| { |
| "paper_id": "O18-1027", |
| "header": { |
| "generated_with": "S2ORC 1.0.0", |
| "date_generated": "2023-01-19T08:09:48.845452Z" |
| }, |
| "title": "Energy Saving Knowledge Chatbot", |
| "authors": [ |
| { |
| "first": "Jhih-Jie", |
| "middle": [], |
| "last": "\u9673\u5fd7\u6770", |
| "suffix": "", |
| "affiliation": { |
| "laboratory": "", |
| "institution": "National Tsing Hua University", |
| "location": {} |
| }, |
| "email": "" |
| }, |
| { |
| "first": "", |
| "middle": [], |
| "last": "Chen", |
| "suffix": "", |
| "affiliation": { |
| "laboratory": "", |
| "institution": "\u5de5\u696d\u6280\u8853\u7814\u7a76\u9662 Industrial Technology Research Institute", |
| "location": {} |
| }, |
| "email": "" |
| }, |
| { |
| "first": "Shih-Ying", |
| "middle": [], |
| "last": "\u5f35\u4ed5\u7a4e", |
| "suffix": "", |
| "affiliation": {}, |
| "email": "" |
| }, |
| { |
| "first": "", |
| "middle": [], |
| "last": "Chang", |
| "suffix": "", |
| "affiliation": { |
| "laboratory": "", |
| "institution": "\u5de5\u696d\u6280\u8853\u7814\u7a76\u9662 Industrial Technology Research Institute", |
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| { |
| "first": "Tsu-Jin", |
| "middle": [], |
| "last": "\u90b1\u5b50\u6649", |
| "suffix": "", |
| "affiliation": {}, |
| "email": "" |
| }, |
| { |
| "first": "", |
| "middle": [], |
| "last": "Chiu", |
| "suffix": "", |
| "affiliation": { |
| "laboratory": "", |
| "institution": "National Tsing Hua University", |
| "location": {} |
| }, |
| "email": "" |
| }, |
| { |
| "first": "Ming-Chiao", |
| "middle": [], |
| "last": "\u8521\u540d\u55ac", |
| "suffix": "", |
| "affiliation": {}, |
| "email": "" |
| }, |
| { |
| "first": "", |
| "middle": [], |
| "last": "Tsai", |
| "suffix": "", |
| "affiliation": { |
| "laboratory": "", |
| "institution": "National Tsing Hua University", |
| "location": {} |
| }, |
| "email": "" |
| }, |
| { |
| "first": "Jason", |
| "middle": [ |
| "S" |
| ], |
| "last": "\u5f35\u4fca\u76db", |
| "suffix": "", |
| "affiliation": {}, |
| "email": "" |
| } |
| ], |
| "year": "", |
| "venue": null, |
| "identifiers": {}, |
| "abstract": "The lack of understanding of energy saving knowledge leads people to be slow in replacing their old, energy-inefficient household appliances, thus impeding the energy-saving promotion by the government. Recently, chatbot becomes more and more popular since conversation is a natural and effective interface for knowledge enquiry and information sharing. In this paper, we describe the development of an intelligent chatbot, which provides terminology defintions, energy saving tips, energy saving logo explanations, calculation of", |
| "pdf_parse": { |
| "paper_id": "O18-1027", |
| "_pdf_hash": "", |
| "abstract": [ |
| { |
| "text": "The lack of understanding of energy saving knowledge leads people to be slow in replacing their old, energy-inefficient household appliances, thus impeding the energy-saving promotion by the government. Recently, chatbot becomes more and more popular since conversation is a natural and effective interface for knowledge enquiry and information sharing. In this paper, we describe the development of an intelligent chatbot, which provides terminology defintions, energy saving tips, energy saving logo explanations, calculation of", |
| "cite_spans": [], |
| "ref_spans": [], |
| "eq_spans": [], |
| "section": "Abstract", |
| "sec_num": null |
| } |
| ], |
| "body_text": [ |
| { |
| "text": "power cost and so on. We hope the application will speed up the knowledge transfer and promote the energy saving in everyday life. The method involves collecting knowledge related to energy saving, labelling the collected questions into (question, answer) pairs for training and testing, and training a classifier-based chatbot using Support Vector Machine (SVM) for answering energy-saving related questions. We utilize existing chatbot development tool, Chatfuel, to develop a chatbot system. This prototype has great potential in drawing attention to energy-saving awareness and practice. ", |
| "cite_spans": [], |
| "ref_spans": [], |
| "eq_spans": [], |
| "section": "", |
| "sec_num": null |
| } |
| ], |
| "back_matter": [], |
| "bib_entries": { |
| "BIBREF0": { |
| "ref_id": "b0", |
| "title": "Extracting Chatbot Knowledge from Online Discussion Forums", |
| "authors": [ |
| { |
| "first": "Jizhou", |
| "middle": [], |
| "last": "Huang", |
| "suffix": "" |
| }, |
| { |
| "first": "Ming", |
| "middle": [], |
| "last": "Zhou", |
| "suffix": "" |
| }, |
| { |
| "first": "Dan", |
| "middle": [], |
| "last": "Yang", |
| "suffix": "" |
| } |
| ], |
| "year": 2007, |
| "venue": "IJCAI", |
| "volume": "7", |
| "issue": "", |
| "pages": "", |
| "other_ids": {}, |
| "num": null, |
| "urls": [], |
| "raw_text": "Huang, Jizhou, Ming Zhou, and Dan Yang. \"Extracting Chatbot Knowledge from Online Discussion Forums.\" IJCAI. Vol. 7. 2007.", |
| "links": null |
| }, |
| "BIBREF1": { |
| "ref_id": "b1", |
| "title": "Finding question-answer pairs from online forums", |
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| "last": "Cong", |
| "suffix": "" |
| } |
| ], |
| "year": 2008, |
| "venue": "Proceedings of the 31st annual international ACM SIGIR conference on Research and development in information retrieval", |
| "volume": "", |
| "issue": "", |
| "pages": "", |
| "other_ids": {}, |
| "num": null, |
| "urls": [], |
| "raw_text": "Cong, Gao, et al. \"Finding question-answer pairs from online forums.\" Proceedings of the 31st annual international ACM SIGIR conference on Research and development in information retrieval. ACM, 2008.", |
| "links": null |
| }, |
| "BIBREF2": { |
| "ref_id": "b2", |
| "title": "A classification-based approach to question answering in discussion boards", |
| "authors": [ |
| { |
| "first": "Liangjie", |
| "middle": [], |
| "last": "Hong", |
| "suffix": "" |
| }, |
| { |
| "first": "Brian", |
| "middle": [ |
| "D" |
| ], |
| "last": "Davison", |
| "suffix": "" |
| } |
| ], |
| "year": 2009, |
| "venue": "Proceedings of the 32nd international ACM SIGIR conference on Research and development in information retrieval", |
| "volume": "", |
| "issue": "", |
| "pages": "", |
| "other_ids": {}, |
| "num": null, |
| "urls": [], |
| "raw_text": "Hong, Liangjie, and Brian D. Davison. \"A classification-based approach to question answering in discussion boards.\" Proceedings of the 32nd international ACM SIGIR conference on Research and development in information retrieval. ACM, 2009.", |
| "links": null |
| }, |
| "BIBREF3": { |
| "ref_id": "b3", |
| "title": "Modeling semantic relevance for question-answer pairs in web social communities", |
| "authors": [ |
| { |
| "first": "Baoxun", |
| "middle": [], |
| "last": "Wang", |
| "suffix": "" |
| } |
| ], |
| "year": 2010, |
| "venue": "Proceedings of the 48th Annual Meeting of the Association for Computational Linguistics", |
| "volume": "", |
| "issue": "", |
| "pages": "", |
| "other_ids": {}, |
| "num": null, |
| "urls": [], |
| "raw_text": "Wang, Baoxun, et al. \"Modeling semantic relevance for question-answer pairs in web social communities.\" Proceedings of the 48th Annual Meeting of the Association for Computational Linguistics. Association for Computational Linguistics, 2010.", |
| "links": null |
| } |
| }, |
| "ref_entries": { |
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Tuning)</td></tr><tr><td>\u5982\uff1a\u80fd\u6e90\u5c40\u3001\u53f0\u7063\u96fb\u529b\u516c\u53f8\uff0c\u900f\u904e\u7db2\u8def\u722c\u87f2\u3001\u7db2\u9801\u89e3\u6790\u6280\u8853\uff0c\u5f9e\u9019\u4e9b\u7db2\u7ad9\u64f7\u53d6\u4ee5\u81ea\u7136\u8a9e \u804a\u5929\u6a5f\u5668\u4eba\uff0c\u900f\u904e\u5c0d\u8a71\u6216\u6587\u5b57\u9032\u884c\u4ea4\u8ac7\uff0c\u6a21\u64ec\u4eba\u985e\u5c0d\u8a71\uff0c\u662f\u6975\u4f73\u7684\u4e92\u52d5\u4ecb\u9762\u3002\u800c\u50b3\u7d71\u554f \u8a13\u7df4\u51fa\u6700\u4f73\u7684\u6a21\u578b\u4f86\u9032\u884c\u6bd4\u8f03\u3002</td></tr><tr><td>\u7b54\u6a5f\u5668\u4eba\u65e9\u5df2\u5ee3\u6cdb\u61c9\u7528\u5728\u96fb\u8a71\u5ba2\u670d\u4e0a\uff0c\u900f\u904e\u9810\u5148\u8a2d\u5b9a\u597d\u7684\u6307\u4ee4\u3001\u5c0d\u8a71\u6d41\u7a0b\uff0c\u5373\u53ef\u5b8c\u6210\u7c21 \u8a00\u69cb\u6210\u7684\u7528\u96fb\u77e5\u8b58\u8207\u554f\u7b54\u8cc7\u6599\u3002\u6b64\u5916\uff0c\u6211\u5011\u4e5f\u722c\u53d6\u7db2\u8def\u4e0a\u8207\u7bc0\u80fd\u76f8\u95dc\u7684\u554f\u7b54\u8cc7\u6599\uff0c\u5982\uff1a</td></tr><tr><td>\u55ae\u3001\u4f8b\u5e38\u7684\u7528\u6236\u670d\u52d9\uff0c\u5982\u8cc7\u8a0a\u67e5\u8a62\uff0c\u5e33\u6236\u8a2d\u5b9a\u7b49\u3002 \u5947\u6469\u77e5\u8b58\u5bb6\uff0c\u9019\u985e\u7db2\u7ad9\u6709\u8a31\u591a\u81ea\u7136\u8a9e\u8a00\u63cf\u8ff0\u7684\u554f\u984c\u8207\u89e3\u7b54\uff0c\u800c\u7522\u751f\u9019\u4e9b\u554f\u984c\u7684\u65cf\u7fa4\u4ea6\u76f8 \u5f9e\u8868\u4e2d\u4e8c\uff0c\u6211\u5011\u53ef\u4ee5\u770b\u5230\u8cc7\u6599\u6aa2\u7d22\u6a21\u578b\u5728\u6e2c\u8a66\u8cc7\u6599\u96c6\u6709 66.5% \u7684\u6e96\u78ba\u5ea6\uff0c\u800c SVM \u5206</td></tr><tr><td>\u4e00\u3001\u7dd2\u8ad6 \u7576\u8c50\u5bcc\u591a\u5143\uff0c\u53ef\u4ee5\u8aaa\u662f\u6700\u597d\u7684\u554f\u7b54\u6a5f\u5668\u4eba\u8a13\u7df4\u8cc7\u6599\u3002 \u985e\u6a21\u578b\u5247\u6709 74% \u7684\u6e96\u78ba\u5ea6\uff0c\u6211\u5011\u53ef\u4ee5\u770b\u5230 SVM \u5206\u985e\u6a21\u578b\u986f\u8457\u512a\u65bc\u8cc7\u6599\u6aa2\u7d22\u6a21\u578b\u3002\u800c \u5716\u4e94\u3001\u554f\u7b54\u7cfb\u7d71\u67b6\u69cb \u62dc\u8fd1\u5e74\u4eba\u5de5\u667a\u6167\u767c\u5c55\u8fc5\u901f\u6240\u8cdc\uff0c\u8fd1\u5e74\u5c0d\u8a71\u6a5f\u5668\u4eba\u6709\u4e86\u7a81\u7834\u6027\u7684\u767c\u5c55\uff0c\u81ea\u7136\u8a9e\u8a00\u8655\u7406\u7684\u6280 1\u3001\u5efa\u7acb\u95dc\u9375\u5b57\u96c6 \u91dd\u5c0d\u6a19\u8a18\u8cc7\u6599\u8f03\u5b8c\u6574\u7684\u51b7\u6c23\u5206\u985e\u9032\u884c\u5be6\u9a57\uff0c\u5171 1287 \u984c\u300123 \u985e\uff0c\u7d50\u679c\u6709 80% \u7684\u6e96\u78ba\u7387\u3002</td></tr><tr><td>\u53f0\u7063\u7528\u96fb\u5c16\u5cf0\u96c6\u4e2d\u5728\u590f\u5b63\uff0c\u5c24\u5176\u590f\u65e5\u767d\u5929\u5929\u6c23\u708e\u71b1\uff0c\u6bcf\u5929\u5c16\u5cf0\u6642\u523b\u7684\u4f9b\u96fb\u8ca0\u8f09\u5f80\u5f80\u8655\u65bc \u6eff\u8f09\u7684\u72c0\u614b\uff0c\u96fb\u529b\u516c\u53f8\u4e5f\u9762\u81e8\u4f9b\u96fb\u8abf\u5ea6\u7684\u6311\u6230\u3002\u5982\u4f55\u6709\u6548\u7684\u63a7\u5236\u3001\u5206\u6563\u5c16\u5cf0\u7528\u96fb\uff0c\u540c\u6642 \u6e1b\u5c11\u4f9b\u96fb\u8abf\u5ea6\u4e0a\u7684\u5371\u96aa\uff0c\u9032\u800c\u63d0\u5347\u4f9b\u96fb\u54c1\u8cea\u8207\u7a69\u5b9a\u5ea6\uff0c\u662f\u96fb\u529b\u516c\u53f8\u9700\u8981\u9762\u5c0d\u7684\u91cd\u8981\u8ab2\u984c\u3002 \u50b3\u7d71\u7bc0\u80fd\u77e5\u8b58\u7684\u50b3\u905e\uff0c\u90fd\u662f\u4ee5\u7db2\u7ad9\u6216\u662f\u7d19\u672c\u7684\u5ba3\u50b3\u7684\u65b9\u5f0f\uff0c\u4f86\u544a\u77e5\u7bc0\u80fd\u7684\u4e00\u4e9b\u6280\u5de7\uff0c\u4f46 \u662f\u9019\u7a2e\u77e5\u8b58\u50b3\u905e\u65b9\u5f0f\u6548\u679c\u6709\u9650\u3002\u5982\u5716\u4e00\u6240\u793a\uff0c\u76f4\u63a5\u5373\u6642\u7684\u56de\u994b\u6700\u6709\u7bc0\u80fd\u6548\u76ca\u3002\u672c\u8a08\u756b\u9078 \u64c7 \u4f7f\u7528 \u5373\u6642 \u6a5f\u5668 \u4eba( chatbot )\u7684 \u65b9\u5f0f \u4f86\u5efa\u69cb \u6211\u5011 \u7684\u6e9d \u901a\u4e92 \u52d5\u5e73 \u53f0\uff0c \u5982\uff1a Facebook messenger\uff0c\u642d\u914d\u667a\u6167\u96fb\u8868\u7684\u5373\u6642\u8cc7\u8a0a\uff0c\u53ef\u4ee5\u5728\u7528\u96fb\u5c16\u5cf0\u6216\u662f\u96fb\u50f9\u904e\u9ad8\u6642\uff0c\u5373\u6642\u7d66\u4e88\u7bc0\u80fd \u8853\u4e5f\u6709\u4e86\u9577\u8db3\u7684\u9032\u6b65\uff0c\u900f\u904e\u64f7\u53d6\u5c0d\u8a71\u4e2d\u7684\u95dc\u9375\u5b57\uff0c\u8fa8\u8b58\u4f7f\u7528\u8005\u7684\u76ee\u7684\uff0c\u5f9e\u8cc7\u6599\u5eab\u4e2d\u6aa2\u7d22 \u56db\u3001\u5be6\u9a57 \u641c\u5c0b\u8cc7\u6599\u9700\u8981\u641c\u5c0b\u95dc\u9375\u5b57\uff0c\u6240\u4ee5\u6211\u5011\u53c3\u8003\u4e86\u80fd\u6e90\u5c40\u7db2\u9801\u6240\u5217\u7684\u7bc0\u80fd\u6a19\u7ae0\u5bb6\u96fb\u6e05\u55ae\uff0c\u5efa\u7acb \u8868\u4e8c\u3001\u5404\u6a21\u578b\u554f\u984c\u6e2c\u8a66\u96c6\u4e0a\u7684\u5206\u985e\u6e96\u78ba\u5ea6 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\u516c\u53f8\u61c9\u7528\u5728\u624b\u6a5f\u4e0a\u7684\u8a9e\u97f3\u52a9\u7406 Siri\uff0c\u53ef\u900f\u904e\u8a9e\u97f3\u5b8c\u6210\u4e00\u7cfb\u5217\u65e5\u5e38\u7684\u4efb\u52d9\uff0c\u5982\uff1a\u8a2d\u5b9a\u884c\u4e8b\u66c6\u3001\u56de\u8986\u8a0a\u606f\u3001\u8a9e\u97f3\u5c0e\u822a\u2026\u7b49\uff0c \u751a\u81f3\u53ef\u4ee5\u548c\u4f7f\u7528\u8005\u9032\u884c\u804a\u5929\u3002Amazon \u7684\u667a\u6167\u5bb6\u5ead\u52a9\u7406 Alexa\uff0c\u6574\u5408\u5404\u5f0f\u5404\u6a23\u7684\u667a\u6167\u5bb6 \u96fb\uff0c\u53ef\u4ee5\u76e3\u63a7\u5bb6\u5ead\u4f7f\u7528\u60c5\u5f62\u3002\u6b64\u5916\uff0c\u73fe\u6709\u7684\u793e\u4ea4\u5e73\u53f0 Facebook Messenger\uff0c\u4e5f\u63a8\u51fa\u53ef\u5ba2 \u540d\u55ae\uff0c\u4f86\u904e\u6ffe\u96dc\u8a0a\u8cc7\u6599\u3002\u6700\u5f8c\u7684\u5bb6\u96fb\u95dc\u9375\u5b57\u96c6\u6709\u51b7\u6c23\u3001\u96fb\u6247\u3001\u9664\u6fd5\u3001\u51b0\u7bb1\u3001\u6d17\u8863\u3001\u71c8\u7ba1\u3001 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\u500b\u662f\u5bb6\u96fb\u5206\u985e\uff0c\u53e6\u4e00\u500b\u5247\u662f\u5c0d\u61c9\u7684\u56de\u7b54\u3002\u6211\u5011\u5c07\u9019\u4efd\u8cc7\u6599\u5207\u6210\u8a13\u7df4\u3001\u6e2c\u8a66\u96c6\uff0c\u6bd4\u4f8b\u70ba\u56db (\u4e09) \u3001\u554f\u984c\u56de\u7b54\u4eba\u5de5\u6a19\u6ce8 \u70ba\u4e86\u6709\u6548\u5730\u78ba\u4fdd\u7b54\u6848\u7684\u54c1\u8cea\u8207\u53ef\u9760\u5ea6\uff0c\u6211\u5011\u9810\u5148\u8a2d\u5b9a\u597d\u7684\u56de\u7b54\u7684\u96c6\u5408\uff0c\u4e26\u5c07\u6bcf\u500b\u554f\u984c\u5206 \u5716\u56db\u3001\u5206\u985e\u7cfb\u7d71\u67b6\u69cb\u5716 SVM (\u9810\u8a2d\u53c3\u6578) 73.1% 78.3% \u6bd4\u4e00\uff0c\u6211\u5011\u4f7f\u7528\u9019 3,477 \u7b46\u8cc7\u6599\u9032\u884c\u4e0d\u540c\u6a21\u578b\u7684\u5be6\u9a57\u8207\u8a55\u4f30\u3002 \u5716\u4e09\u3001\u9032\u884c\u554f\u984c\u5206\u985e\u6a19\u8a18\u7684\u554f\u7b54\u7ba1\u7406\u7cfb\u7d71 \u6247\u3001\u5ef6\u9577\u7dda\u3001\u63d2\u5ea7\u3002\u7bc0\u80fd\u95dc\u9375\u5b57\u96c6\u5247\u6709\u7701\u96fb\uff0c\u96fb\u8cbb\uff0c\u7bc0\u7701\uff0c\u74b0\u4fdd\uff0c\u7bc0\u80fd\uff0c\u96fb\u50f9\uff0c\u8017\u96fb\u3002 2\u3001\u641c\u5c0b\u5f15\u64ce\u6536\u96c6\u6587\u4ef6 \u985e\u5230\u9019\u4e9b\u8a2d\u5b9a\u597d\u7684\u56de\u7b54\u96c6\u5408\u3002\u63a5\u4e0b\u4f86\uff0c\u6211\u5011\u9010\u9805\u6aa2\u8996\u554f\u984c\u4e26\u4eba\u5de5\u7522\u751f\u7b54\u6848\u5206\u985e\u6216\u6b78\u985e\u5230 (\u56db) \u3001\u5efa\u7acb\u81ea\u52d5\u5206\u985e\u6a21\u578b (\u4e8c) \u3001\u5be6\u9a57\u6a21\u578b\u8207\u7d50\u679c \u6211\u5011\u7684\u554f\u7b54\u7cfb\u7d71\u6574\u9ad4\u67b6\u69cb\u5982\u5716\u4e94\u6240\u793a\uff0c\u900f\u904e\u722c\u87f2\u7a0b\u5f0f\u5b9a\u671f\u6536\u96c6\u5947\u6469\u77e5\u8b58\u5bb6\u7684\u554f\u7b54\u8cc7\u6599\uff0c SVM (\u53c3\u6578\u8abf\u6574\u5f8c) 74% 80.7%</td></tr><tr><td>\u5efa\u8b70\uff0c\u9019\u6a23\u7bc0\u80fd\u5efa\u8b70\u88ab\u63a1\u7528\u7684\u6a5f\u7387\u5c31\u6703\u63d0\u5347\u3002 \u88fd\u5316\u7684\u5c0d\u8a71\u6a5f\u5668\u4eba\u6a21\u7d44\uff0c\u7d50\u5408\u5c0d\u8a71\u7ba1\u7406\u8207\u81ea\u7136\u8a9e\u8a00\u8655\u7406\u6280\u8853\uff0c\u5e6b\u52a9\u56de\u7b54\u554f\u984c\uff0c\u4e26\u53ef\u63a8\u5ee3 \u5404\u5f0f\u4fc3\u92b7\u6d3b\u52d5\u3002\u8207\u6a5f\u5668\u5c0d\u8a71\u4e0d\u518d\u662f\u6b7b\u677f\u7684\u904e\u7a0b\uff0c\u800c\u662f\u5bcc\u555f\u767c\u6027\u4e14\u5145\u6eff\u6a02\u8da3\u7684\u9ad4\u9a57\u3002 \u6211\u5011\u4f7f\u7528\u722c\u87f2\u6293\u53d6 Bing \u641c\u5c0b\u5f15\u64ce\u7684\u641c\u5c0b\u7d50\u679c\uff0c\u4e26\u9650\u5236\u641c\u5c0b\u7bc4\u570d\u5728\u5947\u6469\u77e5\u8b58\u5bb6\u7684\u7db2\u57df\u7bc4 \u570d(tw.answers.yahoo.com) \u3002\u56e0\u5947\u6469\u77e5\u8b58\u5bb6\u5167\u5efa\u7684\u641c\u5c0b\u5f15\u64ce\uff0c\u5728\u8d85\u904e\u7279\u5b9a\u9801\u6578\u5f8c\u7684\u7d50\u679c \u4f5c\u70ba\u6211\u5011\u7684\u7bc0\u80fd\u77e5\u8b58\u5eab\u7684\u57fa\u790e\uff0c\u800c\u6211\u5011\u7684\u6a19\u8a3b\u7cfb\u7d71\u8b93\u6a19\u8a3b\u8005\u53ef\u4ee5\u5c0d\u7bc0\u80fd\u77e5\u8b58\u5eab\u7684\u554f\u984c\u9032 \u73fe\u6709\u5206\u985e\uff0c\u76f4\u5230\u6709\u4e00\u5b9a\u6578\u91cf\u7684\u6a19\u8a18\u8cc7\u6599\u5f8c\uff0c\u6211\u5011\u5c07\u5df2\u6a19\u6ce8\u7684\u8cc7\u6599\u532f\u5165\u81ea\u52d5\u5206\u985e\u6a21\u578b\uff0c\u9019 \u500b\u5206\u985e\u6a21\u578b\u53ef\u4ee5\u5b78\u7fd2\u6a19\u8a18\u8cc7\u6599\u4e2d\u7684\u5206\u985e\u6a21\u5f0f\uff0c\u4e26\u81ea\u52d5\u5206\u985e\u5269\u9918\u7684\u672a\u5206\u985e\u8cc7\u6599\uff0c\u8f14\u52a9\u4e26\u52a0 1\u3001\u8cc7\u8a0a\u6aa2\u7d22\u6a21\u578b \u884c\u56de\u7b54\u6a19\u8a18\uff0c\u4e26\u4f5c\u70ba\u8a13\u7df4\u81ea\u52d5\u5206\u985e\u6a21\u578b\u7684\u8cc7\u6599\uff0c\u6700\u5f8c\uff0c\u4e32\u63a5\u5c0d\u8a71\u7ba1\u7406\u5e73\u53f0\u8207\u5373\u6642\u901a\u8a0a\u5e73 1\u3001\u5bb6\u96fb\u5206\u985e \u4e94\u3001\u7d50\u8ad6</td></tr><tr><td>\u672c\u8ad6\u6587\u671f\u671b\u4ee5\u5c0d\u8a71\u6a5f\u5668\u4eba\u7684\u6280\u8853\uff0c\u958b\u767c\u7bc0\u96fb\u554f\u7b54\u6a5f\u5668\u4eba\u3002\u900f\u904e\u81ea\u7136\u8a9e\u8a00\u8655\u7406\u6280\u8853\uff0c\u9054\u5230 \u90fd\u7121\u6cd5\u53d6\u5f97\uff0c\u6211\u5011\u8f49\u800c\u6e2c\u8a66\u722c\u53d6\u5176\u4ed6\u641c\u5c0b\u5f15\u64ce(Google\u3001Bing)\u7684\u641c\u5c0b\u7d50\u679c\uff0c\u6700\u5f8c\u9078\u64c7 \u6211\u5011\u4ee5 TF-IDF \u6a21\u578b(\u4e00\u7a2e\u8a5e\u888b\u6a21\u578b)\u70ba\u57fa\u790e\uff0c\u5c07\u554f\u984c\u7684\u6587\u5b57\u8f49\u63db\u70ba\u4e00\u56fa\u5b9a\u9577\u5ea6\u7684\u5411\u91cf\uff0c \u53f0\uff0c\u4f86\u8207\u4f7f\u7528\u8005\u9032\u884c\u7bc0\u80fd\u77e5\u8b58\u554f\u7b54\u8207\u50b3\u905e\u3002 \u5206\u985e\u554f\u984c\u6240\u5c6c\u7684\u5bb6\u96fb\u985e\u5225\uff0c\u6211\u5011\u4f7f\u7528\u95dc\u9375\u5b57\u6a21\u578b\u4f5c\u70ba\u57fa\u7dda (baseline) \uff0c\u4f86\u6bd4\u8f03\u6211\u5011\u7684 SVM \u901f\u5f8c\u7e8c\u7684\u4eba\u5de5\u6a19\u8a18\u904e\u7a0b\uff0c\u6700\u5f8c\uff0c\u642d\u914d\u958b\u767c\u597d\u7684\u6a19\u8a18\u7cfb\u7d71\uff0c\u4eba\u5de5\u9032\u884c\u6700\u7d42\u7684\u554f\u984c\u5206\u985e\u78ba\u8a8d\u3002 \u672c\u7bc7\u8ad6\u6587\u5efa\u7acb\u4e86\u4e00\u500b\u554f\u7b54\u6a5f\u5668\u4eba\u6846\u67b6\u5be6\u8e10\uff0c\u5f9e\u8cc7\u6599\u6536\u96c6\u3001\u4eba\u5de5\u6a19\u8a18\u3001\u5efa\u7acb\u5c0d\u8a71\u6d41\u7a0b\u3001\u8a13</td></tr><tr><td>\u5c0d\u8a71\u7684\u95dc\u9375\u5b57\u64f7\u53d6\u8207\u8a9e\u610f\u7406\u89e3\uff0c\u4e26\u7d50\u5408\u7528\u96fb\u3001\u7bc0\u96fb\u3001\u653f\u7b56\u5ba3\u5c0e\u76f8\u95dc\u8cc7\u8a0a\uff0c\u642d\u914d\u81ea\u7136\u8a9e\u8a00 \u53ef\u4ee5\u900f\u904e\u722c\u87f2\u4e26\u4e14\u53d6\u5f97\u5b8c\u6574\u8cc7\u6599 Bing \u641c\u5c0b\u5f15\u64ce\u3002 1\u3001\u4eba\u5de5\u5efa\u7acb\u9810\u8a2d\u56de\u7b54\u96c6 \u85c9\u7531\u8a08\u7b97\u5411\u91cf\u4e4b\u9593\u7684\u76f8\u4f3c\u5ea6(Cosine Similarity) \uff0c\u53ef\u4ee5\u5f97\u5230\u554f\u984c\u8207\u554f\u984c\u4e4b\u9593\u7684\u76f8\u4f3c\u5ea6\u3002 \u5206\u985e\u6a21\u578b\u3002\u95dc\u9375\u5b57\u6a21\u578b\u5c07 16 \u985e\u5bb6\u96fb\u540d\u7a31\u4f5c\u70ba\u95dc\u9375\u5b57\uff0c\u4ee5\u554f\u984c\u6587\u5b57\u4e2d\u6700\u5e38\u898b\u7684\u95dc\u9375\u5b57\u4f5c 3\u3001\u4e8c\u968e\u5206\u985e \u7df4\u81ea\u52d5\u56de\u7b54\u5206\u985e\u6a21\u578b\uff0c\u4e26\u63d0\u51fa\u4e00\u500b\u81ea\u52d5\u5206\u985e\u6a21\u578b\u8f14\u52a9\u4eba\u5de5\u6a19\u8a18\u7684\u5faa\u74b0\u3002\u5be6\u9a57\u7d50\u679c\u986f\u793a\u6211</td></tr><tr><td>\u751f\u6210\u6280\u8853\uff0c\u7522\u751f\u63a5\u8fd1\u81ea\u7136\u8a9e\u8a00\u7684\u56de\u8986\u3002\u4e5f\u671f\u8a31\u80fd\u63d0\u5347\u570b\u4eba\u5c0d\u65bc\u7528\u96fb\u76f8\u95dc\u8b70\u984c\u7684\u95dc\u6ce8\u3002 3\u3001\u8cc7\u6599\u89e3\u6790\u8207\u5132\u5b58 \u6839\u64da\u4e0d\u540c\u95dc\u9375\u5b57\u7d44\u5408\u7684\u641c\u5c0b\u7d50\u679c\uff0c\u6211\u5011\u5148\u5c07\u554f\u984c\u5206\u6210\u4e86 16 \u500b\u5927\u985e\uff0c\u63a5\u4e0b\u4f86\uff0c\u4fbf\u4eba\u5de5\u641c \u6211\u5011\u5728\u5df2\u6a19\u6ce8\u7684\u554f\u984c\u8cc7\u6599\u96c6\u4e0a\u5efa\u7acb TF-IDF \u6a21\u578b\u5f8c\uff0c\u7576\u4f7f\u7528\u8005\u8a62\u554f\u4e00\u500b\u65b0\u7684\u554f\u984c\uff0c\u6211 \u70ba\u5206\u985e\u3002\u5f9e\u8868\u4e00\u4e2d\uff0c\u6211\u5011\u53ef\u4ee5\u770b\u5230\u95dc\u9375\u5b57\u6a21\u578b\u6709 72.1% \u7684\u6e96\u78ba\u7387\uff0c\u800c\u6211\u5011\u7684 SVM \u6a21 \u4e0d\u540c\u5bb6\u96fb\u6703\u6709\u985e\u4f3c\u7684\u554f\u984c\uff0c\u958b\u767c\u5206\u985e\u5668\u7684\u904e\u7a0b\u4e2d\u6211\u5011\u767c\u73fe\uff0c\u5206\u985e\u5668\u6709\u6642\u6210\u529f\u8fa8\u8a8d\u554f\u984c\u7684 \u5011\u7684\u7cfb\u7d71\u6709\u4e0d\u932f\u7684\u6e96\u78ba\u5ea6\uff0c\u53e6\u5916\uff0c\u6211\u5011\u9080\u8acb\u7bc0\u80fd\u5c08\u5bb6\u4f86\u8a66\u7528\uff0c\u4e26\u5c0d\u554f\u7b54\u6a5f\u5668\u4eba\u6709\u6b63\u9762\u7684</td></tr><tr><td>\u8853\uff0c\u5c07\u5176\u4e2d\u7684\u7db2\u5740\u3001\u65e5\u671f\u3001\u6a19\u984c\u3001\u5167\u5bb9\u8207\u6700\u4f73\u56de\u7b54\u64f7\u53d6\u51fa\u4f86\uff0c\u4e26\u4f9d\u4e0d\u540c\u5bb6\u96fb\u985e\u578b\u5404\u81ea\u5b58 \u70ba\u4f8b\uff0c\u6709 23 \u9805\u9810\u8a2d\u7684\u56de\u7b54) \u3002 \u8868\u4e00\u3001\u5bb6\u96fb\u5206\u985e\u6e96\u78ba\u5ea6 \u554f\u984c\uff0c\u6211\u5011\u7279\u5225\u8a2d\u8a08\u4e86\u5169\u968e\u6bb5\u5206\u985e\uff0c\u5148\u5206\u985e\u5bb6\u96fb\uff0c\u518d\u5206\u985e\u554f\u984c\u7684\u985e\u5225\uff0c\u9032\u800c\u9078\u51fa\u76f8\u5c0d\u61c9 \u8005\u554f\u984c\u7684\u56de\u7b54\u3002\u9019\u6a23\u7684\u65b9\u6cd5\u4e0d\u50c5\u76f4\u89ba\u4e14\u5bb9\u6613\u5be6\u4f5c\uff0c\u5206\u985e\u7684\u6548\u679c\u4e5f\u76f8\u7576\u4e0d\u932f\u3002\u6b64\u5916\uff0c\u900f\u904e \u4e8c\u3001\u76f8\u95dc\u7814\u7a76 \u6700\u5f8c\uff0c\u6211\u5011\u81ea Bing \u641c\u5c0b\u5f15\u64ce\u53d6\u5f97\u5947\u6469\u77e5\u8b58\u5bb6 28,533 \u7b46\u554f\u7b54\u6587\u4ef6\uff0c\u4e26\u900f\u904e\u7db2\u9801\u89e3\u6790\u6280 \u7d22\u4e26\u53c3\u8003\u76f8\u95dc\u7684\u7bc0\u80fd\u77e5\u8b58\u8207\u5bb6\u96fb\u63a1\u8cfc\u6280\u5de7\uff0c\u70ba\u5404\u985e\u578b\u7684\u5bb6\u96fb\u554f\u984c\u5efa\u7acb\u56de\u7b54\u5167\u5bb9(\u4ee5\u51b7\u6c23 \u5011\u4fbf\u53ef\u4ee5\u5f9e\u73fe\u6709\u8cc7\u6599\u96c6\u4e2d\u627e\u51fa\u6700\u76f8\u4f3c\u7684\u554f\u984c\uff0c\u4e26\u4ee5\u8a72\u554f\u984c\u6240\u6a19\u8a3b\u7684\u6a19\u6e96\u56de\u7b54\uff0c\u4f5c\u70ba\u4f7f\u7528 \u578b\u5247\u6709 92% \u7684\u6e96\u78ba\u5ea6\uff0c\u986f\u8457\u512a\u65bc\u95dc\u9375\u5b57\u6a21\u578b\u3002 \u610f\u5716(\u8cfc\u8cb7\u5bb6\u96fb\u3001\u5bb6\u96fb\u6bd4\u8f03\u3001\u540d\u8a5e\u91cb\u7fa9\u2026\u7b49) \uff0c\u4f46\u5bb6\u96fb\u7684\u985e\u578b\u537b\u932f\u8aa4\u4e86\u3002\u70ba\u4e86\u89e3\u6c7a\u9019\u500b \u8a55\u50f9\uff0c\u5373\u4f7f\u5076\u6709\u56de\u7b54\u7121\u6cd5\u5b8c\u6574\u56de\u61c9\u554f\u984c\u7684\u60c5\u5f62\uff0c\u4f46\u9084\u662f\u80fd\u9054\u5230\u76f8\u95dc\u7bc0\u80fd\u77e5\u8b58\u50b3\u905e\u7684\u4f5c\u7528\u3002</td></tr><tr><td>[1]\u5c07\u7db2\u8def\u8ad6\u58c7\u4e0a\u7684(\u554f\u984c\uff0c\u56de\u7b54)\u7684\u914d\u5c0d\u8cc7\u6599\uff0c\u505a\u70ba SVM \u6a21\u578b\u7684\u8a13\u7df4\u8cc7\u6599\uff0c\u4f86\u5efa\u7acb\u804a \u6210 json \u683c\u5f0f\u7684\u6a94\u6848\uff0c\u505a\u70ba\u5f8c\u7e8c\u8cc7\u6599\u5206\u6790\u8207\u6a21\u578b\u8a13\u7df4\u4f7f\u7528\u3002 2\u3001\u81ea\u52d5\u5206\u985e\u6a21\u578b\u8f14\u52a9\u9810\u6a19 \u8a2d\u5b9a\u76f8\u4f3c\u5ea6\u7684\u9580\u6abb\u503c\uff0c\u6211\u5011\u53ef\u4ee5\u81ea\u52d5\u628a\u76f8\u4f3c\u5ea6\u904e\u4f4e\u7684\u554f\u984c\u6b78\u985e\u70ba\u5f85\u8655\u7406\uff0c\u518d\u9032\u884c\u5f8c\u7e8c\u7684 \u7684\u56de\u7b54\u3002\u554f\u984c\u5206\u985e\u91dd\u5c0d\u4e0d\u540c\u5bb6\u96fb\u5206\u5225\u8a13\u7df4\uff0c\u5171\u6709 16 \u500b\u554f\u984c\u8a9e\u610f\u5206\u985e\u5668\u3002 \u672a\u4f86\uff0c\u6211\u5011\u5c07\u6703\u6301\u7e8c\u6536\u96c6\u8207\u6a19\u8a18\u8cc7\u6599\uff0c\u4e26\u5b8c\u5584\u66f4\u591a\u5bb6\u96fb\u7684\u554f\u984c\u6a19\u8a18\u3002\u521d\u6b65\u9080\u8acb\u5be6\u9a57\u986f\u793a\uff0c</td></tr><tr><td>\u5929\u6a5f\u5668\u4eba\u3002[2]\u6307\u51fa\u4e86\u4f7f\u7528\u7db2\u8def\u8ad6\u58c7\u4e0a\u7684\u554f\u7b54\u8cc7\u6599\u4f5c\u70ba\u8a13\u7df4\u8cc7\u6599\u6703\u6709\u7684\u56f0\u5883\u8207\u6311\u6230\uff0c\u4e26 (\u4e8c) \u3001\u5efa\u7acb\u5c0d\u8a71\u6d41\u7a0b \u4eba\u5de5\u6a19\u8a18\u662f\u975e\u5e38\u8017\u6642\u3001\u4eba\u529b\u5bc6\u96c6\u7684\u5de5\u4f5c\uff0c\u70ba\u4e86\u52a0\u901f\u6574\u9ad4\u6a19\u8a18\u904e\u7a0b\uff0c\u7576\u6bcf\u500b\u5bb6\u96fb\u5206\u985e\u6709\u4e00 \u4eba\u5de5\u6a19\u6ce8\u3002 \u5bb6\u96fb\u5206\u985e\u7684\u6a21\u578b\u540c\u6a23\u63a1\u7528\u4e86\u4e0a\u8ff0\u7684\u8cc7\u6599\u6aa2\u7d22\u6a21\u578b\u8207 SVM \u5206\u985e\u6a21\u578b\uff0c\u4e26\u4f7f\u7528 TFIDF \u4f5c\u70ba \u6a21\u578b \u5bb6\u96fb\u5206\u985e\u6e96\u78ba\u5ea6 \u6574\u9ad4\u56de\u7b54\u7684\u6eff\u610f\u5ea6\u5f88\u9ad8\uff0c\u4f46\u8a31\u591a\u60c5\u6cc1\u662f\u4f7f\u7528\u8005\u4e0d\u77e5\u9053\u8a72\u554f\u4ec0\u9ebc\u554f\u984c\uff0c\u6216\u662f\u5982\u4f55\u53bb\u554f\u554f</td></tr><tr><td>\u63d0\u51fa\u4e86\u4e00\u500b\u65b9\u6cd5\uff0c\u53ef\u4ee5\u5728\u7db2\u8def\u554f\u7b54\u8ad6\u58c7\u6709\u6548\u5730\u53d6\u5f97\u6b63\u78ba\u7684\u554f\u7b54\u8cc7\u6599\u3002[3]\u5247\u63d0\u5230\uff0c\u53ef\u4ee5 \u63a5\u4e0b\u4f86\uff0c\u6211\u5011\u5229\u7528 Chatfuel \u804a\u5929\u6a5f\u5668\u4eba\u5e73\u53f0\u5efa\u7f6e\u6211\u5011\u7684\u5c0d\u8a71\u6d41\u7a0b\uff0cChatfuel \u53ef\u4ee5\u9810\u5148\u8a2d \u5b9a\u6578\u91cf\u7684\u6a19\u8a18\u8cc7\u6599\u5f8c\uff0c\u6211\u5011\u4fbf\u4f7f\u7528\u5df2\u6a19\u8a18\u7684\u8cc7\u6599\u8a13\u7df4\u81ea\u52d5\u5206\u985e\u6a21\u578b(\u7b2c\u56db\u5c0f\u7bc0) \uff0c\u4e26\u4f7f \u7136\u800c\uff0c\u6bd4\u8f03\u76f8\u4f3c\u5ea6\u7684\u6b65\u9a5f\u9700\u8981\u6bd4\u8f03\u6a19\u6ce8\u8cc7\u6599\u96c6\u4e2d\u7684\u6bcf\u4e00\u500b\u554f\u984c\uff0c\u4e00\u65e6\u6a19\u6ce8\u8cc7\u6599\u96c6\u6578\u91cf\u589e \u6bcf\u500b\u554f\u984c\u7684\u7279\u5fb5\u503c\uff0c\u5206\u985e\u5171\u6709 16 \u985e\u5bb6\u96fb\u3002\u5206\u5b8c\u5bb6\u96fb\u5f8c\uff0c\u518d\u6839\u64da\u4e0d\u540c\u985e\u5225\u9078\u7528\u4e0d\u540c\u7684\u554f \u95dc\u9375\u5b57\u6a21\u578b 72.1% \u984c\u3002\u6211\u5011\u4e5f\u5c07\u6703\u9032\u884c\u4f7f\u7528\u8005\u7814\u7a76\uff0c\u4e3b\u52d5\u89f8\u53ca\u4f7f\u7528\u8005\uff0c\u800c\u975e\u88ab\u52d5\u7684\u63a5\u53d7\u554f\u984c\uff0c\u4e5f\u63a2\u8a0e\u5927\u773e</td></tr><tr><td>\u5716\u4e00\uff0c\u5bb6\u5ead\u7bc0\u80fd\u56de\u994b\u65b9\u5f0f\u7684\u6548\u76ca\u6bd4\u8f03 \u4f7f\u7528\u4e00\u5957\u56de\u8986\u6392\u5e8f\u65b9\u6cd5\u4f86\u6539\u9032\u56de\u7b54\u7cfb\u7d71\u7684\u54c1\u8cea\u3002[4]\u4f7f\u7528\u8a9e\u610f\u76f8\u4f3c\u5ea6\u5f9e\u73fe\u6709\u7684(\u554f\u984c\uff0c 3\u3001\u4eba\u5de5\u78ba\u8a8d\u9810\u6a19\u8cc7\u6599 \u986f\u793a\u4e8c\u968e\u6bb5\u5206\u985e\u7684\u505a\u6cd5\u80fd\u5920\u6709\u6548\u7684\u63d0\u5347\u56de\u7b54\u7684\u6e96\u78ba\u5ea6\u3002 \u7b54\u6848)\u914d\u5c0d\u4e2d\u627e\u51fa\u6700\u4f73\u89e3\u7b54\u3002 \u5b9a\u5c0d\u8a71\u7684\u5167\u5bb9\uff0c\u4e26\u4e14\u53ef\u4ee5\u6839\u64da\u4e0d\u540c\u7684\u56de\u7b54\uff0c\u8a2d\u8a08\u4e0d\u540c\u7684\u5c0d\u8a71\u904e\u7a0b\uff0c\u6b64\u5916\uff0c\u4e5f\u53ef\u4ee5\u5728\u5c0d\u8a71 \u7528\u8a13\u7df4\u597d\u7684\u6a21\u578b\u9810\u6e2c\u672a\u6a19\u8a18\u7684\u8cc7\u6599\uff0c\u4f5c\u70ba\u8a72\u554f\u984c\u7684\u9810\u8a2d\u5206\u985e\u3002 \u52a0\uff0c\u5206\u985e\u7684\u901f\u5ea6\u5c31\u6703\u5448\u7dda\u6027\u7684\u4e0b\u964d\uff0c\u9019\u6a23\u7684\u6a21\u578b\u5728\u53ef\u64f4\u5c55\u6027(scalability)\u4e0a\u548c\u6548\u7387\u4e0a\uff0c SVM \u6a21\u578b 92% \u984c\u5206\u985e\u5668\uff0c\u5982\u6b64\u4e00\u4f86\uff0c\u5206\u985e\u6a21\u578b\u7684\u985e\u5225\u6578\u91cf\u53ef\u4ee5\u5927\u5e45\u6e1b\u5c11\uff0c\u521d\u6b65\u5be6\u9a57(\u7b2c\u56db\u7bc0)\u7d50\u679c\u4e5f \u5c0d\u65bc\u7bc0\u80fd\u6240\u95dc\u6ce8\u7684\u8b70\u984c\uff0c\u9032\u884c\u63a8\u85a6\u554f\u984c\u7cfb\u7d71\u7684\u5efa\u7f6e\uff0c\u9032\u4e00\u6b65\u6539\u5584\u804a\u5929\u904e\u7a0b\u3002</td></tr></table>", |
| "type_str": "table", |
| "num": null, |
| "text": "Keywords: Chatbot, SVM, Web Crawler." |
| } |
| } |
| } |
| } |
