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| }, | |
| { | |
| "first": "S", | |
| "middle": [], | |
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| { | |
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| "last": "Yin", | |
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| "last": "Ma", | |
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| "year": 2016, | |
| "venue": "Proceedings of the 54th Annual Meeting of the Association for Computational Linguistics", | |
| "volume": "1", | |
| "issue": "", | |
| "pages": "887--896", | |
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| "urls": [], | |
| "raw_text": "D. Huang, S. Shi, C.-Y. Lin, J. Yin, and W.-Y. Ma, \"How well do computers solve math word problems? Large-scale Dataset construction and evaluation,\" in Proceedings of the 54th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), Berlin, Germany, Association for Computational Linguistics (ACL), 2016, pp. 887-896. [Online]. Available: http://www.aclweb.org/anthology/P/P16/P16-1084.pdf.", | |
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| "ref_entries": { | |
| "FIGREF0": { | |
| "num": null, | |
| "uris": null, | |
| "type_str": "figure", | |
| "text": "(a) \u7d19\u672c\u984c\u76ee\u539f\u6a23 (b) \u8cc7\u6599\u5eab\u984c\u76ee\u6a23\u8c8c \u5716\u4e8c\u3001\u683c\u5f0f\u8f49\u63db\u7684\u7b26\u865f\u4e1f\u5931\u554f\u984c \u7576\u7136\uff0c\u5728\u8655\u7406\u9019\u4e9b\u984c\u76ee\u6642\uff0c\u6211\u5011\u9084\u9047\u5230\u4e00\u4e9b\u4e0d\u53ef\u907f\u514d\u7684\u96dc\u8a0a\u554f\u984c\u3002\u4f8b\u5982\uff1a\u6587\u672c\u4e2d\u5e36 \u6709\u8a31\u591a\u7684\u5783\u573e\u7b26\u865f(\u5716\u4e00(a)(b)) \uff0c\u4e26\u4e14\u5728\u683c\u5f0f\u8f49\u63db\u6642\u56e0\u7b26\u865f\u8cc7\u8a0a\u4e1f\u5931\u800c\u5c0e\u81f4\u984c\u76ee\u5167\u5bb9\u8868 3\"\u90e8\u5206(\u4e43\u984c\u76ee\u4e2d\u63d0\u4f9b \u89e3\u984c\u6240\u9700\u4e4b\u8cc7\u8a0a\uff1b\u4f46\u53ef\u80fd\u5305\u542b\u984d\u5916\u7684\u4e0d\u76f8\u95dc\u8a0a\u606f)\u548c\"\u554f\u53e5\"\u90e8\u5206\u5169\u6bb5\u6210\u5206\u3002\u5728\u4e00\u500b\u984c\u76ee \u4e2d\uff0c\"\u554f\u53e5\"\u4e3b\u8981\u662f\u4ecb\u65bc\u554f\u865f\u4e4b\u524d\u548c\u6700\u5f8c\u4e00\u500b\u9017\u865f\u4e4b\u5f8c\u3002\u800c\u5c0d\u65bc\u591a\u91cd\u554f\u53e5\uff0c\u6211\u5011\u5247\u662f\u5c07\u984c \u76ee\u4ee5\u624b\u52d5\u65b9\u5f0f\u5206\u958b\u6210\u4e00\u500b\"\u4e3b\u5e79\"\u548c\u591a\u500b\"\u554f\u53e5\"\uff0c\u4f7f\u4e0d\u540c\u7684\"\u554f\u53e5\"\u548c\u76f8\u540c\u7684\"\u4e3b\u5e79\"\u53ef\u4ee5\u5206 \u5225\u9023\u63a5\u5728\u4e00\u8d77\u6c42\u89e3\u3002 )\u4e2d\u7b2c\u4e00\u5217\"Train-G3-001533\"\uff0c\u8868\u793a\u6b64\u984c\u662f\u88ab\u6307\u5b9a\u70ba\u8a13\u7df4\u96c6\u4e2d\u5c6c\u65bc\u4e09\u5e74 \u7d1a\u7684\u7b2c 1533 \u865f\u984c\u76ee (\u4e5f\u5c31\u662f\u6b64\u984c\u662f\u88ab\u5132\u5b58\u5728\u8a13\u7df4\u96c6\u4e2d\uff0c\u4e09\u5e74\u7d1a\u7684\u7b2c 16 \u500b XML \u6a94\u6848\u88e1) \uff0c \u4ee5\u65b9\u4fbf\u7814\u7a76\u8005\u5b9a\u4f4d\u6216\u67e5\u8a62\u3002\u53e6\u5916\uff0c\u6bcf\u500b\u984c\u76ee\u9084\u9644\u6709\u4e00\u500b\u5e74\u7d1a\u7e3d\u7de8\u865f(\u5373\u5716\u56db(a)\u4e2d\u7b2c\u4e00 \u5217\u7684 ID) \u3002\u5982\u6b64\u4f8b\u4e2d\"IIS-MR-MATH-GRADE03-001752\"\uff0c\u8868\u793a\u6b64\u984c\u865f\u662f\u4f4d\u65bc\u5168\u90e8\u4e09\u5e74\u7d1a \u4e2d\u7b2c 1752 \u865f\u984c\u76ee(\u6b64\u7de8\u865f\u70ba\u6b64\u984c\u5728\u539f\u59cb\u984c\u5eab\u7684\u4f4d\u7f6e\uff0c\u4ee5\u4fbf\u5c0d\u6bd4\u539f\u59cb\u8cc7\u6599\u4f5c\u6821\u5c0d) \u3002 \u5728 XML \u6a94\u6848\u4e2d\uff0c\u6bcf\u4e00\u500b\u984c\u76ee\u5747\u5305\u542b\u4e86\"\u4e3b\u5e79\"\u3001 \"\u554f\u53e5\"\u3001\u548c\"\u7b54\u6848\"\u90e8\u5206(\u6211\u5011\u5728\u5169 \u7aef\u5206\u5225\u7d66\u4e88\"Body\"\u3001\"Question\"\u548c\"Answer\"\u7684\u6a19\u7c64) (\u5982\u5716\u56db(a)\u6240\u793a) \u3002\u5982 3.2 \u7bc0\u6240\u8ff0\uff0c \u6bcf\u4e00\u500b\u554f\u53e5\u548c\u4e00\u500b\u7b54\u6848\u70ba\u4e00\u7d44\u3002\u56e0\u6b64\u6bcf\u4e00\u7d44\u554f\u53e5\u548c\u7b54\u6848\u7686\u6709\u4e00\u500b\u7de8\u865f(QA idx\uff0c\u5982\u5716\u56db (b)\u6240\u793a) \uff0c\u76ee\u7684\u5728\u65bc\u6c42\u89e3\u591a\u91cd\u554f\u53e5\u984c\u76ee\u6642\uff0c\u80fd\u53c3\u8003\u5148\u524d\u554f\u7b54\u6240\u63d0\u4f9b\u7684\u89e3\u984c\u8a0a\u606f\uff0c\u4ee5\u6c42\u89e3 \u4e0b\u4e00\u500b\u554f\u7b54\u3002\u4ee5\u5716\u56db(b)\u70ba\u4f8b\uff0c\u53ef\u7531\u7b2c\u4e00\u500b\u554f\u53e5\u7684\u6c42\u89e3\u904e\u7a0b(77\u00f79\uff1d8 \u9918 5)\u4f86\u7372\u5f97\u7b2c\u4e8c \u500b\u554f\u53e5(\"\u5269\u4e0b\u5e7e\u516c\u5206\uff1f\")\u7684\u7b54\u6848(\u5373\"5 \u516c\u5206\")\u3002 \u6bcf\u4e00\u500b\u984c\u76ee\u9664\u5305\u542b\"\u4e3b\u5e79\"\u90e8\u5206\u548c\"\u554f\u53e5\"\u90e8\u5206\u5916\uff0c\u9084\u6a19\u8a3b\u4e86\u4ee5\u4e0b\u8a0a\u606f\uff1a(1)\u7b54\u6848\u7684\u55ae \u4f4d\uff0c(2)\u6982\u6578\u548c\u5206\u6578\u554f\u984c\u7684\u6a19\u8a3b\u3002\u9019\u5169\u9805\u7684\u6a19\u8a3b\u5de5\u4f5c\uff0c\u6211\u5011\u4ecd\u7136\u5229\u7528 Microsoft Office Excel \u548c\u6587\u5b57\u7de8\u8f2f\u5668\u5de5\u5177\u4f86\u5e6b\u52a9\u5b8c\u6210\u3002\u5177\u9ad4\u5167\u5bb9\u8a73\u8ff0\u5982\u4e0b\u3002" | |
| }, | |
| "TABREF0": { | |
| "content": "<table><tr><td>\u5167\u5bb9\uff0c\u6211\u5011\u50c5\u50c5\u6e05\u9664\u4e86\u7121\u95dc\u89e3\u984c\u7684\u5783\u573e\u7b26\u865f\uff0c\u4e26\u4e0d\u589e\u522a\u539f\u59cb\u5167\u5bb9\uff0c\u4ee5\u5b8c\u6574\u5448\u73fe\u6587\u5b57\u6558\u8ff0 \u4f9d\u5b58\u7d50\u69cb[10]\u3001\u7247\u8a9e\u7d50\u69cb[11]\u3001\u8a9e\u7bc7 (Discourse) [12]\u3001\u5171\u540c\u6307\u7a31\u95dc\u4fc2 (Coreference Relations) \u7d1a\u5206\u5225\u5efa\u7acb(\u975e\u4e0d\u5206\u5e74\u7d1a\u6df7\u5728\u4e00\u8d77) \uff0c\u5305\u542b\u6240\u6709\u6578\u5b78\u554f\u984c(\u53ea\u904e\u6ffe\u6389\u90a3\u4e9b\u7d14\u6578\u5b57\u554f\u984c) \uff0c \u7b2c\u56db\uff0c\u96a8\u8457\u5404\u500b\u5e74\u7d1a\u4e0d\u540c\uff0c\u6578\u5b78\u6587\u5b57\u554f\u984c\u7684\u96e3\u6613\u7a0b\u5ea6\u4e5f\u4e0d\u76f8\u540c\u3002\u6211\u5011\u4e0d\u61c9\u5c07\u4ed6\u5011\u6df7 \u4e9b\u662f\u5229\u7528\u6587\u5b57\u8868\u793a(\u4f8b\u5982\uff1a\u662f\u975e\u984c\u3001\u9078\u64c7\u984c\u3001\u61c9\u7528\u984c\u3001\u586b\u7a7a\u984c\u7b49\u7b49) \uff0c\u6709\u4e9b\u5247\u4ee5\u6578\u5b78\u7b26</td></tr><tr><td>\u4e4b\u539f\u8c8c\u3002\u6b64\u5916\uff0c\u6211\u5011\u9084\u52a0\u8a3b\u4e86\u984d\u5916\u7684\u8a0a\u606f\uff0c\u4e26\u5728\u90e8\u5206\u984c\u76ee\u4e0a\u6a19\u8a3b\u984c\u578b\u548c\u8a9e\u8a00\u5206\u6790\uff0c\u4ee5\u52a9 [13]\u3001\u548c\u8ff0\u8a9e\u53c3\u6578(Predicate-Argument)[13, 14]\u7b49\u3002\u7136\u800c\u6578\u5b78\u6587\u5b57\u554f\u984c\u6587\u672c\u7684\u7279\u6027\uff0c\u8207 \u4e26\u4e14\u8a3b\u8a18\u6559\u80b2\u90e8\u9812\u5e03\u7684\u80fd\u529b\u6307\u6a19\u6240\u5c0d\u61c9\u7684\u6578\u5b78\u4e3b\u984c\u3002\u53e6\u5916\uff0c\u70ba\u4e86\u8b93\u6211\u5011\u7684\u8cc7\u6599\u4e0d\u5931\u771f\uff0c \u5408\u800c\u4e0d\u52a0\u5206\u8fa8\uff0c\u800c\u61c9\u5206\u5225\u6a19\u8a18\u5176\u5c0d\u61c9\u7684\u5404\u500b\u5e74\u7d1a\uff0c\u4ee5\u4fbf\u77e5\u9053\u5404\u500b\u89e3\u984c\u7cfb\u7d71\u5230\u9054\u4f55\u7a2e\u7a0b\u5ea6\u3002 \u865f\u6216\u8a08\u7b97\u5f0f\u7684\u65b9\u5f0f\u5448\u73fe(\u4f8b\u5982\uff1a\u8a08\u7b97\u984c) \uff0c\u53e6\u5916\u9084\u6709\u4ee5\u5716\u6848\u8868\u9054\u3001\u4f5c\u7b54\u4e4b\u65b9\u5f0f(\u4f8b\u5982\uff1a</td></tr><tr><td>\u7814\u7a76\u8005\u77ad\u89e3\u6578\u5b78\u6587\u5b57\u554f\u984c\u4e4b\u7279\u6027\u53ca\u5efa\u7acb\u6a21\u578b\u3002 \u5176\u4ed6\u9818\u57df\u7684\u6587\u672c\u7279\u6027\u4e26\u4e0d\u76f8\u540c(\u5982\u542b\u6709\u76f8\u7576\u591a\u7684\u96f6\u6307\u4ee3(Zero Anaphora)\u7b49) \uff0c\u800c\u4e14\u9700 \u6211\u5011\u4e0d\u5c0d\u4efb\u4f55\u6587\u53e5\u4f5c\u589e\u522a\u3002\u56e0\u6b64\u672c\u8a9e\u6599\u5eab\u4e26\u975e\u91dd\u5c0d\u67d0\u4e00\u500b\u7279\u5b9a\u89e3\u984c\u7cfb\u7d71\u6240\u69cb\u5efa\uff0c\u53ef\u9069\u7528 \u914d\u5408\u984c\u3001\u756b\u756b\u770b\u3001\u91cf\u91cf\u770b\u3001\u770b\u5716\u505a\u505a\u770b\u7b49\u7b49) \uff0c\u4e0d\u52dd\u679a\u8209\u3002\u672c\u8a9e\u6599\u5eab\u50c5\u53d6\u51fa\u4ee5\u6587\u5b57\u8868\u793a \u7b2c\u4e94\uff0c\u570b\u5c0f\u6578\u5b78\u6587\u5b57\u554f\u984c\u61c9\u5305\u62ec\u6240\u6709\u51fa\u73fe\u7684\u6578\u5b78\u904b\u7b97\u984c\u578b\u3002\u800c\u4e14\u6bcf\u500b\u6578\u5b78\u554f\u984c\u61c9\u6a19 \u8981\u984d\u5916\u7684\u5e74\u7d1a\u5206\u985e\u3001\u6578\u5b78\u4e3b\u984c\u3001\u6240\u9700\u6578\u5b78\u904b\u7b97\u53ca\u7b54\u6848\u7b49\u5176\u4ed6\u975e\u8a9e\u8a00\u5b78\u7684\u6a19\u8a3b\u3002\u56e0\u6b64\u82e5\u8981 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\u5efa\u7acb\u4e00\u500b\u6578\u5b78\u6587\u5b57\u554f\u984c\u89e3\u984c\u7cfb\u7d71\uff0c\u6709\u5fc5\u8981\u69cb\u5efa\u4e00\u500b\u5c0d\u61c9\u7684\u6578\u5b78\u6587\u5b57\u554f\u984c\u8a9e\u6599\u5eab\u3002 \u8a3b\u5176\u6240\u5c6c\u7684\u4e3b\u984c(\u5982\u6642\u9593\u3001\u7a7a\u9593\u7b49)\u53ca\u7b54\u6848\uff0c\u4ee5\u65b9\u4fbf\u5728\u7814\u767c\u89e3\u984c\u7cfb\u7d71\u6642\uff0c\u53ef\u4ee5\u660e\u78ba\u77e5\u9053 \u6790\uff0c\u4ee5\u4f9b\u8a2d\u8a08\u767c\u5c55\u7cfb\u7d71\u7684\u4eba\u4f5c\u70ba\u53c3\u8003\u3002 \u8868\u4e00\u3001\u4e09\u7a2e\u7279\u5b9a\u984c\u578b\u7684\u539f\u59cb\u984c\u76ee\u8cc7\u6599 \u7cfb\u7d71\u7684\u7f3a\u6f0f\u4e26\u80fd\u81ea\u52d5\u8a55\u4f30\u6b63\u78ba\u7387\u3002 \u7b2c\u516d\uff0c\u81f3\u5c11\u5728\u90e8\u5206\u6578\u5b78\u554f\u984c\u4e0a\uff0c\u61c9\u6a19\u8a3b\u984c\u76ee\u4e2d\u7684\u8a9e\u8a00 \u540c\u7d44\u614b\u7684\u7279\u6b8a\u8a9e\u6599\u5eab\uff0c\u5c07\u6709\u52a9\u65bc\u5f9e\u4e8b\u76f8\u95dc\u7814\u7a76\u8005\u4e4b\u5de5\u4f5c\u3002 \u76ee\u524d\u570b\u969b\u4e0a\u5df2\u6709\u6578\u500b\u82f1\u6587\u6578\u5b78\u6587\u5b57\u554f\u984c\u984c\u5eab[2, 5-6, 32-34]\u53ef\u4f9b\u89e3\u984c\u7cfb\u7d71\u6bd4\u8f03\u3002\u5b83\u5011 \u4e3b\u8981\u662f\u5f9e\u7f8e\u570b\u6578\u5b78\u8ad6\u58c7\u7db2\u7ad9\uff0c\u6293\u53d6\u7279\u5b9a\u7684\u6578\u5b78\u984c\u76ee\u3002\u7136\u800c\u5b83\u5011\u53ea\u6db5\u84cb\u5c11\u6578\u6578\u5b78\u904b\u7b97\u5b50 (\u5373 \u672c\u8ad6\u6587\u5176\u9918\u90e8\u5206\u7684\u5b89\u6392\u5982\u4e0b\uff1a\u7b2c\u4e8c\u7ae0\uff0c\u63ed\u793a\u4e00\u500b\u7406\u60f3\u7684\u6578\u5b78\u6587\u5b57\u554f\u984c\u8a9e\u6599\u5eab\u8a72\u5982\u4f55 \u69cb\u5efa\uff1b\u7b2c\u4e09\u7ae0\uff0c\u6558\u8ff0\u5efa\u69cb\u8a9e\u6599\u5eab\u7684\u904e\u7a0b\u53ca\u5de5\u4f5c\uff1b\u7136\u5f8c\u5728\u7b2c\u56db\u7ae0\u8a0e\u8ad6\u6211\u5011\u5728\u5efa\u69cb\u8a9e\u6599\u5eab\u6642\uff0c \u5b78\u73fe\u8c61(\u4f8b\u5982\uff1a\u53e5\u6cd5\u5206\u6790\u3001\u6307 \u984c\u578b \u6587\u5b57\u984c\u76ee</td></tr><tr><td>\u52a0\u6cd5\uff0c\u6e1b\u6cd5\uff0c\u4e58\u6cd5\uff0c\u9664\u6cd5\u548c\u4ee3\u6578\u65b9\u7a0b) \uff0c\u4e26\u4e14\u522a\u9664\u8d85\u904e\u4ed6\u5011\u7cfb\u7d71\u89e3\u984c\u80fd\u529b\u7684\u984c\u76ee(\u6216\u5c0d \u6240\u9047\u5230\u7684\u554f\u984c\u548c\u6240\u5b78\u5230\u7684\u7d93\u9a57\uff1b\u7b2c\u4e94\u7ae0\uff0c\u56de\u9867\u76f8\u95dc\u7684\u7814\u7a76\u5de5\u4f5c\uff1b\u6700\u5f8c\uff0c\u7d50\u8ad6\u5c07\u64b0\u5beb\u5728\u7b2c \u4ee3\u3001\u860a\u6db5\u3001\u6307\u7a31\u7b49)\u53ca\u6240\u5c0d\u61c9\u7684\u6578\u5b78\u904b\u7b97\u5f0f\uff0c\u4ee5\u65b9\u4fbf\u5728\u7814\u767c\u89e3\u984c\u7cfb\u7d71\u6642\uff0c\u7814\u7a76\u8005\u53ef\u4ee5\u64da \u9078\u64c7\u984c 1 \u96bb\u8718\u86db\u6709 8 \u96bb\u8173\uff0c8 \u96bb\u8718\u86db\u5171\u6709\u5e7e\u96bb\u8173\uff1f(\u246042 \u96bb\u246164 \u96bb\u246274 \u96bb)</td></tr><tr><td>\u95dc\u9375\u8a5e\uff1a\u6578\u5b78\u6587\u5b57\u554f\u984c\uff0c\u8a9e\u6599\u5eab\uff0c\u6a19\u8a3b \u984c\u76ee\u5167\u5bb9\u52a0\u4ee5\u4eba\u70ba\u4fee\u6539\uff0c\u4ee5\u6eff\u8db3\u7cfb\u7d71\u8981\u6c42) \u3002\u6b64\u5916\uff0c\u4e5f\u6c92\u6709\u5c0d\u6578\u5b78\u984c\u76ee\u505a\u5e74\u7d1a\u5206\u5c64\u3002\u6700 \u516d\u7ae0\u3002 \u4ee5\u5206\u6790\u5efa\u6a21\uff0c\u4e26\u53ef\u57f7\u884c\u534a\u6559\u5c0e\u5f0f\u5b78\u7fd2(Semi-supervised Learning) \u3002 \u82e5\u4e00\u500b\u6578\u5b78\u6587\u5b57\u554f\u984c \u61c9\u7528\u984c 6 \u5305\u7cd6\u679c\u8ce3 186 \u5143\uff0c10 \u5305\u7cd6\u679c\u8ce3\u5e7e\u5143\uff1f</td></tr><tr><td>1. \u7dd2\u8ad6 \u5f8c\uff0c\u984c\u5eab\u4e0a\u6c92\u6709\u6a19\u8a3b\u4efb\u4f55\u8a9e\u8a00\u5b78\u8cc7\u8a0a(\u56e0\u6b64\u7121\u6cd5\u53c3\u7167\u5c0d\u61c9\u7684\u8a9e\u8a00\u5206\u6790) \u3002\u4f8b\u5982\uff0cKushman \u586b\u7a7a\u984c \u91cf\u89d2\u5668\u4e2d\u5f9e\u523b\u5ea6 0 \u7684\u7dda\u65cb\u8f49\u5230\u523b\u5ea6 20 \u7684\u7dda\uff0c\u6240\u5f62\u6210\u7684\u89d2\u662f()\u5ea6\u3002 \u8a9e\u6599\u5eab\uff0c\u80fd\u6eff\u8db3\u4e0a\u8ff0\u6240\u5217\u7684\u516d\u500b\u689d\u4ef6\uff0c\u5c31\u53ef\u7528\u4f86\u6e05\u695a\u7684\u8a55\u4f30\u89e3 \u7b49\u4eba[5]\u50c5\u50c5\u6293\u53d6\u4ee3\u6578\u6587\u5b57\u554f\u984c\u7684\u984c\u76ee\uff1b\u800c Hosseini \u7b49\u4eba[2]\u4e5f\u53ea\u662f\u622a\u53d6\u51fa\u7d44\u5408\u52a0\u6cd5\u3001\u6e1b \u6cd5\u3001\u4e00\u5143\u4e00\u6b21\u65b9\u7a0b\u548c\u7f8e\u5143\u6587\u5b57\u554f\u984c\u3002\u6700\u8fd1\uff0cRoy \u7b49\u4eba[6]\u5247\u662f\u767c\u4f48\u76f8\u5c0d\u8f03\u5927\u7684\u8a9e\u6599\u5eab\u3002\u4ed6 \u5728\u8868\u4e00\u6240\u793a\u7684\u9078\u64c7\u984c\u8207\u586b\u7a7a\u984c\u7576\u4e2d\uff0c\u901a\u5e38\u542b\u6709\u975e\u6587\u5b57\u3001\u6578\u5b57\u7b49\u8207\u89e3\u984c\u7121\u95dc\u7684\u7b26\u865f\uff0c 2. \u7406\u60f3\u6578\u5b78\u6587\u5b57\u554f\u984c\u8a9e\u6599\u5eab\u7684\u7279\u6027 \u984c\u7cfb\u7d71\u7684\u771f\u6b63\u7a0b\u5ea6(\u4e26\u53ef\u8207\u570b\u5c0f\u5404\u500b\u5e74\u7d1a\u5c0d\u61c9) \u3001\u516c\u5e73\u6bd4\u8f03\u4e0d\u540c\u7cfb\u7d71\u7684\u89e3\u984c\u80fd\u529b\u3001\u4e26\u660e \u56e0\u6b64\u6211\u5011\u5fc5\u9808\u505a\u4e9b\u4fee\u6b63\u3002\u4f8b\u5982\uff1a\u5c07\u9078\u64c7\u984c\u9078\u9805\u90e8\u5206\u522a\u9664\u3001\u628a\u586b\u7a7a\u984c\u62ec\u865f\u6539\u70ba\u570b\u5b57\"\u5e7e\"\uff0c</td></tr><tr><td>\u6c42\u89e3\u6578\u5b78\u6587\u5b57\u554f\u984c(Math Word Problem)[1-4]\uff0c\u57fa\u65bc\u4ee5\u4e0b\u7684\u539f\u56e0[3, 4]\u5e38\u88ab\u9078\u4f5c\u7814\u7a76\u81ea\u7136\u8a9e \u8a00\u7406\u89e3\u7684\u6e2c\u8a66\u6848\u4f8b\uff1a(1) \u6578\u5b78\u6587\u5b57\u554f\u984c\u7684\u7b54\u6848\uff0c\u7121\u6cd5\u55ae\u7d14\u5730\u85c9\u7531\u5be6\u884c\u95dc\u9375\u5b57\u6216\u7279\u5fb5\u914d\u5c0d \u88ab\u64f7\u53d6(\u5982\u50b3\u7d71\u7684\u554f\u7b54(Q&A)\u7cfb\u7d71) \uff0c\u56e0\u6b64\u53ef\u4ee5\u6e05\u695a\u5730\u986f\u793a\u51fa\u7406\u89e3\u548c\u63a8\u7406\u7684\u512a\u52e2\u3002(2) \u8207\u5176\u4ed6\u9818\u57df\u76f8\u6bd4\uff0c\u6578\u5b78\u6587\u5b57\u554f\u984c\u901a\u5e38\u4e0d\u5177\u6709\u90a3\u9ebc\u8907\u96dc\u7684\u8a9e\u6cd5(\u5982\u4eba\u6587\u793e\u6703\u9818\u57df) \uff0c\u4e26\u50c5 \u5011\u5728\u984c\u76ee\u4e2d\u589e\u52a0\u4e86\u9700\u8981\u5169\u500b\u4ee5\u4e0a\u904b\u7b97\u5b50(Multi-step)\u7684\u7b97\u8853\u554f\u984c\uff0c\u4f46\u522a\u53bb\u4e86\u9700\u8981\u80cc\u666f \u77e5\u8b58\u7684\u554f\u984c\u3002\u6b64\u5916\uff0c\u6982\u6578\u554f\u984c\u4e5f\u4e00\u4f75\u88ab\u522a\u6389\u3002Upadhyay \u548c Chang[32]\u3001Koncel-Kedziorski \u7b49\u4eba[33]\u3001Huang \u7b49\u4eba[34]\u96d6\u7136\u5229\u7528\u722c\u87f2\u7a0b\u5f0f(Crawler)\u6216\u81ea\u52d5\u62bd\u53d6\u7a0b\u5f0f\u500b\u5225\u5efa\u7acb\u4e86\u5927 \u578b(1,000 \u984c\u30013,320 \u984c\u548c 18,000 \u984c)\u7684\u591a\u6a23\u6027\u6578\u5b78\u6587\u5b57\u554f\u984c\u984c\u5eab\uff0c\u66f4\u63a1\u7528\u6700\u4f73\u5316\u6f14\u7b97 \u78ba\u6307\u51fa\u5404\u7cfb\u7d71\u7684\u504f\u9817\u53ca\u7f3a\u5931\u3002\u6b64\u5916\uff0c\u82e5\u6709\u500b\u5225\u7684\u61c9\u7528\u6216\u898f\u683c\uff0c\u4ea6\u80fd\u8b93\u7814\u767c\u4eba\u54e1\u6309\u5176\u5404\u81ea \u4e26\u5c07\u53e5\u865f\u63db\u4e0a\u554f\u865f\u800c\u6210\u70ba\u554f\u53e5\uff0c\u5982\u4e0b\u8868\u6240\u793a\u3002 \u5728\u8a2d\u8a08\u69cb\u5efa\u4e00\u500b\u6578\u5b78\u6587\u5b57\u554f\u984c\u8a9e\u6599\u5eab\u4e4b\u524d\uff0c\u6211\u5011\u5fc5\u9808\u5148\u78ba\u7acb\u4e00\u4e9b\u6e96\u5247\uff0c\u4ee5\u4fbf\u5728\u5404\u7a2e\u8a2d\u8a08 \u9700\u6c42\uff0c\u7d44\u5408\u51fa\u5c0d\u61c9\u7684\u6aa2\u7d22\u689d\u4ef6\uff0c\u5f9e\u5b8c\u6574\u7684\u8a9e\u6599\u5eab\u4e2d\u62bd\u53d6\u51fa\u4e0d\u540c\u7d44\u614b\u7684\u7279\u6b8a\u8a9e\u6599\u5eab\uff0c\u4fbf\u65bc \u9078\u9805\u4e0a\uff0c\u80fd\u6709\u6240\u53d6\u6368\u3002\u7531\u65bc\u6c42\u89e3\u6578\u5b78\u6587\u5b57\u554f\u984c\u662f\u81ea\u7136\u8a9e\u8a00\u7406\u89e3\u5728\u4eba\u5de5\u667a\u6167\u4e0a\u9762\u7684\u61c9\u7528\uff0c \u8868\u4e8c\u3001\u8655\u7406\u5f8c\u7684\u6578\u5b78\u6587\u5b57\u8cc7\u6599 \u4f9b\u5404\u7814\u7a76\u7cfb\u7d71\u4f86\u8a55\u91cf\u81ea\u5df1\u7684\u6548\u80fd\u3002\u4f8b\u5982\uff1a\u53ef\u6309\u5e74\u7d1a\u5206\u5c64\u3001\u4f9d\u4e3b\u984c\u5206\u985e\u3001\u4f9d\u6578\u5b78\u984c\u578b\u5206\u985e\u3001 \u56e0\u6b64\u4e00\u500b\u7406\u60f3\u7684\u6578\u5b78\u6587\u5b57\u554f\u984c\u8a9e\u6599\u5eab\uff0c\u6211\u5011\u8a8d\u70ba\u61c9\u8a72\u5177\u5099\u4e0b\u5217\u7279\u6027(\u800c\u524d\u8ff0\u7684\u82f1\u6587\u6578\u5b78 \u6587\u5b57\u554f\u984c\u984c\u5eab\uff0c\u90fd\u6216\u591a\u6216\u5c11\u9055\u53cd\u9019\u4e9b\u539f\u5247) \u3002 \u6216\u7d44\u5408\u4e0a\u8ff0\u689d\u4ef6\u7b49\u3002 \u984c\u578b \u6587\u5b57\u984c\u76ee</td></tr><tr><td>\u9700\u8981\u5c11\u91cf\u7684\u9818\u57df\u77e5\u8b58(\u8207\u7269\u7406\u5316\u5b78\u9818\u57df\u76f8\u8f03) \uff0c\u56e0\u6b64\u7814\u7a76\u4eba\u54e1\u53ef\u4ee5\u8457\u91cd\u65bc\u81ea\u7136\u8a9e\u8a00\u7406\u89e3 \u548c\u63a8\u7406\u7684\u4efb\u52d9\u4e0a\u3002(3) \u6578\u5b78\u6587\u5b57\u554f\u984c\u7684\"\u4e3b\u5e79\"\u90e8\u5206(\u5373\u63cf\u8ff0\u554f\u984c\u7d66\u5b9a\u8cc7\u8a0a\u7684\u90e8\u5206) \uff0c\u901a\u5e38 \u6cd5\u964d\u4f4e\u5927\u91cf\u65b9\u7a0b\u5f0f\u6a23\u677f\u548c\u8fad\u5f59\u7684\u91cd\u8907\u7387[33]\uff0c\u53ef\u60dc\u90fd\u9084\u662f\u4ee5\u7dda\u6027\u4ee3\u6578\u554f\u984c\u70ba\u4e3b\uff0c\u800c\u6839\u672c \u5ffd\u7565\u90a3\u4e9b\u7dda\u6027\u4ee3\u6578\u4ee5\u5916\u7684\u6578\u5b78\u554f\u984c\u3002 \u7b2c\u4e00\uff0c\u5b83\u61c9\u5305\u542b\u5404\u7a2e\u81ea\u7136\u8a9e\u8a00\u7684\u63cf\u8ff0\u65b9\u5f0f(\u56e0\u70ba\u6c42\u89e3\u7cfb\u7d71\u61c9\u8a72\u5177\u5099\u76f8\u7576\u7684\u81ea\u7136\u8a9e\u8a00 \u9078\u64c7\u984c 1 \u96bb\u8718\u86db\u6709 8 \u96bb\u8173\uff0c8 \u96bb\u8718\u86db\u5171\u6709\u5e7e\u96bb\u8173\uff1f 3. \u8a9e\u6599\u5eab\u69cb\u5efa \u586b\u7a7a\u984c \u91cf\u89d2\u5668\u4e2d\u5f9e\u523b\u5ea6 0 \u7684\u7dda\u65cb\u8f49\u5230\u523b\u5ea6 20 \u7684\u7dda\uff0c\u6240\u5f62\u6210\u7684\u89d2\u662f\u5e7e\u5ea6\uff1f</td></tr><tr><td>\u53ea\u7531\u5c11\u6578\u53e5\u5b50\u7d44\u6210\uff0c\u56e0\u6b64\u7406\u89e3\u548c\u63a8\u7406\u904e\u7a0b\u80fd\u88ab\u7a0b\u5f0f\u5feb\u901f\u57f7\u884c\uff0c\u52a0\u901f\u7814\u767c\u9032\u7a0b\u3002(4) \u6578\u5b78 \u6587\u5b57\u554f\u984c\u6c42\u89e3\u5668\u6709\u5be6\u969b\u7684\u61c9\u7528\uff0c\u5982\u5c0f\u5b78\u6578\u5b78\u5bb6\u6559\uff0c\u548c\u751f\u6d3b\u6578\u5b78\u52a9\u624b\u7b49\u3002 \u5728 2014 \u5e74\u4ee5\u524d\uff0c\u5927\u591a\u6578\u63d0\u51fa\u7684\u65b9\u6cd5\u90fd\u662f\u57fa\u65bc\u898f\u5247(Rule-based)\u7684[1-2, 35-37]\uff0c\u4e5f \u7406\u89e3\u80fd\u529b) \u3002\u56e0\u6b64\u6211\u5011\u4e0d\u61c9\u5c0d\u984c\u76ee\u8868\u9054\u65b9\u5f0f\u52a0\u4ee5\u4eba\u70ba\u4fee\u6539(\u5373\u4eba\u5de5\u7c21\u5316\u53e5\u578b) \uff0c\u800c\u61c9\u5b8c\u6574 \u672c\u7ae0\u7bc0\u5b89\u6392\u5982\u4e0b\uff1a3.1 \u7bc0\u63d0\u53ca\u539f\u59cb\u984c\u5eab\u7684\u4f86\u6e90\u8207\u7d44\u6210\uff1b\u7136\u5f8c\u6211\u5011\u5728 3.2 \u7bc0\u8a0e\u8ad6\u8a9e\u6599\u5eab\u9810 \u81f3\u4eca\u96d6\u7136\u6709\u5c11\u6578\u7814\u7a76\u4e2d\u6587\u6578\u5b78\u6587\u5b57\u554f\u984c\u7684\u8ad6\u6587\u767c\u8868[35-37]\uff0c\u4f46\u64da\u6211\u5011\u6240\u77e5\uff0c\u9084\u6c92\u6709 \u4fdd\u7559\u6240\u6709\u6578\u5b78\u6587\u5b57\u554f\u984c\u7684\u73fe\u5b58\u578b\u5f0f(\u9664\u4e86\u6e05\u9664\u4e82\u78bc\u53ca\u5783\u573e\u8cc7\u8a0a\u5916) \uff0c\u4ee5\u4fbf\u516c\u5e73\u6bd4\u8f03\u4e0d\u540c \u8655\u7406\u7684\u7a0b\u5e8f\uff1b3.3 \u7bc0\u63ed\u793a\u6a19\u8a3b\u6578\u5b78\u8a9e\u6599\u5eab\u7684\u904e\u7a0b\uff1b\u6700\u5f8c\uff0c3.4 \u7bc0\u5c07\u6558\u8ff0\u5c0d\u8a9e\u6599\u5eab\u6240\u505a\u7684\u7d71 \u4efb\u4f55\u516c\u958b\u3001\u53ef\u4f9b\u7814\u7a76\u4f7f\u7528\u7684\u4e2d\u6587\u6578\u5b78\u6587\u5b57\u554f\u984c\u8a9e\u6599\u5eab\uff0c\u56e0\u6b64\u6211\u5011\u69cb\u5efa\u4e86\u7b2c\u4e00\u500b\u4e2d\u6587\u570b\u5c0f \u6578\u5b78\u6587\u5b57\u554f\u984c\u8a9e\u6599\u5eab\u3002\u5efa\u7acb\u9019\u500b\u8a9e\u6599\u5eab\u7684\u76ee\u7684\uff0c\u662f\u8981\u63d0\u4f9b\u8a9e\u6599\u5eab\u4ee5\u7814\u7a76\u6578\u5b78\u6587\u5b57\u554f\u984c\u6587 \u89e3\u984c\u7cfb\u7d71\u7684\u81ea\u7136\u8a9e\u8a00\u7406\u89e3\u80fd\u529b\u3002 \u8a08\u5206\u6790\u3002</td></tr><tr><td>\u5c31\u662f\u4ee5\u4eba\u5de5\u5efa\u69cb\u7684\u898f\u5247\u505a\u5404\u7a2e\u76f8\u95dc\u6c7a\u7b56\u7684\u5224\u65b7\u3002\u4f46\u69cb\u5efa\u4e00\u500b\u9ad8\u6db5\u84cb\u5ea6(Coverage Rate) \u672c\u7684\u7279\u6027\uff0c\u4ee5\u4fbf\u627e\u51fa\u8cbc\u5207\u7684\u7279\u5fb5\u3001\u63d0\u51fa\u5408\u9069\u7684\u67b6\u69cb\u548c\u6a21\u578b\uff0c\u4e26\u63d0\u4f9b\u7d71\u8a08\u5206\u985e\u5668\u6240\u9700\u8981\u7684 \u7b2c\u4e8c\uff0c\u6c42\u89e3\u6578\u5b78\u6587\u5b57\u554f\u984c\u662f\u4eba\u5de5\u667a\u6167\u7684\u61c9\u7528\u3002\u56e0\u6b64\u4e00\u500b\u89e3\u984c\u7cfb\u7d71\u61c9\u5177\u5099\u8655\u7406\u666e\u901a\u5e38 3.1 \u539f\u59cb\u984c\u5eab</td></tr><tr><td>\u6458\u8981 \u672c\u7bc7\u8ad6\u6587\u63d0\u51fa\u4e86\u4e00\u500b\u7406\u60f3\u6578\u5b78\u6587\u5b57\u554f\u984c\u8a9e\u6599\u5eab\u6240\u61c9\u5177\u5099\u7684\u7279\u6027\uff0c\u4e26\u6558\u8ff0\u6211\u5011\u5982\u4f55\u5efa\u7f6e\u4e00 \u500b\u5b8c\u5584\u7684\u4e2d\u6587\u570b\u5c0f\u6578\u5b78\u6587\u5b57\u554f\u984c\u8a9e\u6599\u5eab\uff1a\u5176\u904e\u7a0b\u3001\u6240\u6a19\u8a3b\u7684\u5167\u5bb9\u3001\u4ee5\u53ca\u906d\u9047\u7684\u56f0\u96e3\u3002\u6c42 \u89e3\u6578\u5b78\u6587\u5b57\u554f\u984c\u662f\u81ea\u7136\u8a9e\u8a00\u7406\u89e3\u5728\u4eba\u5de5\u667a\u6167\u9818\u57df\u7684\u4e00\u500b\u5e38\u898b\u61c9\u7528\u3002\u8fd1\u5e74\u4f86\u6709\u95dc\u65bc\u82f1\u6587\u6578 \u7684\u898f\u5247\u96c6\uff0c\u662f\u76f8\u7576\u56f0\u96e3\u4e14\u6240\u8cbb\u4e0d\u8cb2\u7684\u5de5\u4f5c\u3002\u6b64\u5916\uff0c\u5728\u6c42\u89e3\u6b67\u7570\u554f\u984c\u4e0a\u4e5f\u986f\u5f97\u7b28\u62d9\u3002\u56e0\u6b64 \u8fd1\u4f86\u5927\u90e8\u5206\u63d0\u51fa\u7684\u65b9\u6cd5[3-7]\u90fd\u662f\u57fa\u65bc\u7d71\u8a08(Statistic-based)\u7684\uff0c\u4e5f\u5c31\u662f\u5176\u4e2d\u4e00\u4e9b(\u6216\u5168 \u80fd\u8a13\u7df4\u6a21\u578b\u3002\u56e0\u70ba\u76ee\u524d\u6578\u5b78\u6587\u5b57\u554f\u984c\u6c42\u89e3\u5668\u4ee5\u5c0f\u5b78\u7a0b\u5ea6\u70ba\u4e3b\uff0c\u6240\u4ee5\u6211\u5011\u5fc5\u9808\u5148\u69cb\u5efa\u4e00\u500b \u8207\u524d\u8ff0\u7684\u82f1\u6587\u6578\u5b78\u6587\u5b57\u554f\u984c\u984c\u5eab[2, 5-6, 32-34]\u76f8\u6bd4\uff0c\u6211\u5011\u7684\u6578\u5b78\u6587\u5b57\u554f\u984c\u8a9e\u6599\u5eab\u662f \u95b1 3.3.1 \u7bc0\u8868\u4e09)\u53ca\u9700\u8981\u5169\u500b\u4ee5\u4e0a\u904b\u7b97\u5b50(Multi-step operation)\u7684\u7b97\u8853\u554f\u984c\u3002\u6211\u5011\u4e0d\u61c9 \u6b64\u8cc7\u6599\u5167\u5bb9\u9664\u4e86\u984c\u76ee\u8207\u984c\u865f\u4e4b\u5916\uff0c\u4e5f\u9644\u6709\u76f8\u5c0d\u61c9\u7684\u5b78\u671f\u5e74\u7d1a\u3001\u984c\u578b\u3001\u6559\u80b2\u90e8\u8a02\u5b9a\u7684\u80fd\u529b \u570b\u5c0f\u6578\u5b78\u6587\u5b57\u554f\u984c\u8a9e\u6599\u5eab\uff0c\u4ee5\u4fbf\u5f9e\u4e8b\u76f8\u95dc\u7684\u7814\u7a76\u3002 \u5b78\u6216\u63d0\u4f9b\u7d66\u5b78\u751f\u7df4\u7fd2\u3002\u5176\u4e2d\u5305\u542b\u570b\u8a9e\u3001\u6578\u5b78\u3001\u793e\u6703\u3001\u81ea\u7136\u3001\u751f\u6d3b\u3001\u5065\u5eb7\u9ad4\u80b2\u7b49\u79d1\u76ee\uff0c\u800c 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| |
| "html": null, | |
| "text": "The 2016 Conference on Computational Linguistics and Speech Processing ROCLING 2016, pp. 352-371 \uf0d3 The Association for Computational Linguistics and Chinese Language Processing", | |
| "num": null, | |
| "type_str": "table" | |
| }, | |
| "TABREF1": { | |
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\u5728\u8655\u7406\u6578\u5b78\u8a9e\u6599\u5eab\u7684\u5404\u968e\u6bb5\u4efb\u52d9\u4e2d\uff0c\u6211\u5011\u9047\u5230\u4e00\u4e9b\u4e0d\u540c\u7684\u554f\u984c\uff1a\u5305\u62ec\u4e86\u984c\u76ee\u5207\u5272\u3001\u6a19\u8a3b \u4e2d\u7684\u6587\u5b57\u554f\u984c\uff0c\u4e26\u4e14\u6309\u7167\u5e74\u7d1a\u5206\u5c64\u5c07\u6bcf\u984c(\u5305\u542b\u5207\u5272\u984c\u76ee\u70ba\"\u4e3b\u5e79\"\u548c\"\u554f\u53e5\"\u5169\u90e8\u5206)\u8f49 300 1,700 \u4f9b\uff0c\u800c\"\u554f\u53e5\"\u4e3b\u8981\u5728\u8a62\u554f\u55ae\u4e00\u540d\u8a5e\u7684\u6578\u91cf\uff0c\u56e0\u6b64\"\u4e3b\u5e79\"\u7684\u8a5e\u985e\u4ee5\u666e\u901a\u540d\u8a5e\u3001\u6578\u8a5e\u5b9a\u8a5e\u6700 \u55ae\u4f4d\uff0c\u4ee5\u53ca\u65b7\u8a5e\u932f\u8aa4\u7b49\u65b9\u9762\u3002\u9996\u5148\uff0c\u7576\u6211\u5011\u57f7\u884c\u5728 3.2 \u7bc0\u4e2d\u6240\u63d0\u5207\u5272\u4e3b\u5e79\u53ca\u554f\u53e5\u7684\u539f\u5247 \u6210\u89e3\u984c\u7cfb\u7d71\u5bb9\u6613\u4f7f\u7528\u7684\u683c\u5f0f\u3002\u4e0d\u50c5\u5982\u6b64\uff0c\u6211\u5011\u9084\u5728\u984c\u76ee\u4e2d\u6dfb\u52a0\u7b54\u6848\u55ae\u4f4d\u3001\u5206\u6578\u8207\u6982\u6578\u984c \u5408\u8a08 1,780 3,642 5,210 4,338 4,062 4,461 23,493 \u591a\uff0c\u4f46\"\u554f\u53e5\"\u5247\u4ee5\u666e\u901a\u540d\u8a5e\u6700\u591a\uff0c\u800c\u6578\u8a5e\u5b9a\u8a5e\u504f\u5c11\u3002 (\u5373\"\u554f\u53e5\"\u4e3b\u8981\u662f\u4ecb\u65bc\u554f\u865f\u4e4b\u524d\u8207\u6700\u5f8c\u4e00\u500b\u9017\u865f\u4e4b\u5f8c)\u5f8c\uff0c\u767c\u73fe\u5927\u90e8\u5206\u984c\u76ee\u90fd\u80fd\u6b63\u78ba\u5730 \u7684\u88dc\u5145\u8a0a\u606f\u3001\u53ca\u6559\u80b2\u90e8\u6307\u6a19\u5206\u985e\uff1b\u4e26\u65bc\u5c11\u90e8\u5206\u8a9e\u6599\u4e2d\u6a19\u8a3b\u5341\u516d\u7a2e\u984c\u76ee\u985e\u578b\u548c\u8a9e\u8a00\u5b78\u73fe\u8c61 \u610f\u7684\u5dee\u5225\u3002\u6211\u5011\u5c07\u6578\u91cf\u8907\u5408\u8a5e\u4fee\u98fe\u540d\u8a5e\u7684\u8a9e\u610f\u52a0\u4ee5\u6b63\u898f\u5316\uff0c\u7d66\u4e88\u4e00\u81f4\u6027\u7684\u8868\u9054\u65b9\u5f0f\uff0c\u4ee5 \u8868\u4e03\u3001\u4e2d\u6587\u6578\u5b78\u8a9e\u6599\u5eab\u8a5e\u6027\u6a19\u8a3b\u7d71\u8a08\u8868 \u88ab\u5207\u5272\u958b\u4f86\uff0c\u4f46\u6709\u6975\u5c11\u6578\u7684\u984c\u76ee\u4e26\u4e0d\u4e00\u5b9a\u9069\u7528(\u5982\"[\u5c0f\u7389\u548c\u5bb6\u4eba\u4e00\u8d77\u53bb\u722c\u5c71\uff0c\u4e0a\u5c71\u82b1\u4e86 (\u53e5\u6cd5\u5206\u6790\u3001\u6307\u4ee3\u3001\u860a\u6db5\u3001\u6307\u7a31\u7b49) \uff0c\u4f7f\u7814\u7a76\u8005\u53ef\u4ee5\u77ad\u89e3\u6578\u5b78\u6587\u5b57\u554f\u984c\u7684\u6587\u672c\u7279\u6027\uff0c\u4e26 \u5229\u6578\u5b78\u8a08\u7b97\u6216\u5728\u4e0d\u540c\u91cf\u8a5e\u55ae\u4f4d\u4e2d\u8f49\u63db\u3002\u4e0b\u8ff0\u70ba\u5e7e\u500b\u6578\u91cf\u8907\u5408\u8a5e\u7684\u4f8b\u5b50\uff1a \u8868\u4e94\u3001\u4e2d\u6587\u6578\u5b78\u984c\u76ee\u7684\u5e73\u5747\u9577\u5ea6 145 \u5206\uff0c\u4e0b\u5c71\u82b1\u4e86 2 \u6642 18 \u5206\uff0c\u4e0a\u5c71\u82b1\u7684\u6642\u9593\u6bd4\u8f03\u9577\uff0c][\u9084\u662f\u4e0b\u5c71\u82b1\u7684\u6642\u9593\u6bd4\u8f03\u9577\uff1f]\")\u3002 \u53ef\u8b93\u89e3\u984c\u7cfb\u7d71\u6e2c\u8a66\u6a21\u578b\u53ca\u57f7\u884c\u534a\u6559\u5c0e\u5f0f\u5b78\u7fd2\u3002 \u5716\u4e94\u3001\u6982\u6578\u8207\u5206\u6578\u7684\u984c\u76ee\u6a19\u8a3b 3.3.1 \u984c\u578b\u53ca\u8a9e\u8a00\u5b78\u6a19\u8a3b \u70ba\u4e86\u77ad\u89e3\u6587\u672c\u7684\u6027\u8cea\u3001\u57f7\u884c\u534a\u6559\u5c0e\u5f0f\u5b78\u7fd2(Semi-supervised Learning)\u53ca\u6e2c\u8a66\u6a21\u578b\uff0c\u9664 \u4e86\u5c0d\u6bcf\u4e00\u500b\u984c\u76ee\u52a0\u8a3b\u57fa\u672c\u8cc7\u8a0a\u5916(\u5982\u4e0a\u7bc0\u6240\u8ff0) \uff0c\u6211\u5011\u9084\u5f9e\u8a9e\u6599\u5eab\u7576\u4e2d\u9078\u64c7\u82e5\u5e72\u984c\u76ee\u9032 \u884c\u6578\u5b78\u984c\u578b\u53ca\u8a9e\u8a00\u5b78\u6a19\u8a3b(\u56e0\u8a9e\u6599\u5eab\u6a19\u8a3b\u975e\u5e38\u8017\u6642\u8017\u529b\uff0c\u6545\u672c\u7814\u7a76\u50c5\u6a19\u8a18 75 \u984c\u8a13\u7df4\u8a9e \u6599\u3001200 \u984c\u767c\u5c55\u96c6\u8a9e\u6599\u53ca 200 \u984c\u6e2c\u8a66\u8a9e\u6599)\u4f5c\u70ba\u5148\u5c0e\u7814\u7a76(Pilot Study)\u4e4b\u7528\uff0c\u4ee5\u671f\u5e6b \u5716\u516d\u3001\u570b\u5c0f\u6578\u5b78\u8a9e\u6599\u7684\u8a9e\u610f\u8868\u9054\u6a19\u8a18 \u8868\u4e09\u986f\u793a\u6240\u6a19\u8a3b\u7684\u5341\u516d\u7a2e\u6578\u5b78\u984c\u578b(\u4ee5\u6578\u5b78\u89e3\u984c\u65b9\u5f0f\u5206\u985e\uff0c\u800c\u975e\u4ee5\u6578\u64da\u5f62\u5f0f\u5206\u985e\uff1b \u4e26\u4f9d\u5404\u984c\u578b\u5728\u8a13\u7df4\u96c6\u4e2d\u7684\u767e\u5206\u6bd4\u6392\u5e8f) \u3002\u56e0\u540c\u984c\u4e2d\u53ef\u80fd\u6709\u591a\u500b\u554f\u53e5\uff0c\u56e0\u6b64 75 \u984c\u8a13\u7df4\u96c6\u88ab 1 \u9846\u86cb def:{\u86cb|egg: quantifier={\u9846.null|\u7121\u7fa9: quantity={1}}} 1 \u76d2\u86cb def:{\u86cb|egg: \u76d2.container={\u76d2\u5b50|box: quantity={1}}} 1 \u6253\u86cb def:{\u86cb|egg: \u6253.quantity={12: quantity={1}}} 1 \u516c\u65a4\u86cb def:{\u86cb|egg: \u516c\u65a4.weight={\u516c\u65a4: quantity={1}}} \u6211\u5011\u4f9d\u7167\u91cf\u8a5e\u4e0d\u540c\u7684\u8a9e\u7fa9\u5167\u6db5\uff0c\u7d66\u4e88\u4e0d\u540c\u7684\u5ee3\u7fa9\u77e5\u7db2\u8a5e\u5f59\u5b9a\u7fa9\uff0c\u800c\u6578\u8a5e\u548c\u91cf\u8a5e\u7d50\u5408 \u5f8c\u7684\u6578\u91cf\u8a5e\u8a9e\u610f\u8868\u9054\u65b9\u5f0f[31]\u57fa\u672c\u4e0a\u8207\u529f\u80fd\u8a5e\u76f8\u540c\uff0c\u7686\u4ee5 relation={value} \u5f62\u5f0f\u51fa\u73fe\u3002 \u4e0d\u540c\u7684\u91cf\u8a5e\u55ae\u4f4d\u5e36\u4f86\u4e0d\u540c\u7684\u8a9e\u7fa9\uff0c\u5728\u6578\u5b78\u6587\u5b57\u984c\u4e2d\u66f4\u653e\u5927\u5b83\u7684\u7279\u6b8a\u6027\uff0c\u4e8b\u7269\u5fc5\u9808\u80fd\u5920\u5728 \u984c\u76ee\u7684 \u5169\u90e8\u5206 \u5e73\u5747\u4e2d\u6587\u5b57\u6578 (Char.) \u5e73\u5747\u4e2d\u6587\u8a5e\u6578 (Word) \u8a5e\u985e \u4e3b\u5e79 \u554f\u53e5 \u666e\u901a\u540d\u8a5e(Na) 29.0% 41.2% \u6578\u8a5e\u5b9a\u8a5e(Neu) 26.1% 7.6% \u518d\u8005\uff0c\u5728\"\u4e3b\u5e79\"\u90e8\u5206\u4ee5\u641c\u5c0b\u95dc\u9375\u5b57\"\u5927\u7d04\"\u3001 \"*\u5206\u4e4b*\"\u4f86\u67e5\u627e\u6982\u6578\u3001\u5206\u6578\u984c\u76ee\u662f\u4e0d\u5bb9\u6613 \u672c\u8a9e\u6599\u5eab\u662f\u7b2c\u4e00\u500b\u5b8c\u6574\u7684\u4e2d\u6587\u570b\u5c0f\u6578\u5b78\u6587\u5b57\u554f\u984c\u7684\u8a9e\u6599\u5eab\uff0c\u53ef\u7528\u4f86\u6e05\u695a\u7684\u8a55\u4f30\u89e3\u984c \u7684\uff0c\u53cd\u800c\u5fc5\u9808\u85c9\u7531\"\u554f\u53e5\"\u6240\u8981\u6c42\u7b54\u6848\u7684\u6558\u8ff0(\u5982\"\u5927\u7d04\"\u3001\"\u7d04\"\u3001\"*\u5206\u4e4b*\"\u7b49) \uff0c\u624d\u5f97\u4ee5\u627e \u7cfb\u7d71\u7684\u771f\u6b63\u7a0b\u5ea6(\u8207\u570b\u5c0f\u5404\u500b\u5e74\u7d1a\u5c0d\u61c9) \u3001\u516c\u5e73\u6bd4\u8f03\u4e0d\u540c\u7cfb\u7d71\u7684\u89e3\u984c\u80fd\u529b\u3001\u4e26\u53ef\u6e05\u695a\u7684 \u5230\u6b64\u985e\u984c\u76ee\u3002\u4ee5\u4e0a\u9019\u4e9b\u554f\u984c\uff0c\u7686\u662f\u85c9\u7531\u4eba\u5de5\u6aa2\u8996\u5f8c\u624d\u4e88\u4ee5\u78ba\u8a8d\u4e26\u4fee\u8a02\u3002 \u53cd\u6620\u51fa\u5404\u7cfb\u7d71\u7684\u504f\u9817\u53ca\u7f3a\u5931\u3002\u6b64\u5916\uff0c\u82e5\u6709\u500b\u5225\u7684\u61c9\u7528\u6216\u898f\u683c\uff0c\u4ea6\u80fd\u8b93\u7814\u767c\u4eba\u54e1\u6309\u5176\u9700\u6c42 \u5c08\u6709\u540d\u8a5e(Nb) 13.3% 11.8% \u4e3b\u5e79 27 18.2 \u554f\u53e5 9.4 \u6700\u5f8c\uff0c\u6211\u5011\u767c\u73fe\u56e0\u70ba\u6578\u5b78\u6587\u5b57\u554f\u984c\u8a9e\u6599\u8207\u7528\u4f86\u767c\u5c55\u65b7\u8a5e\u5de5\u5177\u7684\u901a\u7528\u578b\u8a9e\u6599\u5dee\u7570\u751a \u7d44\u5408\u51fa\u60f3\u8981\u7684\u6aa2\u7d22\uff0c\u4fbf\u65bc\u5851\u9020\u4e0d\u540c\u7d44\u614b\u7684\u7279\u6b8a\u8a9e\u6599\u5eab\u3002 \u52d5\u4f5c\u53ca\u7269\u52d5\u8a5e(VC) 6.3% 8.7% 6.8 \u8a72\u65b7\u8a5e\u5de5\u5177\u6703\u4ee5\u6a19\u9ede\u7b26\u865f\u5c07\u984c\u76ee\u65b7\u958b\u6210\u6578\u500b\u53e5\u5b50\uff0c\u7d93\u904e\u5206\u6790\u5f8c\u767c\u73fe\uff1a\"\u4e3b\u5e79\"\u8207\"\u554f \u5730\u65b9\u8a5e(Nc) 5.5% 5.2% \u5176\u4ed6\u8a5e\u985e 19.8% 25.5% \u5927\uff0c\u800c\u5c0e\u81f4\u5728\u4eba\u540d(\u5982\"[\u5c0f][\u8070]\")\u53ca\u52d5\u8a5e\u7247\u8a9e(\u5982\"[\u62fc\u62fc][\u5716]\")\u5e38\u6709\u65b7\u8a5e\u932f\u8aa4\u7684\u60c5\u5f62\u3002 \u81f4\u8b1d \u56e0\u6b64\u5728\u89e3\u984c\u7cfb\u7d71\u6240\u7528\u7684\u65b7\u8a5e\u5de5\u5177\uff0c\u6709\u5fc5\u8981\u57f7\u884c\u9818\u57df\u8abf\u9069(Domain Adaptation)\u3002 \u52a9\u6a5f\u5668\u5b78\u7fd2\u9032\u884c\u984c\u578b\u5206\u985e\u3002\u5176\u4e2d 200 \u984c\u767c\u5c55\u96c6\u8a9e\u6599\u53ca 200 \u984c\u6e2c\u8a66\u8a9e\u6599\u70ba\u96a8\u6a5f\u62bd\u9078\uff1b\u800c\u70ba \u4e86\u6db5\u84cb\u6bcf\u4e00\u7a2e\u6578\u5b78\u984c\u578b\uff0c75 \u984c\u8a13\u7df4\u8a9e\u6599\u5247\u70ba\u4eba\u5de5\u5f9e\u8a13\u7df4\u96c6\u4e2d\u6bcf\u4e00\u985e\u984c\u578b\u62bd\u5e7e\u984c\u7d44\u6210\u3002 \u8868\u4e09\u3001\u5341\u516d\u7a2e\u984c\u578b\u5206\u4f48\u7d71\u8a08(\u55ae\u4f4d\uff1a\u767e\u5206\u6bd4) \u89e3\u984c\u985e\u578b \u8a13\u7df4\u96c6 \u767c\u5c55\u96c6 \u6e2c\u8a66\u96c6 5%)\u548c\u52a0 \u4e0d\u540c\u91cf\u8a5e\u55ae\u4f4d\u4e2d\u8f49\u63db\u7d50\u5408\u7684\u8a9e\u610f\uff0c\u5f97\u4ee5\u6b63\u78ba\u7684\u7406\u89e3\u6587\u5b57\u984c\u7684\u984c\u5e79\u3002\u8a9e\u6cd5\u7d50\u69cb\u6a19\u8a18\u53ca\u8a9e\u610f \u8868\u9054\u6a19\u8a18\uff0c\u662f\u5148\u7531\u7a0b\u5f0f\u505a\u731c\u6e2c\uff0c\u518d\u7531\u6a19\u8a3b\u4eba\u54e1(\u4ff1\u8a9e\u8a00\u5b78\u5c08\u696d\u80cc\u666f)\u505a\u6aa2\u67e5\u3001\u66f4\u6b63\u3002 \u53e5\"\u53e5\u9577\u70ba 5 \u81f3 8 \u500b\u8a5e\u7684\u53e5\u6578\u6bd4\u4f8b\uff0c\u5206\u5225\u4f54\u53e5\u5b50\u7e3d\u6578(79,822 \u53e5)\u7684 63%\u548c 70%\uff0c\u5176\u4e2d \"\u4e3b\u5e79\"\u4ee5 7 \u500b\u8a5e\u7684\u6bd4\u4f8b\u70ba\u6700\u591a(21.8%) \uff0c\"\u554f\u53e5\"\u4ee5 6 \u500b\u8a5e\u6578\u70ba\u6700\u591a(19.4%) (\u5982\u8868\u516d(a) \u5408\u8a08 100% 100% \u6700\u5f8c\uff0c\u8868\u516b\u986f\u793a\u6559\u80b2\u90e8\u80fd\u529b\u6307\u6a19 4 \u985e\u5225(\u539f\u59cb\u984c\u5eab\u6240\u63d0\u4f9b)\u7684\u5206\u4f48\u60c5\u5f62\u3002\u56e0\u70ba\u4e00\u500b \u672c\u7814\u7a76\u611f\u8b1d\u4e2d\u592e\u7814\u7a76\u9662\u8cc7\u8a0a\u79d1\u5b78\u7814\u7a76\u6240\u8a31\u805e\u5ec9\u6559\u6388\u4e3b\u6301\u4e4b\u667a\u6167\u578b\u4ee3\u7406\u4eba\u7cfb\u7d71\u5be6\u9a57 5. \u76f8\u95dc\u7814\u7a76 \u5ba4\u63d0\u4f9b\u4e26\u6388\u6b0a\u570b\u5c0f\u984c\u5eab\uff0c\u4ee5\u53ca\u611f\u8b1d\u79d1\u6280\u90e8\u300c\u5efa\u7acb\u4e00\u500b\u9818\u57df\u76f8\u95dc\u6a5f\u5668\u95b1\u8b80\u7cfb\u7d71\u4e4b\u63a2\u8a0e\u300d \u6cd5(14.0%)\u6bd4\u4f8b\u6700\u591a\uff0c\u4ee5\u5206\u6578(0.4%)\u8207\u96c6\u5408(0.4%)\u984c\u578b\u6bd4\u4f8b\u6700\u5c11\u3002 \u5c0d\u8a9e\u8a00\u5b78\u7684\u6a19\u8a3b 1 \uff0c\u4e3b\u8981\u5305\u542b\u8a9e\u6cd5\u7d50\u69cb\u6a19\u8a18\u53ca\u8a9e\u610f\u8868\u9054\u6a19\u8a18\u3002\u8a9e\u6cd5\u7d50\u69cb\u6a19\u8a18\u8207\u4e2d\u7814 \u6240\u793a) \u3002\u6b64\u5916\uff0c\u7531\u8868\u516d(b)\u53ef\u898b\uff0c\u4e0d\u8ad6\"\u4e3b\u5e79\"\u6216\"\u554f\u53e5\"\uff0c\u8a5e\u9577\u7686\u4ee5 2 \u500b\u5b57\u6240\u4f54\u7684\u6bd4\u4f8b\u70ba\u6700 \u984c\u76ee\u53ef\u80fd\u5305\u542b\u591a\u7a2e\u80fd\u529b\u6307\u6a19\uff0c\u56e0\u6b64\u5728\u984c\u5eab\u8cc7\u8a0a\u4e2d\u6709\u55ae\u4e00\u985e\u5225(\u4f54\u8a9e\u6599\u5eab 80.90%\u7684\u984c\u76ee) \u5728\u904e\u53bb\u8a9e\u6599\u5eab\u6a19\u8a3b\u65b9\u9762\uff0c\u4e0d\u4e4f\u8457\u91cd\u65bc\u7814\u7a76\u5256\u6790\u6a39[8, 15-18]\u3001\u8a9e\u610f\u89d2\u8272[9, 19-22]\u3001\u4f9d\u5b58\u7d50 (MOST104-2221-E-001-025)\u8a08\u5283\u7d93\u8cbb\u88dc\u52a9\u3002\u6b64\u5916\uff0c\u6211\u5011\u4e5f\u8981\u8b1d\u8b1d\u672c\u6240\u7684\u81ea\u7136\u8a9e\u8a00\u7406\u89e3\u5be6 3.4 \u76f8\u95dc\u7d71\u8a08\u8cc7\u6599 \u591a(50.1%\u8207 64.1%) \uff0c\u8207\u4e00\u822c\u8a9e\u6599\u6240\u898b\u76f8\u540c\u3002 \u8207\u7d44\u5408\u985e\u5225(\u7531\u55ae\u4e00\u985e\u5225\u505a\u7d44\u5408) (\u4f54\u8a9e\u6599\u5eab 19.10%\u7684\u984c\u76ee)\u4e4b\u5206\u3002 \u69cb[10, 23]\u3001\u7247\u8a9e\u7d50\u69cb[11]\uff0c\u6216\u8005\u662f\u8a9e\u7bc7[12]\u3001\u5171\u540c\u6307\u7a31\u95dc\u4fc2[13]\u548c\u8ff0\u8a9e\u53c3\u6578[13, 14]\u7b49\u7b49 \u9a57\u5ba4\u53ca\u8a5e\u5eab\u5c0f\u7d44\u5be6\u9a57\u5ba4\u5176\u4ed6\u53c3\u8207\u7814\u7a76\u548c\u5354\u540c\u6307\u5c0e\u4e4b\u8001\u5e2b\u8207\u76f8\u95dc\u540c\u4ec1\u3002\u6700\u5f8c\uff0c\u6211\u5011\u611f\u8b1d\u6240</td></tr><tr><td>9.\u4ee3\u6578 \u9662\u7684\u4e2d\u6587\u53e5\u7d50\u69cb\u6a39\u8cc7\u6599\u5eab 2 [26, 27]\u6a19\u8a18\u65b9\u5f0f\u4e00\u81f4\uff0c\u542b\u62ec\u8a5e\u985e\u3001\u8a9e\u6cd5\u7d50\u69cb\u548c\u8a9e\u610f\u89d2\u8272\u8a0a\u606f\u3002 3.45 2.8 2.2 1.\u5e7e\u4f55 18.39 6.9 4.8 \u5728\u524d\u4e00\u7bc0\u4e2d\uff0c\u6211\u5011\u5df2\u63d0\u5230\u6240\u6709\u7684\u984c\u76ee\u88ab\u5206\u6210\u516d\u500b\u5e74\u7d1a\uff0c\u4e26\u4e14\u4ee5\u4e82\u6578\u62bd\u53d6\u65b9\u5f0f\u5206\u6210\u4e09\u500b\u96c6 \u8868\u516d\u3001\u4e2d\u6587\u6578\u5b78\u8a9e\u6599\u5eab\u65b7\u8a5e\u7d71\u8a08\u8868 \u8868\u516b\u3001\u6559\u80b2\u90e8\u80fd\u529b\u6307\u6a19\u5206\u985e\u7d71\u8a08\u8868 \u65b9\u9762\uff0c\u537b\u7f3a\u4e4f\u91dd\u5c0d\u6578\u5b78\u6587\u5b57\u554f\u984c\u7684\u8a9e\u6599\u5eab\u3002 \u6709\u533f\u540d\u5be9\u7a3f\u6559\u6388\u7684\u5bf6\u8cb4\u610f\u898b\uff0c\u4ee5\u6539\u5584\u6b64\u8ad6\u6587\u95d5\u6f0f\u4e4b\u8655\u3002</td></tr><tr><td>10.\u52a0\u6cd5 11.\u55ae\u4f4d\u8f49\u63db 12.\u6700\u5c0f\u516c\u500d\u6578 2.30 2.30 2.30 13.\u5dee\u8ddd 1.15 14.\u6700\u5927\u516c\u56e0\u6578 1.15 15.\u96c6\u5408 1.15 16.\u5206\u6578 0 \u800c\u8a9e\u610f\u8868\u9054\u6a19\u8a18\u5247\u5206\u6210\u5169\u90e8\u5206\uff0c\u7b2c\u4e00\u90e8\u5206\u63a1\u7528\u4e2d\u7814\u9662\u53e5\u7d50\u69cb\u6a39\u7684\u8a9e\u7fa9\u89d2\u8272\u4ee5\u53ca\u76f8\u4f9d\u95dc 14.7 14.0 4.1 2.6 0 0.9 0.9 1.3 0.5 0.9 1.4 0.4 0.5 0.4 2.\u4e58\u6cd5 16.09 13.8 21.1 3.\u9664\u6cd5 16.09 18.8 19.3 4.\u7e3d\u5408 14.94 8.3 6.1 5.\u6e1b\u6cd5 8.05 20.6 17.5 6.\u6bd4\u8f03 4.60 2.8 3.5 7.\u9918\u6578 4.60 1.8 2.6 8.\u6bd4/\u6bd4\u4f8b 3.45 2.3 2.2 \u4fc2\uff0c\u4f46\u8f49\u63db\u6210\u5ee3\u7fa9\u77e5\u7db2 3 [28, 29]\u6240\u63a1\u7528\u7684\u7dda\u6027\u8a9e\u610f\u8868\u9054\u5f62\u5f0f\uff0c\u5167\u5bb9\u5305\u542b\u8a9e\u610f\u89d2\u8272\u3001\u8a5e\u5f59 \u8207\u8a5e\u5e8f\uff0c\u5c11\u6578\u4e2d\u5fc3\u8a9e\u7701\u7565\u6703\u5728\u9019\u968e\u6bb5\u88dc\u56de\u3002\u6b64\u5916\uff0c\u8868\u9054\u5f0f\u4e2d\u66f4\u9644\u52a0\u4e86\u5171\u540c\u6307\u7a31\u8a0a\u606f\uff0c\u5728 \u65b9\u62ec\u865f\u5167\u4ee5 x \u8207\u6578\u5b57\u6a19\u8a3b\u5169\u500b\u8a5e\u5f59\u6709\u76f8\u540c\u7684\u6307\u7a31\uff0c\u5982\u5716\u516d\u4e2d\u7b2c\u4e00\u53e5\u77ed\u8a9e\u548c\u7b2c\u4e09\u53e5\u77ed\u8a9e\u51fa \u73fe\u7684\u300c\u8c46\u5b50\u300d\u8207\u7b2c\u4e8c\u53e5\u77ed\u8a9e\u7684\u300c\u5176\u4e2d\u300d\u4e09\u500b\u8a5e\u5f59\u7686\u6307\u7a31\u76f8\u540c\u4e8b\u7269\uff0c\u56e0\u6b64\u4ee5[x1]\u5171\u540c\u6a19\u8a18 \u4e4b\u3002\u7b2c\u4e8c\u90e8\u5206\u5247\u662f\u5c07\u8a5e\u5f59\u7684\u8a9e\u610f\u9075\u7167\u5ee3\u7fa9\u77e5\u7db2\u7684\u8a5e\u5f59\u5b9a\u7fa9\u89e3\u6b67\u5f8c\uff0c\u4f9d\u5e8f\u5206\u5225\u5217\u65bc\u5176\u4e0b\uff1b \u82e5\u8a5e\u5f59\u70ba\u5ee3\u7fa9\u77e5\u7db2\u4e2d\u672a\u51fa\u73fe\u904e\u7684\u672a\u77e5\u8a5e\uff0c\u5247\u6703\u900f\u904e\u8a9e\u7fa9\u731c\u6e2c\u6a21\u7d44\u4f86\u7d66\u4e88\u9069\u7576\u5b9a\u7fa9\u5f0f\u3002\u5982 6 \u4ee5\u4e0a 3.2% 0.1% 8 11.6% 15.1% \u5c0f\u8a08 80.90% \u554f\u984c\u7684\u984c\u5eab\uff0c\u5728\u984c\u76ee\u62bd\u6a23\u4e0a\u7686\u6709\u6240\u504f\u9817\uff0c\u7121\u6cd5\u516c\u5e73\u6bd4\u8f03\u4e0d\u540c\u89e3\u984c\u7cfb\u7d71\u53ca\u53cd\u6620\u5404\u500b\u89e3\u984c\u7cfb \u984c\u76ee\u4e2d\u300c\u67cf\u582f\u300d\u53ef\u81ea\u52d5\u731c\u6e2c\u70ba\u4eba\u540d\uff0c\u4e26\u7d66\u4e88\u5b9a\u7fa9\u5f0f\uff1b\u6b64\u5916\uff0c\u5ee3\u7fa9\u77e5\u7db2\u7684\u8868\u9054\u512a\u52e2\u4e4b\u4e00\u662f \u5408\uff0c\u5176\u4e2d\u8a13\u7df4\u96c6\u6709 20,093 \u984c\uff0c\u767c\u5c55\u96c6\u548c\u6e2c\u8a66\u96c6\u90fd\u5404\u6709 1,700 \u984c(\u5305\u542b\u4e0a\u7bc0\u6240\u8ff0\u4e4b\u5df2\u6a19\u8a3b (a)\u53e5\u9577\u5206\u6790(#79,822 \u53e5) (b)\u8a5e\u9577\u5206\u6790(#608,732 \u8a5e) (a)\u55ae\u4e00\u6307\u6a19\u985e\u5225 (b)\u7d44\u5408\u6307\u6a19\u985e\u5225 \u8fd1\u5e74\u96d6\u7136\u5df2\u6709\u95dc\u65bc\u6578\u5b78\u6587\u5b57\u554f\u984c\u7814\u7a76\u7684\u984c\u5eab\u51fa\u73fe[2, 5-6, 24-25]\uff0c\u4f46\u90fd\u662f\u5f9e\u7db2\u7ad9\u8ad6\u58c7 \u53c3\u8003\u6587\u737b \u7684\u8a9e\u6599\u53ca\u5176\u4ed6\u672a\u6a19\u8a3b\u7684\u8a9e\u6599) \uff0c\u56e0\u6b64\u6211\u5011\u570b\u5c0f\u6578\u5b78\u8a9e\u6599\u5eab\u5408\u8a08\u6709 23,493 \u984c(\u5982\u8868\u56db\u6240 \u793a) \u3002\u5404\u5e74\u7d1a\u984c\u76ee\u6578\u91cf\u4ee5\u4e09\u5e74\u7d1a 5,210 \u984c\u70ba\u6700\u591a\uff0c\u5176\u9918\u4f9d\u5e8f\u70ba\u516d\u5e74\u7d1a 4,461 \u984c\u3001\u56db\u5e74\u7d1a 4,338 \u984c\u3001\u4e94\u5e74\u7d1a 4,062 \u984c\u3001\u4e8c\u5e74\u7d1a 3,642 \u984c\uff0c\u800c\u4ee5\u4e00\u5e74\u7d1a 1,780 \u984c\u70ba\u6700\u5c11\u3002\u8868\u4e94\u986f\u793a\u984c \u76ee\"\u4e3b\u5e79\"\u90e8\u5206\u7684\u5e73\u5747\u9577\u5ea6\u70ba 27 \u500b\u4e2d\u6587\u5b57\u3002\u4f7f\u7528\u4e2d\u7814\u9662\u4e2d\u6587\u8a5e\u5eab\u5c0f\u7d44\u7684\u65b7\u8a5e\u548c\u6a19\u8a5e\u6027\u5de5 5 5.3% 0.6% 7 21.8% 19.0% \u5c0f\u8a08 19.10% \u9023\u7d50 0.40% \u5ea6\u91cd\u758a\u7684\u8fad\u5f59\u8207\u516c\u5f0f\u3002\u63db\u53e5\u8a71\u8aaa\uff0c\u8a31\u591a\u984c\u76ee\u7686\u5927\u540c\u5c0f\u7570\u3002\u7e3d\u800c\u8a00\u4e4b\uff0c\u4e0a\u8ff0\u9019\u4e9b\u6578\u5b78\u6587\u5b57 \u5747\u9577\u5ea6\u5247\u70ba 9.4 \u500b\u4e2d\u6587\u5b57\uff0c\u4ee5\u53ca 6.8 \u500b\u4e2d\u6587\u8a5e\u3002 4 13.3% 3.0% 6 16.8% 19.4% \u6578\u8207\u91cf+\u4ee3\u6578+\u5e7e\u4f55 1.20% \u7d71\u8a08\u8207\u6a5f\u7387 0.05% \u984c\uff0cKoncel-Kedziorski \u7b49\u4eba[25]\u53ea\u6293\u53d6\u4ee3\u6578\u6587\u5b57\u76f8\u95dc\u7684\u554f\u984c(\u53d6\u6750\u81ea[2]) \uff0c\u4f46\u5176\u4e2d\u6709\u9ad8 \u5177\u52a0\u4ee5\u65b7\u8a5e\u5f8c\uff0c\u6bcf\u500b\u984c\u76ee\u5e73\u5747\u542b\u6709 18.2 \u500b\u4e2d\u6587\u8a5e\u3002\u5728\u53e6\u4e00\u65b9\u9762\uff0c\u984c\u76ee\"\u554f\u53e5\"\u90e8\u5206\u7684\u5e73 \u8a5e\u9577(\u5b57\u6578) \u4e3b\u5e79 \u554f\u53e5 1 7.7% 14.4% 2 50.1% 64.1% 3 20.4% 17.8% \u53e5\u9577(\u8a5e\u6578) \u4e3b\u5e79 \u554f\u53e5 1~3 1.0% 0.7% 4 4.9% 6.3% 5 12.7% 16.4% \u7d44\u5408\u985e\u5225 \u767e\u5206\u6bd4 \u6578\u8207\u91cf+\u4ee3\u6578 \u6578\u8207\u91cf+\u5e7e\u4f55 \u4ee3\u6578+\u5e7e\u4f55 0.21% \u5e7e\u4f55 1.68% \u7684\u554f\u984c\uff0c\u537b\u628a\u9700\u8981\u80cc\u666f\u77e5\u8b58\u7684\u6982\u6578\u984c\u76ee\u6392\u9664\u5728\u5916\uff0cShi \u7b49\u4eba[24]\u53ea\u6293\u53d6\u6578\u5b57\u904b\u7b97\u6587\u5b57\u554f 4.44% \u4ee3\u6578 6.72% \u6578\u5b57\u8cc7\u8a0a\u548c\u8fad\u5f59\u7a7a\u7f3a(Lexical Gap) ) \uff0cRoy \u7b49\u4eba[6]\u96d6\u7136\u589e\u52a0\u4e86\u5169\u500b\u4ee5\u4e0a\u904b\u7b97\u5b50 (\u591a\u6b65\u9a5f) 13.25% \u6293\u53d6\u7684\u7279\u5b9a\u6578\u64da\u8cc7\u6599\u7d44\u6210\u7684\uff1a\u5982 Kushman \u7b49\u4eba[5]\u522a\u6389\u4e86\u975e\u7dda\u6027\u4ee3\u6578\u554f\u984c\u7684\u984c\u76ee\uff0c \u55ae\u4e00\u985e\u5225 \u767e\u5206\u6bd4 \u6578\u8207\u91cf 72.04% Hosseini \u7b49\u4eba[2]\u53ea\u6293\u53d6\u7d44\u5408\u52a0\u6cd5\u3001\u6e1b\u6cd5\u3001\u4e00\u5143\u4e00\u6b21\u65b9\u7a0b\u548c\u7f8e\u5143\u6587\u5b57\u7b49\u554f\u984c(\u4f46\u5e36\u6709\u591a\u9918 [1] A.</td></tr><tr><td>9 10~12 14.8% 11.1% 9.3% 9.2% \u8868\u516b(a)\u986f\u793a\u6211\u5011\u7684\u4e2d\u6587\u6578\u5b78\u8a9e\u6599\u5eab\u5728\u55ae\u4e00\u80fd\u529b\u6307\u6a19\u65b9\u9762\uff0c\u4e3b\u8981\u4ee5\"\u6578\u8207\u91cf\"(72.04%)</td></tr><tr><td>13 \u4ee5\u4e0a 7.1% 2.8%</td></tr></table>", | |
| "html": null, | |
| "text": "\u516c\u65a4 \"\u7684 \u5b9a \u7fa9 \u5f0f \uff1a \"weight={ \u516c\u65a4 : quantity={2}}\" \u662f\u7531 \" \u516c\u65a4 \" \u7684 \u5b9a \u7fa9 \u5f0f \uff1a Mukherjee and U. Garain, \"A review of methods for automatic understanding of natural language mathematical problems,\" Artificial Intelligence Review, vol. 29, no. 2, pp. 93-122, Apr. 2008.[2] M. J. Hosseini, H. Hajishirzi, O. Etzioni, and N. Kushman, \"Learning to solve arithmetic word problems with verb Categorization,\" in Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP), 2014. [Online]. Available: http://ssli.ee.washington.edu/~hannaneh/algebra-emnlp14.pdf.", | |
| "num": null, | |
| "type_str": "table" | |
| } | |
| } | |
| } | |
| } |