></tr><tr><td colspan=\"5\">\u4e00\u63d0\u7684\u662f\uff0c\u5c07\u660e\u78ba\u5ea6\u8207\u95dc\u806f\u6a21\u578b(KL+Clarity+RM)\u4e92\u76f8\u7d50\u5408\u4e4b\u5f8c\uff0c\u6458\u8981\u4e4b\u6210\u6548\u5728 TD \u4e0a</td></tr><tr><td colspan=\"5\">\u53ef\u903c\u8fd1\u65bc\u76e3\u7763\u5f0f\u6a5f\u5668\u5b78\u7fd2\u65b9\u6cd5\u7684 SVM\uff0c\u6b64\u4e00\u5be6\u9a57\u7d50\u679c\u4ee4\u4eba\u611f\u5230\u9a5a\u8a1d\uff0c\u56e0\u70ba\u672c\u8ad6\u6587\u6240\u63a2</td></tr><tr><td colspan=\"5\">\u8a0e\u4e4b\u5404\u5f0f\u6458\u8981\u65b9\u6cd5\u50c5\u8003\u616e\u4e86\u6587\u4ef6\u8207\u8a9e\u53e5\u4e2d\u7684\u55ae\u4e00\u7a2e\u7279\u5fb5\u503c\uff0c\u5373\u85c9\u7531\u8a5e\u5f59\u5206\u4f48\u8cc7\u8a0a\u4f86\u6311\u9078</td></tr><tr><td colspan=\"5\">\u8a9e\u53e5\uff0c\u800c\u652f\u6301\u5411\u91cf\u6a5f\u4e0d\u50c5\u4f7f\u7528\u4e86 19 \u7a2e\u7279\u5fb5\u503c\uff0c\u66f4\u9700\u8981\u4f7f\u7528\u4eba\u5de5\u6a19\u8a3b\u7684\u6b63\u78ba\u7b54\u6848\u9032\u884c\u6a21</td></tr><tr><td colspan=\"5\">\u578b\u7684\u8a13\u7df4\u3002\u6211\u5011\u8a8d\u70ba\uff0c\u6b64\u7d50\u679c\u4e4b\u539f\u56e0\u53ef\u80fd\u662f\u7531\u65bc\u652f\u6301\u5411\u91cf\u6a5f\u4e4b\u6458\u8981\u6280\u8853\u662f\u5c07\u6458\u8981\u4efb\u52d9\u8996</td></tr><tr><td colspan=\"5\">\u70ba\u4e00\u500b\u4e8c\u5143\u5206\u985e\u554f\u984c\uff0c\u5728\u81ea\u52d5\u6458\u8981\u7684\u7814\u7a76\u4e2d\u6216\u8a31\u53ef\u4ee5\u9054\u5230\u67d0\u4e00\u7a0b\u5ea6\u7684\u6458\u8981\u6210\u6548\uff0c\u4f46\u672a\u5fc5</td></tr><tr><td colspan=\"5\">\u662f\u6700\u597d\u7684\u89e3\u6c7a\u65b9\u6cd5\u3002\u53e6\u4e00\u65b9\u9762\uff0c\u5728 SD \u7684\u5be6\u9a57\u4e2d\uff0cSVM \u76f8\u8f03\u65bc\u5176\u4ed6\u65b9\u6cd5\u80fd\u64c1\u6709\u7279\u5225\u7a81</td></tr><tr><td colspan=\"5\">\u51fa\u7684\u7d50\u679c\uff0c\u5176\u539f\u56e0\u53ef\u80fd\u662f\u56e0\u70ba\u6211\u5011\u6240\u4f7f\u7528\u7684\u5be6\u9a57\u8a9e\u6599\u662f\u7d93\u4eba\u5de5\u5207\u97f3\uff0c\u56e0\u6b64 SD \u4e2d\u8a9e\u53e5\u7684</td></tr></table>", |
| "type_str": "table", |
| "num": null |
| } |
| } |
| } |
| } |
