| { |
| "paper_id": "O15-3004", |
| "header": { |
| "generated_with": "S2ORC 1.0.0", |
| "date_generated": "2023-01-19T08:10:10.005365Z" |
| }, |
| "title": "Extractive Spoken Document Summarization with Representation Learning Techniques", |
| "authors": [ |
| { |
| "first": "\u65bd\u51f1\u6587", |
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| "last": "\u3001\u738b\u65b0\u6c11", |
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| { |
| "first": "\uf02b", |
| "middle": [], |
| "last": "\u3001\u9673\u67cf\u7433", |
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| { |
| "first": "Kai-Wun", |
| "middle": [], |
| "last": "Shih", |
| "suffix": "", |
| "affiliation": {}, |
| "email": "" |
| }, |
| { |
| "first": "Kuan-Yu", |
| "middle": [], |
| "last": "Chen", |
| "suffix": "", |
| "affiliation": {}, |
| "email": "kychen@iis.sinica.edu" |
| }, |
| { |
| "first": "Shih-Hung", |
| "middle": [], |
| "last": "Liu", |
| "suffix": "", |
| "affiliation": {}, |
| "email": "" |
| }, |
| { |
| "first": "Hsin-Min", |
| "middle": [], |
| "last": "Wang", |
| "suffix": "", |
| "affiliation": {}, |
| "email": "" |
| }, |
| { |
| "first": "Berlin", |
| "middle": [], |
| "last": "Chen", |
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| "email": "berlin@ntnu.edu.tw" |
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| "abstract": "The rapidly increasing availability of multimedia associated with spoken documents on the Internet has prompted automatic spoken document summarization to be an important research subject. Thus far, the majority of existing work has focused on extractive spoken document summarization, which selects salient sentences from an original spoken document according to a target summarization ratio and concatenates them to form a summary concisely, in order to convey the most important theme of the document. On the other hand, there has been a surge of interest in developing representation learning techniques for a wide variety of natural language processing (NLP)-related tasks. However, to our knowledge, they are largely unexplored in the context of extractive spoken document summarization. With the above background, this study explores a novel use of both word and sentence representation techniques for extractive spoken document summarization. In addition, three variants of sentence ranking models building on top of such representation techniques are proposed. Furthermore, extra information cues like the prosodic features extracted from spoken documents, apart from the lexical features, are also employed for boosting the summarization performance. A series of experiments conducted on the MATBN broadcast news corpus indeed reveal the performance merits of our proposed summarization methods in relation to several state-of-the-art baselines.", |
| "pdf_parse": { |
| "paper_id": "O15-3004", |
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| { |
| "text": "The rapidly increasing availability of multimedia associated with spoken documents on the Internet has prompted automatic spoken document summarization to be an important research subject. Thus far, the majority of existing work has focused on extractive spoken document summarization, which selects salient sentences from an original spoken document according to a target summarization ratio and concatenates them to form a summary concisely, in order to convey the most important theme of the document. On the other hand, there has been a surge of interest in developing representation learning techniques for a wide variety of natural language processing (NLP)-related tasks. However, to our knowledge, they are largely unexplored in the context of extractive spoken document summarization. With the above background, this study explores a novel use of both word and sentence representation techniques for extractive spoken document summarization. In addition, three variants of sentence ranking models building on top of such representation techniques are proposed. Furthermore, extra information cues like the prosodic features extracted from spoken documents, apart from the lexical features, are also employed for boosting the summarization performance. A series of experiments conducted on the MATBN broadcast news corpus indeed reveal the performance merits of our proposed summarization methods in relation to several state-of-the-art baselines.", |
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| "section": "Abstract", |
| "sec_num": null |
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| "text": "\u5de8\u91cf\u8cc7\u6599\u5145\u65a5\u8457\u73fe\u4eca\u7684\u4e16\u754c\uff0c\u5728\u5168\u7403\u8cc7\u8a0a\u7db2(World Wide Web)\u4e2d\u5df2\u5b58\u5728\u6709\u6578\u5341\u5104\u7bc7\u7db2\u9801\uff0c \u4e26\u4e14\u4ee5\u6307\u6578\u7684\u500d\u6578\u6301\u7e8c\u6210\u9577\u8457\u3002\u70ba\u6b64\uff0c\u4eba\u5011\u5fc5\u9808\u4ef0\u8cf4\u53ca\u6642\u6458\u8981\u5404\u985e\u8cc7\u8a0a\u7684\u81ea\u52d5\u5316\u5de5\u5177\uff0c \u4ee5 \u6e1b \u7de9 \u8cc7 \u8a0a \u904e \u8f09 (Information Overload) \u7684 \u554f \u984c \u3002 \u9019 \u4e9b \u8feb \u5207 \u7684 \u9700 \u6c42 \u4fc3 \u4f7f \u4e86 \u81ea \u52d5 \u6458 \u8981 (Automatic Summarization)\u6280\u8853\u7684\u84ec\u52c3\u767c\u5c55 (Luhn, 1958 )\u3002\u81ea\u52d5\u6458\u8981\u6280\u8853\u53ef\u6982\u5206\u70ba\u7bc0\u9304\u5f0f (Extractive)\u6458\u8981\u4ee5\u53ca\u62bd\u8c61\u5f0f(Abstractive)\u6458\u8981\u3002\u524d\u8005\u4e3b\u8981\u662f\u4f9d\u64da\u7279\u5b9a\u7684\u6458\u8981\u6bd4\u4f8b\uff0c\u5f9e\u539f\u59cb \u7684\u6587\u4ef6\u4e2d\u9078\u53d6\u91cd\u8981\u7684\u8a9e\u53e5\u5b50\u96c6(Sentence Subset)\uff0c\u900f\u904e\u8a72\u8a9e\u53e5\u5b50\u96c6\u7c21\u6f54\u5730\u8868\u793a\u539f\u59cb\u6587\u4ef6\u7684 \u5927\u81f4\u5167\u5bb9\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 \u5bb9\u3002\u96d6\u7136\u62bd\u8c61\u5f0f\u6458\u8981\u662f\u6700\u70ba\u8cbc\u8fd1\u4eba\u5011\u65e5\u5e38\u64b0\u5beb\u6458\u8981\u7684\u5f62\u5f0f\uff0c\u4f46\u5176\u6d89\u53ca\u6df1\u5c64\u7684\u81ea\u7136\u8a9e\u8a00\u8655 \u7406\u80fd\u529b (Mitra et al., 1997) \u5411 \u91cf\u7a7a \u9593\u6a21\u578b \u5df2\u88ab \u5ee3\u6cdb\u5730 \u61c9\u7528 \u65bc\u8cc7\u8a0a \u6aa2\uf96a \u4e2d\uff0c\u7528 \u4ee5\u4f30 \u6e2c\u4f7f\u7528 \u8005\u67e5 \u8a62(Query) \u8207\u6587\u4ef6 (Document)\u4e4b\u9593\u7684\u76f8\u95dc\u7a0b\ufa01 (Salton & Lesk, 1968 ", |
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| "text": "(Luhn, 1958", |
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| "text": "| | \u221d | (1) \u5176\u4e2d P(D)\u5c0d\u65bc\u6bcf\u4e00\u8a9e\u53e5\u7686\u76f8\u540c\uff0c\u6545\u53ef\u5ffd\u7565\u3002\u800c\u6211\u5011\u5047\u8a2d\u6bcf\u4e00\u8a9e\u53e5 S \u7684\u4e8b\u524d\u6a5f\u7387 P(S)\u70ba\u4e00 \u500b\u5747\u52fb\u5206\u4f48(Uniform Distribution)\uff0c\u56e0\u6b64 P(S)\u4ea6\u53ef\u5ffd\u7565\u3002\u503c\u5f97\u4e00\u63d0\u7684\u662f\uff0c\u7531\u65bc\u6587\u4ef6\u4e2d\u7684\u8a9e \u53e5\u901a\u5e38\u8f03\u70ba\u7c21\u77ed\uff0c\u4e0d\u5bb9\u6613\u5efa\u7acb\u4e00\u500b\u6e96\u78ba\u7684\u6a21\u578b\u4f86\u5b8c\u6574\u5730\u63cf\u8ff0\u6bcf\u4e00\u8a9e\u53e5\u7684\u5167\u5bb9\u6db5\u610f\u3002\u70ba\u6b64\uff0c \u6709 \u7814 \u7a76 \u5b78 \u8005 \u9678 \u7e8c \u63d0 \u51fa \u5404 \u5f0f \u8f03 \u70ba \u5f37 \u5065 \u6027 \u7684 \u8a9e \u8a00 \u6a21 \u578b \uff0c \u4f8b \u5982 \u95dc \u806f \u6a21 \u578b (Relevance Model)(Lavrenko & Croft, 2001)\u7b49\uff0c\u671f\u671b\u53ef\u4ee5\u6539\u5584\u6b64\u4e00\u554f\u984c\u3002\u95dc\u806f\u6a21\u578b\u7684\u512a\u9ede\u5728\u65bc\u878d\u5165\u95dc \u806f\u6587\u4ef6\u4e2d\u7684\u8cc7\u8a0a\uff0c\u85c9\u6b64\u8c50\u5bcc\u8a9e\u53e5\u6a21\u578b\u4f7f\u5f97\uf901\u6e96\u78ba\u5730\u8868\u9054\u8a9e\u53e5\u7684\u4e3b\u984c\u7279\u6027\uff0c\u4ee5\u63d0\u5347\u6458\u8981\u7684 \u6210\u6548\u3002", |
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| "section": "\u7dd2\u8ad6", |
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| "text": "Okapi BM25 \u662f\u65bc 1994 \u5e74\u7531\u5b78\u8005 Robertson \u7b49\u4eba\u6240\u63d0\u51fa\u7684\u6b0a\u91cd\u8a08\u7b97\u516c\u5f0f\uff0c\u662f\u73fe\u4eca\u8cc7\u8a0a\u6aa2 \u7d22\u6a21\u578b\u4e2d\u6700\u8457\u540d\u7684\u6a5f\u7387\u5f0f\u6aa2\u7d22\u6a21\u578b\u4e4b\u4e00\u3002\u5176\u6b0a\u91cd\u8a08\u7b97\u65b9\u5f0f\u4e3b\u8981\u662f\u5c07\u8a5e\u983b\u5c0d\u6587\u4ef6\u9577\u5ea6\u4f5c\u6b63 \u898f\u5316\uff0c\u6709\u6548\u964d\u4f4e\u56e0\u6587\u4ef6\u9577\u5ea6\u4e0d\u540c\u800c\u7522\u751f\u7684\u6aa2\u7d22\u8aa4\u5dee (Robertson & Jones, 1976; Robertson & \u7bc0\u9304\u5f0f\u8a9e\u97f3\u6587\u4ef6\u6458\u8981\u4f7f\u7528\u8868\u793a\u6cd5\u5b78\u7fd2\u6280\u8853 69 Walker, 1994; Robertson et al., 1996) \u3002\u7576\u5229\u7528\u8a72\u65b9\u6cd5\u65bc\u6587\u4ef6\u6458\u8981\u4efb\u52d9\u6642\uff0c\u6211\u5011\u9996\u5148\u5c0d\u6587\u4ef6 \u7684\u8a5e\u5e8f\u5217 ", |
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| "start": 116, |
| "end": 141, |
| "text": "(Robertson & Jones, 1976;", |
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| { |
| "start": 142, |
| "end": 189, |
| "text": "Robertson & \u7bc0\u9304\u5f0f\u8a9e\u97f3\u6587\u4ef6\u6458\u8981\u4f7f\u7528\u8868\u793a\u6cd5\u5b78\u7fd2\u6280\u8853 69 Walker, 1994;", |
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| { |
| "start": 190, |
| "end": 213, |
| "text": "Robertson et al., 1996)", |
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| "section": "B. Okapi Best Match 25 (BM25)", |
| "sec_num": null |
| }, |
| { |
| "text": "EQUATION", |
| "cite_spans": [], |
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| "start": 0, |
| "end": 8, |
| "text": "EQUATION", |
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| "raw_str": "\u2026 | | \u8a08\u7b97\u51fa\u6bcf\u500b\u8a5e \u8207\u8a9e\u53e5 S \u4e4b\u9593\u7684\u76f8\u4f3c\u6027\u5206\u6578\uff0c\u63a5\u8457\u5c07\u6bcf\u500b\u8a5e \u5c0d\u65bc\u8a9e\u53e5 S \u7684\u76f8\u4f3c\u6027\u5206\u6578\u9032\u884c\u52a0\u6b0a\u6c42\u548c\uff0c\u9032\u800c\u5f97\u5230\u6587\u4ef6 D \u8207\u8a9e\u53e5 S \u7684\u76f8\u4f3c\u6027\u5206\u6578\uff0c\u516c\u5f0f \u5982\u4e0b\uff1a 25 , , , 1 (2) \u2208 , , 1 ,", |
| "eq_num": "(3 , , 1 , 1" |
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| "section": "B. Okapi Best Match 25 (BM25)", |
| "sec_num": null |
| }, |
| { |
| "text": "EQUATION", |
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| "eq_spans": [ |
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| "start": 0, |
| "end": 8, |
| "text": "EQUATION", |
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| "eq_num": "(9)" |
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| "section": "B. Okapi Best Match 25 (BM25)", |
| "sec_num": null |
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| "text": "\u5176\u4e2d \u8207 \u5206\u5225\u70ba\u4f4d\u7f6e \u53ca t \u7684\u8a5e\u8868\u793a\u6cd5\u3002\u5728 CBOW \u8207 SG \u7684\u5be6\u4f5c\u4e2d\u7686\u5f15\u5165\u968e\u5c64\u8edf \u5f0f\u6700\u5927\u5316\u6cd5 (Mikolov et al., 2013b; Morin & Bengio, 2005) ", |
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| "start": 50, |
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| "text": "(Mikolov et al., 2013b;", |
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| "start": 74, |
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| "text": "Morin & Bengio, 2005)", |
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| "section": "B. Okapi Best Match 25 (BM25)", |
| "sec_num": null |
| }, |
| { |
| "text": "EQUATION", |
| "cite_spans": [], |
| "ref_spans": [], |
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| { |
| "start": 0, |
| "end": 8, |
| "text": "EQUATION", |
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| "text": "\u63a5\u8457\uff0c\u900f\u904e\u7dda\u6027\u7d44\u5408(Linear Combination)\u7684\u65b9\u5f0f\uff0c\u53ef\u4ee5\u5f62\u6210\u4e00\u500b\u8907\u5408\u5f0f\u7684\u8a9e\u53e5\u8a9e\u8a00\u6a21\u578b\uff0c \u800c\u6587\u4ef6\u7684\u751f\u6210\u6a5f\u7387\u5c31\u53ef\u4ee5\u7d93\u7531\u4e0b\u5f0f\u8a08\u7b97\uff1a ", |
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| { |
| "text": "EQUATION", |
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| "start": 0, |
| "end": 8, |
| "text": "EQUATION", |
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| "raw_str": "P | \u03bb \u2022 | \u2022 \u2208 1 \u03bb \u2022 , \u2208", |
| "eq_num": "(15)" |
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| "section": "\u9918\u5f26\u76f8\u4f3c\u5ea6(Cosine Similarity)", |
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| }, |
| { |
| "text": "EQUATION", |
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| { |
| "start": 0, |
| "end": 8, |
| "text": "EQUATION", |
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| "eq_num": "(17" |
| } |
| ], |
| "section": "\u9918\u5f26\u76f8\u4f3c\u5ea6(Cosine Similarity)", |
| "sec_num": "4.1" |
| } |
| ], |
| "back_matter": [], |
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| "num": null, |
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| "text": "\u53ca\u8ca0\u4f8b\u63a1\u6a23\u6cd5(Mikolov et al., 2013b;Mnih & Kavukcuoglu, 2013)\uff0c\u4ee5\u589e\u9032\u8a13\u7df4\u904e\u7a0b\u4e2d\u53c3\u6578\u4f30\u6e2c\u7684\u6548\u80fd\u3002 3.2 \u8a9e\u53e5\u8868\u793a\u6cd5(Sentence Representation) \u96d6\u7136\u8a5e\u8868\u793a\u6cd5\u5df2\u88ab\u5ee3\u6cdb\u4f7f\u7528\uff0c\u4f46\u8a31\u591a\u81ea\u7136\u8a9e\u8a00\u8655\u7406\u7684\u76f8\u95dc\u4efb\u52d9\u6240\u9700\u8981\u7684\u662f\u8a9e\u53e5\u7684\u8868\u793a\u6cd5\u3002 \u5ef6\u7e8c\u8a5e\u8868\u793a\u6cd5\u7684\u57fa\u672c\u6a21\u578b\u67b6\u69cb\u8207\u7cbe\u795e\uff0c\u5b78\u8005 Le \u8207 Mikolov \u63d0\u51fa\u5169\u7a2e\u5b78\u7fd2\u8a9e\u53e5\u8868\u793a\u6cd5\u7684\u6a21 \u578b\uff0c\u5206\u5225\u662f\u5206\u6563\u5f0f\u5132\u5b58\u6a21\u578b\u8207\u5206\u6563\u5f0f\u8a5e\u888b\u6a21\u578b(Le & Mikolov, 2014)\u3002 A. \u5206\u6563\u5f0f\u5132\u5b58\u6a21\u578b(Distributed Memory Model of Paragraph Vector, PV-DM) \u5206\u6563\u5f0f\u5132\u5b58\u6a21\u578b(PV-DM)\u985e\u4f3c\u65bc\u9023\u7e8c\u578b\u8a5e\u888b\u6a21\u578b\u3002PV-DM \u540c\u6a23\u4ee5\u6700\u5927\u5316\u76ee\u6a19\u4e2d\u9593\u8a5e\u8f38\u51fa \u7684\u6a5f\u7387\u70ba\u76ee\u6a19\uff0c\u5176\u4e3b\u8981\u5dee\u7570\u70ba\uff1a(1)\u8a13\u7df4\u904e\u7a0b\u4e2d\u65bc\u8f38\u5165\u5c64(Input Layer)\u5f15\u5165\u4e00\u500b\u6bb5\u843d\u7de8\u865f (Paragraph ID)\uff0c\u4ea6\u5373\u8a13\u7df4\u8a9e\u6599\u4e2d\u6bcf\u4e00\u8a9e\u53e5\u7686\u6709\u4e00\u500b\u552f\u4e00\u7684\u6bb5\u843d\u7de8\u865f\u3002\u6bb5\u843d\u7de8\u865f\u8207\u4e00\u822c\u7684 (Distributed Bag-of-Words of Paragraph Vector, PV-DBOW) \u5206\u6563\u5f0f\u5132\u5b58\u6a21\u578b(PV-DM)\u662f\u63a1\u7528\u8a5e\u5411\u91cf\u8207\u6bb5\u843d\u5411\u91cf\u7684\u5e73\u5747\u6216\u662f\u4e32\u806f\uff0c\u9032\u884c\u9810\u6e2c\u4e00\u500b\u8a5e\u3002 \u5206\u6563\u5f0f\u8a5e\u888b\u6a21\u578b(PV-DBOW)\u5247\u662f\u4ee5\u6bb5\u843d\u5411\u91cf\u4f5c\u70ba\u8f38\u5165\uff0c\u5f9e\u8a72\u5411\u91cf\u5c0d\u61c9\u7684\u6bb5\u843d\u4e2d\u96a8\u6a5f\u63a1\u6a23 \u8a5e\u5e8f\u5217\u4f5c\u70ba\u8f38\u51fa\uff0c\u8a72\u65b9\u6cd5\u6e1b\u5c11\u4e86\u8f38\u5165\u5c64\u7684\u53c3\u6578\u91cf\u3002\u985e\u4f3c\u65bc\u8df3\u8e8d\u5f0f\u6a21\u578b\uff0c\u5176\u5716\u5f62\u8868\u793a\u6cd5\u5982 \u5716 2(b)\u6240\u793a\u3002\u8a72\u6a21\u578b\u7684\u6982\u5ff5\u7c21\u55ae\u4e14\u50c5\u9700\u5c11\u91cf\u5132\u5b58\u7a7a\u9593(\u8a5e\u5411\u91cf\u8207\u8f38\u51fa\u5c64\u8edf\u5f0f\u6700\u5927\u5316\u6cd5\u65bc\u8a13 \u7df4\u968e\u6bb5\u6240\u5f97\u4e4b\u53c3\u6578)\u30024. \u904b\u7528\u8868\u793a\u6cd5\u5b78\u7fd2\u65bc\u8a9e\u97f3\u6587\u4ef6\u6458\u8981", |
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| "content": "<table><tr><td/><td colspan=\"7\">\u7bc0\u9304\u5f0f\u8a9e\u97f3\u6587\u4ef6\u6458\u8981\u4f7f\u7528\u8868\u793a\u6cd5\u5b78\u7fd2\u6280\u8853</td><td>\u65bd\u51f1\u6587 \u7b49 71 \u65bd\u51f1\u6587 \u7b49</td></tr><tr><td colspan=\"8\">, (Continuous Distributed Representation)\u3002\u5f62\u5f0f\u4e0a\uff0c\u7d66\u5b9a\u4e00\u8a5e\u5e8f\u5217 | | , \u2211 , | | , , 0 \u6a19\u51fd\u6578(Objective Function)\u662f\u8981\u6700\u5927\u5316\u5c0d\u6578\u6a5f\u7387(Log-Probability)\uff1a ,</td><td>\u2026 \uff0cCBOW \u7684\u76ee (5)</td></tr><tr><td>|</td><td>\u2022 , \u2026 ,</td><td>,</td><td>, \u2026 ,</td><td colspan=\"2\">0 \u2022 ,</td><td>,</td><td>| | 1</td><td>(6) (7)</td></tr><tr><td colspan=\"3\">\u5176\u4e2d|D|\u70ba\u6587\u4ef6 D \u4e2d\u8a9e\u53e5\u7684\u500b\u6578\uff0c</td><td colspan=\"6\">\u2022 ,\u2022 \u70ba\u76f8\u4f3c\u5ea6\u51fd\u6578\uff0c\u7528\u4ee5\u8a08\u7b97\u5169\u500b\u8a9e\u53e5\u4e4b\u9593\u7684\u76f8\u4f3c</td></tr><tr><td>\u7a0b\u5ea6\u3002</td><td/><td/><td/><td/><td/><td/><td/></tr><tr><td colspan=\"7\">3. \u8868\u793a\u6cd5\u5b78\u7fd2(Representation Learning)</td><td/></tr><tr><td colspan=\"5\">3.1 \u8a5e\u8868\u793a\u6cd5(Word Representation)</td><td>|</td><td>| |</td><td>|</td><td>(4)</td></tr><tr><td colspan=\"9\">\u7576\u4e00\u7a2e\u81ea\u7136\u8a9e\u8a00\u8655\u7406\u7684\u554f\u984c\u8981\u8f49\u5316\u70ba\u6a5f\u5668\u5b78\u7fd2\u7684\u554f\u984c\uff0c\u9996\u5148\u9700\u8981\u627e\u5230\u4e00\u7a2e\u65b9\u6cd5\u5c07\u9019\u4e9b\u8a9e</td></tr><tr><td colspan=\"9\">\u5176\u4e2d \u3001 \u4ee5\u53ca b \u5747\u70ba\u81ea\u7531\u53c3\u6578\uff0c\u6839\u64da\u7d93\u9a57\u8a2d\u7f6e\uff0c\u4e00\u822c \u2208 1.2, 2.0 \u3001 \u8a00\u7b26\u865f\u6578\u5b78\u5316\u3002\u50b3\u7d71\u7684\u81ea\u7136\u8a9e\u8a00\u8655\u7406\u4e2d\u6700\u76f4\u89c0\u7684\u65b9\u5f0f\u662f\u63a1\u7528 One-hot \u8868\u793a\u6cd5\uff0c\u5373\u6bcf\u4e00\u500b 0.75\uff1b , \u662f\u8a5e</td></tr><tr><td colspan=\"9\">\u5728\u8a9e\u53e5 S \u4e2d\u51fa\u73fe\u7684\u6b21\u6578\uff1b \u9577\u5ea6\uff0c| \u8a5e\u7686\u4ee5\u4e00\u500b K \u7dad(\u901a\u5e38 K \u70ba\u8a5e\u5f59\u7684\u5927\u5c0f)\u7684\u5411\u91cf\u8868\u793a\u4e4b\uff0c\u800c\u6b64\u5411\u91cf\u4e2d\u50c5\u6709\u67d0\u4e00\u500b\u7dad\u5ea6\u70ba 1\uff0c , \u662f\u8a5e \u5728\u6587\u4ef6 D \u4e2d\u51fa\u73fe\u7684\u6b21\u6578\uff1b\u800c|S|\u662f\u8868\u793a\u8a9e\u53e5 S \u7684 |\u662f\u5728\u6587\u4ef6\u4e2d\u6240\u6709\u8a9e\u53e5\u7684\u5e73\u5747\u9577\u5ea6\uff1aN \u662f\u5728\u96c6\u5408\u4e2d\u7684\u6587\u4ef6\u7e3d\u6578\uff1a \u70ba\u5728\u96c6\u5408 \u5176\u9918\u70ba\u96f6\u3002\u660e\u986f\u5730\uff0c\u6b64\u7a2e\u8868\u793a\u6cd5\u4e2d\u4efb\u610f\u5169\u500b\u8a5e\u4e4b\u9593\u5f7c\u6b64\u4e92\u76f8\u7368\u7acb\uff0c\u610f\u5373\u6211\u5011\u7121\u6cd5\u8a08\u7b97\u51fa</td></tr><tr><td colspan=\"9\">\u4e2d\u6587\u4ef6\u5305\u542b\u8a5e \u7684\u7bc7\u6578\u3002 \u4efb\u5169\u500b\u8a5e\u4e4b\u9593\u7684\u76f8\u4f3c\u7a0b\u5ea6\u3002\u70ba\u4e86\u89e3\u6c7a\u4e0a\u8ff0\u554f\u984c\uff0c\u5b78\u8005 Hinton \u9996\u5148\u65bc 1986 \u5e74\u63d0\u51fa\u4e86\u4e00\u7a2e</td></tr><tr><td colspan=\"9\">\u5206\u6563\u5f0f\u8868\u793a\u6cd5(Distributed Representation)\u6a21\u578b(Hinton, 1986)\uff0c\u85c9\u7531\u8a13\u7df4\u5c07\u6bcf\u4e00\u500b\u8a5e\u91cd\u65b0\u4ee5</td></tr><tr><td colspan=\"9\">2.4 \u4ee5\u5716\u8ad6\u70ba\u57fa\u790e\u6458\u8981\u4e4b\u65b9\u6cd5 \u4e00\u500b\u8f03\u4f4e\u7dad\u5ea6\u7684\u5be6\u6578\u5411\u91cf\u8868\u793a\u4e4b\uff0c\u900f\u904e\u9019\u500b\u4f4e\u7dad\u5ea6\u7684\u5411\u91cf\u8868\u793a\u6cd5\uff0c\u8a5e\u8207\u8a5e\u4e4b\u9593\u7684\u95dc\u4fc2\u53ef (a) (b)</td></tr><tr><td colspan=\"9\">\u4ee5\u7c21\u55ae\u5730\u900f\u904e\u8ddd\u96e2\u516c\u5f0f(\u5982\u9918\u5f26\u3001\u6b50\u5f0f\u8ddd\u96e2)\u4f86\u8a08\u7b97\uff0c\u4e26\u4f9d\u6b64\u5224\u65b7\u8a5e\u8207\u8a5e\u4e4b\u9593\u8a9e\u610f\u7684\u76f8\u8fd1 \u5716 1 (a). \u9023\u7e8c\u578b\u8a5e\u888b\u6a21\u578b\u4e4b\u793a\u610f\u5716 (b). \u8df3\u8e8d\u5f0f\u6a21\u578b\u4e4b\u793a\u610f\u5716</td></tr><tr><td colspan=\"9\">A. \u8a5e\u6b0a\u91cd-\u9006\u5411\u6587\u4ef6\u983b\u7387(\u8a5e\u6b0a\u91cd-\u9006\u5411\u6587\u4ef6\u983b\u7387\u6a21\u578b\u662f\u7531\u5b78\u8005 Rousseau \u8207 Vazirgiannis \u65bc 2013 \u5e74\u6240\u63d0\u51fa(Rousseau \u5176\u4e2d c \u70ba\u4e2d\u9593\u8a5e \u7684\u4e0a\u4e0b\u6587\u4e4b\u7a97\u53e3\u5927\u5c0f(Window Size)\uff0cT \u4ee3\u8868\u8a13\u7df4\u8a9e\u6599\u7684\u9577\u5ea6\uff0c\u4e14</td></tr><tr><td colspan=\"9\">& Vazirgiannis, 2013).\u3002\u9996\u5148\uff0c\u6b64\u65b9\u6cd5\u70ba\u6bcf\u4e00\u7bc7\u6587\u4ef6\u5efa\u7acb\u4e00\u500b\u6709\u5411\u5716(Directed Graph)\uff0c\u5716 \u4e2d\u7684\u6bcf\u4e00\u500b\u9802\u9ede(Vertex)\u4ee3\u8868\u6587\u4ef6\u4e2d\u7684\u4e00\u500b\u552f\u7368\u8a5e(Unique Word)\u3002\u5982\u679c\u4efb\u5169\u500b\u8a5e\u5728\u6587\u4ef6\u4e2d | , \u2026 , , , \u2026 , \u2022 \u2211 \u2022 (8)</td></tr><tr><td colspan=\"9\">\u66fe\u7d93\u76f8\u9130\u51fa\u73fe\uff0c\u5247\u6b64\u5169\u500b\u9802\u9ede\u53ef\u4ee5\u7528\u6bcf\u4e00\u500b\u908a(Edge)\u76f8\u9023\uff0c\u908a\u7684\u65b9\u5411\u8868\u793a\u9019\u5169\u500b\u8a5e\u51fa\u73fe \u6642\u7684\u5148\u5f8c\u6b21\u5e8f\u3002\u6700\u5f8c\uff0c\u7d71\u8a08\u6709\u5411\u5716\u4e2d\u6bcf\u4e00\u500b\u9802\u9ede\u7684\u5167\u5206\u652f\u5ea6(In-degree)\u500b\u6578\uff0c\u4e26\u8207 BM25 \u5176\u4e2d \u70ba\u4f4d\u7f6e t \u8a5e \u7684\u8a5e\u8868\u793a\u6cd5\uff0cV \u662f\u8a5e\u5f59\u7684\u5927\u5c0f\uff0c \u4ee3\u8868 \u7684\u4e0a\u4e0b\u6587\u8a5e\u8868\u793a\u6cd5\u4e4b\u52a0\u6b0a</td></tr><tr><td colspan=\"9\">\u6a21\u578b\u76f8\u7d50\u5408\uff0c\u5373\u53ef\u6c42\u5f97\u6587\u4ef6\u8207\u8a9e\u53e5\u9593\u7684\u95dc\u806f\u7a0b\u5ea6\u3002\u76f8\u8f03\u65bc\u5927\u591a\u6578\u73fe\u5b58\u7684\u6458\u8981\u6a21\u578b(\u4f8b\u5982</td></tr><tr><td colspan=\"9\">TF-IDF \u8207 BM25)\uff0c\u50c5\u8003\u616e\u6bcf\u4e00\u500b\u8a5e\u51fa\u73fe\u7684\u983b\u7387\uff0c\u8a5e\u6b0a\u91cd-\u9006\u5411\u6587\u4ef6\u983b\u7387\u6a21\u578b\u57fa\u65bc\u5167\u5206\u652f</td></tr><tr><td colspan=\"9\">\u5ea6\u500b\u6578\uff0c\u91cd\u65b0\u8ce6\u4e88\u6bcf\u4e00\u500b\u8a5e\u4e00\u500b\u6b0a\u91cd\uff0c\u9032\u4e00\u6b65\u5730\u8003\u616e\u4e86\u6587\u5b57\u9593\u5728\u6587\u4ef6\u4e2d\u7684\u5148\u5f8c\u6b21\u5e8f\u95dc\u4fc2\u3002</td></tr><tr><td colspan=\"8\">B. \u99ac\u53ef\u592b\u96a8\u6a5f\u6f2b\u6b65(Markov Random Walk, MRW)</td></tr><tr><td colspan=\"9\">\u99ac\u53ef\u592b\u96a8\u6a5f\u6f2b\u6b65\u6a21\u578b\u7684\u6982\u5ff5\u662f\u5c07\u6587\u4ef6\u8996\u70ba\u4e00\u500b\u7db2\u969b\u7db2\u8def\uff0c\u6587\u4ef6\u4e2d\u7684\u6bcf\u4e00\u8a9e\u53e5\u4ee3\u8868\u7db2\u8def\u4e0a 2013b)\uff0c\u8a72\u6a21\u578b\u540c\u6a23\u4ee5\u7c21\u5316\u7684\u524d\u994b\u985e\u795e\u7d93\u7db2\u8def\u4f86\u5b78\u7fd2\u8a5e\u8868\u793a\u6cd5\u3002\u66f4\u660e\u78ba\u5730\uff0c\u8df3\u8e8d\u5f0f\u6a21\u578b \u7684\u4e00\u500b\u7bc0\u9ede(Node)\uff0c\u800c\u8a9e\u53e5\u4e4b\u9593\u7684\u76f8\u95dc\u7a0b\u5ea6\u5247\u70ba\u7bc0\u9ede\u9593\u908a\u754c(Edge)\u7684\u6b0a\u91cd(Wan & Yang, \u8207\u9023\u7e8c\u578b\u8a5e\u888b\u6a21\u578b\u7684\u6a21\u578b\u8a13\u7df4\u76ee\u6a19\u6070\u597d\u76f8\u53cd\uff0c\u8df3\u8e8d\u5f0f\u6a21\u578b\u662f\u5e0c\u671b\u5728\u7d66\u5b9a\u4e00\u500b\u8a5e \u5f8c\uff0c\u53ef 2008)\u3002\u99ac\u53ef\u592b\u96a8\u6a5f\u6f2b\u6b65\u6a21\u578b\u63d0\u51fa\u4e00\u5957\u905e\u8ff4\u66f4\u65b0\u7684\u6f14\u7b97\u6cd5\uff0c\u5229\u7528\u7bc0\u9ede\u9593\u908a\u754c\u7684\u6b0a\u91cd\u95dc\u4fc2\u4e0d \u4ee5\u6e96\u78ba\u5730\u9810\u6e2c\u5176\u4e0a\u4e0b\u6587\u4e2d\uff0c\u8a5e\u51fa\u73fe\u7684\u53ef\u80fd\u6027\u3002\u8a13\u7df4\u7684\u904e\u7a0b\u4e2d\uff0c\u8a72\u6a21\u578b\u662f\u4f7f\u7528\u6bcf\u4e00\u7576\u524d\u8a5e \u65b7\u5730\u91cd\u8907\u66f4\u65b0\u7bc0\u9ede\u7684\u91cd\u8981\u6027\uff0c\u6700\u7d42\u7372\u5f97\u6bcf\u4e00\u8a9e\u53e5\u7684\u91cd\u8981\u6027\u5206\u6578\u3002\u66f4\u660e\u78ba\u5730\uff0c\u8a9e\u53e5 \u7684\u91cd \u8981\u6027\u5206\u6578 \u505a\u70ba\u5c0d\u6578\u7dda\u6027\u5206\u985e\u5668(Log-Linear Classifier)\u7684\u8f38\u5165\uff0c\u4e26\u9810\u6e2c\u6b64\u7576\u524d\u8a5e\u4e00\u5b9a\u7bc4\u570d\u5167\u7684\u524d\u5f8c\u7684 \u662f\u7531\u76f8\u9130\u7684\u8a9e\u53e5 \u5206\u6578\u7684\u7dda\u6027\u7d44\u5408\u800c\u5f97\uff0c\u6211\u5011\u53ef\u4ee5\u5c07\u8a9e\u53e5\u9593\u908a\u754c\u7684 \u6b0a\u91cd\u95dc\u4fc2\u4ee5\u4e00\u500b\u77e9\u9663 \u8a5e\uff0c\u5176\u5716\u5f62\u8868\u793a\u5982\u5716 1(b)\u6240\u793a\u3002\u7576\u7d66\u5b9a\u4e00\u8a5e\u5e8f\u5217 \u2026 \u5f8c\uff0cSG \u7684\u76ee\u6a19\u51fd\u6578\u662f\u8981\u6700\u5927 , | | | | \u8868\u793a\u4e4b\uff0c\u5247\u99ac\u53ef\u592b\u96a8\u6a5f\u6f2b\u6b65\u6a21\u578b\u53ef\u4ee5\u8868\u793a\u70ba\uff1a \u5316\u5c0d\u6578\u6a5f\u7387\uff1a</td></tr></table>" |
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| "text": "SD \u7684\u5be6\u9a57\u4e2d\uff0cBM25 \u53cd\u800c\u8d85\u8d8a RM \u6210\u70ba\u6240\u6709\u6a21\u578b\u4e2d\u6700\u4f73\u7684\u6458\u8981\u65b9\u6cd5\uff0c\u6211\u5011\u8a8d\u70ba \u9019\u53ef\u80fd\u662f\u56e0\u70ba RM \u4e2d\u6240\u4f7f\u7528\u7684\u8a9e\u53e5\u6a21\u578b\u53d7\u5230\u8a9e\u97f3\u8fa8\u8b58\u932f\u8aa4\u7684\u5f71\u97ff\uff0c\u56e0\u6b64\u964d\u4f4e\u5c0b\u627e\u6709\u6548\u7684 \u865b\u64ec\u95dc\u806f\u6587\u4ef6(Pseudo Relevant Documents)\u7684\u80fd\u529b\u3002\u6b64\u5916\uff0cTW-IDF \u8207 MRW \u7684\u6458\u8981\u6548\u80fd \u7686\u8f03 LSA \u53ca MMR \u5dee\uff0c\u6211\u5011\u8a8d\u70ba\u4ea6\u662f\u53d7\u5230\u8a9e\u97f3\u8fa8\u8b58\u932f\u8aa4\u7684\u5f71\u97ff\uff0c\u56e0\u4e00\u500b\u8a5e\u6216\u662f\u4e00\u500b\u8a9e\u53e5 \u7684\u91cd\u8981\u6027\u5206\u6578\u662f\u4f86\u81ea\u9130\u8fd1\u5176\u5b83\u8a5e\u6216\u662f\u8a9e\u53e5\u7684\u8ca2\u737b\u3002\u800c LEAD \u7121\u8ad6\u5728 TD \u6216\u662f SD \u4e0a\uff0c\u76f8 \u8f03\u65bc\u5176\u5b83\u6a21\u578b\u7686\u5f97\u5230\u8f03\u5dee\u7684\u6548\u679c\uff0c\u4e3b\u8981\u539f\u56e0\u662f LEAD \u50c5\u9069\u7528\u65bc\u7279\u6b8a\u6587\u4ef6\u7d50\u69cb\uff0c\u56e0\u6b64\u82e5\u6458 \u8981\u6587\u4ef6\u4e0d\u5177\u6709\u67d0\u7a2e\u7279\u6b8a\u7684\u7d50\u69cb\uff0c\u5176\u6458\u8981\u6548\u80fd\u5c31\u6703\u6709\u6240\u4fb7\u9650\u3002 \u3002\u5728 TD \u5be6\u9a57\u4e2d\uff0cCBOW \u6458\u8981\u6548\u80fd\u8f03 BM25 \u5dee\uff0c\u800c SG \u672a\u9054\u5230 MRW \u7684\u6c34\u5e73\u3002 \u5728 SD \u5be6\u9a57\u4e2d\uff0c\u4ecd\u7136\u4ee5 BM25 \u7684\u6458\u8981\u6548\u679c\u70ba\u4f73\u3002 TD \u7684\u5be6\u9a57\u7d50\u679c\u4e2d\u53ef\u4ee5\u89c0\u5bdf\u5230\uff0cPV-DM \u7684\u6458\u8981\u6548\u80fd\u986f\u8457\u5730\u512a\u65bc\u8868 2 \u4e2d\u6240\u6709\u7684\u50b3\u7d71\u6587\u4ef6 \u6458\u8981\u6a21\u578b\uff0c\u4ea6\u662f\u6240\u6709\u8868\u793a\u6cd5\u4e2d\u5177\u6700\u4f73\u6458\u8981\u6548\u80fd\u4e4b\u6a21\u578b\u3002\u6211\u5011\u4ea6\u89c0\u5bdf\u5230 PV-DBOW \u8207\u8868 7 \u4e2d\u7684\u8a5e\u8868\u793a\u6cd5 SG \u6709\u8457\u76f8\u540c\u7684\u6458\u8981\u6210\u6548\u3002\u7136\u800c\u65bc SD \u4e2d\uff0c\u8a72\u5169\u7a2e\u8a9e\u53e5\u8868\u793a\u6cd5\u50c5\u9054\u5230 RM \u7684 \u6c34\u5e73\uff0c\u4f46\u7686\u4ecd\u4e0d\u53ca BM25\u3002", |
| "num": null, |
| "content": "<table><tr><td>) \u6587 \u5b57\u6587 \u4ef6\u5167\u5bb9 \u9664\u4e86 \u63d0\u4f9b\u6587 \u5b57\u8a0a \u606f\u4f5c\u70ba \u91cd\u8981 \u8a9e\u53e5\u9078 \u53d6\u4f9d \u64da\u4e4b\u5916 \uff0c\u8a9e \u53e5\u4e2d\u66f4 \u5305\u542b \u6587\u6cd5 5. \u8a9e\u97f3\u6587\u4ef6\u4e4b\u5404\u7a2e\u7279\u5fb5\u7c21\u4ecb (Grammar)\u3001\u8a9e\u610f(Semantic)\u4ee5\u53ca\u7d50\u69cb(Structure)\u7b49\u8cc7\u8a0a\uff0c\u7686\u53ef\u8996\u70ba\u91cd\u8981\u7684\u7279\u5fb5\u3002\u4e0d\u540c\u65bc\u6587 \u5b57\u6587\u4ef6\uff0c\u8a9e\u97f3\u6587\u4ef6\u5167\u5bb9\u53ef\u80fd\u56e0\u8fa8\u8b58\u932f\u8aa4\u6216\u8a9e\u53e5\u908a\u754c\u5b9a\u7fa9\u7b49\u554f\u984c\uff0c\u4f7f\u5f97\u8a9e\u53e5\u6587\u6cd5\u3001\u8a9e\u610f\u4ee5 \u53ca\u7d50\u69cb\u7b49\u8cc7\u8a0a\u76f8\u5c0d\u8f03\u7f3a\u4e4f\uff0c\u4f46\u8a9e\u97f3\u6587\u4ef6\u537b\u542b\u6709\u8c50\u5bcc\u7684\u97fb\u5f8b\u7279\u5fb5(Prosodic Features)\uff0c\u5982\u8a9e \u8005\u5728\u8aaa\u8a71\u6642\u767c\u97f3\u7684\u9577\u77ed\u5feb\u6162\u3001\u8a9e\u6c23\u7684\u6291\u63da\u9813\u632b\u4ee5\u53ca\u9ad8\u4f4e\u8d77\u4f0f\u7b49\u3002\u56e0\u6b64\u82e5\u5c07\u9019\u4e9b\u8a9e\u8a00\u5b78\u3001 \u8072\u97fb\u5b78\u4ee5\u53ca\u6587\u4ef6\u7d50\u69cb\u7b49\u8cc7\u8a0a\u52a0\u4ee5\u5584\u7528\uff0c\u76f8\u4fe1\u6709\u52a9\u65bc\u63d0\u5347\u7bc0\u9304\u5f0f\u8a9e\u97f3\u6587\u4ef6\u6458\u8981\u7684\u6548\u80fd\u3002 \u7bc0\u9304\u5f0f\u8a9e\u97f3\u6587\u4ef6\u6458\u8981\u4f7f\u7528\u8868\u793a\u6cd5\u5b78\u7fd2\u6280\u8853 75 5.1 \u97fb\u5f8b\u7279\u5fb5(Prosodic Features) A. \u97f3\u9ad8(Pitch) \u4e00\u822c\u8a9e\u8005\u5728\u6558\u8ff0\u4e00\u4ef6\u4e8b\u60c5\u6642\uff0c\u6703\u4ee5\uf96f\u8a71\u7684\u9ad8\u4f4e\u8d77\u4f0f\u3001\u6291\u63da\u9813\u632b\uf92d\u5f37\u8abf\uf96f\u8a71\u7684\u5167\u5bb9\u4ee5\u5438\u5f15 \u807d\u8005\u7684\u6ce8\u610f\uff0c\u8a9e\u8005\u8868\u9054\u81ea\u8eab\u7684\u611f\u89ba\u4f7f\u5f97\u5c0d\u65b9\u63a5\u53d7\u5230\u5f37\u8abf\u7684\u8a0a\u606f\uff0c\u56e0\u6b64\u97f3\u9ad8\u53ef\u8996\u70ba\u4e00\u7a2e\u8a9e \u97f3\u4e2d\u91cd\u8981\u7684\u8cc7\u8a0a\u3002 B. \u80fd\u91cf(Energy) \u80fd\uf97e\u53ef\u7528\uf92d\u8868\u793a\u8a9e\u8005\uf96f\u8a71\u97f3\uf97e\u7684\u5927\u5c0f\uff0c\u7d93\u5e38\u88ab\u8996\u70ba\u4e00\u7a2e\u53ef\uf9dd\u7528\u7684\u91cd\u8981\u8cc7\u8a0a\u3002\u4e00\u822c\u8a9e\u8005\u5728 \u7279\u5225\u5f37\u8abf\u4e00\u4ef6\u4e8b\u60c5\u6216\u662f\u6558\u8ff0\u91cd\u9ede\u6642\uff0c\u6703\u523b\u610f\u5730\u63d0\u9ad8\u97f3\uf97e\uf92d\u8868\u793a\u5f37\u8abf\u95dc\u9375\u5b57\u6216\u662f\uf96f\u8a71\u7684\u5167 \u5bb9\u4ee5\u5e0c\u671b\u5f15\u8d77\u807d\u8005\u7684\u6ce8\u610f\u3002 C. \u97f3\u6846\u9577\u5ea6(Duration) \u985e\u4f3c\u65bc\u8a9e\u53e5\u9577\u5ea6\uff0c\u8a9e\u53e5\u8d8a\u9577\u6240\u5305\u542b\u7684\u8cc7\u8a0a\u8d8a\u591a\uff0c\u800c\u8a9e\uf906\u7684\u97f3\u6846\u9577\u5ea6\u4ee3\u8868\u8a9e\u8005\u8aaa\u8a72\u8a9e\uf906\u7684 \u6642\u9593\u9577\ufa01\uff0c\u56e0\u6b64\u8aaa\u8a71\u6642\u9593\u8d8a\u9577\u7684\u8a9e\uf906\u5176\u5305\u542b\u7684\u8cc7\u8a0a\u4ea6\u8d8a\u591a\u3002 D. \u983b\u8b5c\u5cf0(Peak)\u8207\u5171\u632f\u5cf0(Formant) \u5171\u632f\u5cf0\u88ab\u5b9a\u7fa9\u70ba\"\u8072\u8b5c\u4e2d\u7684\u983b\u8b5c\u5cf0\"\uff0c\u5dee\u7570\u5728\u65bc\u6bcd\u97f3(Vowel)\u6709\u5171\u632f\u5cf0\u7684\u7d50\u69cb\uff0c\u5728\u6bcd\u97f3\u767c \u97f3\u8f03\u70ba\u6e05\u695a\u7684\u97f3\u7bc0(Syllable)\uff0c\u5171\u632f\u5cf0\u6703\u8f03\u9ad8\u3002\u5171\u632f\u5cf0\u662f\u7528\u4f86\u63cf\u8ff0\u8072\u5b78\u5171\u632f\u73fe\u8c61\u7684\u4e00\u7a2e\u6982 \u5ff5\uff0c\u662f\u6c7a\u5b9a\u8a9e\u8005\u7279\u5fb5\u7684\u4e3b\u8981\u56e0\u7d20\u3002\u5728\u6709\u6548\u983b\u5bec\u7bc4\u570d\u4e2d\u6703\u6709\u7d04\u4e94\u500b\u5171\u632f\u5cf0\uff0c\u5f9e\u4f4e\u983b\uf961\u81f3\u9ad8 \u983b\uf961\u4f9d\u5e8f\u6392\uf99c\u70ba\u7b2c\u4e00\u5171\u632f\u5cf0(F1)\u3001\u7b2c\u4e8c\u5171\u632f\u5cf0(F2) \u3001\u7b2c\u4e09\u5171\u632f\u5cf0(F3)\u3001\u7b2c\u56db\u5171\u632f\u5cf0(F4) \u4ee5\u53ca\u7b2c\u4e94\u5171\u632f\u5cf0(F5)\uff0c\u800c\u901a\u5e38\u4ee5 F1\u3001F2\u3001F3 \u8f03\u70ba\u660e\u986f\uff0c\u56e0\u6b64\u901a\u5e38\u4ee5\u9019\u4e09\u500b\u5171\u632f\u5cf0\u70ba\u4ee3\u8868\u3002 \u82e5\u8a9e\u8005\u5728\u8868\u9054\u67d0\u8a9e\u53e5\u8f03\u70ba\u5b57\u6b63\u8154\u5713\uff0c\u5e0c\u671b\u807d\u773e\u53ef\u4ee5\u807d\u5f97\u6e05\u695a\u6642\uff0c\u8a72\u8a9e\u53e5\u53ef\u80fd\u70ba\u91cd\u8981\u8a9e\u53e5\uff0c \u5171\u632f\u5cf0\u6574\u9ad4\u4f86\u8aaa\u6703\u8f03\u9ad8\uff1b\u82e5\u662f\u8a9e\u8005\u6240\u542b\u7cca\u5e36\u904e\u7684\u8a9e\u53e5\u5247\u53ef\u80fd\u70ba\u975e\u91cd\u8981\u8a9e\u53e5\uff0c\u5176\u5171\u632f\u5cf0\u6574 \u9ad4\u4f86\u8aaa\u6703\u8f03\u4f4e\u3002 5.2 \u8a5e\u5f59\u7279\u5fb5(Lexical Features) A. \u96d9\u9023\u8a9e\u8a00\u6a21\u578b\u5206\u6578(Bigram Language Model Score) N \u9023\u8a9e\u8a00\u6a21\u578b(N-gram Language Model)\u662f\u81ea\u7136\u8a9e\u8a00\u8655\u7406\u5e38\u7528\u5230\u7684\u65b9\u6cd5\uff0c\u5176\u5047\u8a2d\u7b2c N \u500b\u8a5e \u7684\u51fa\u73fe\u50c5\u8207\u524d\u9762 N -1 \u500b\u8a5e\u76f8\u95dc\uff0c\u6a21\u578b\u53c3\u6578\u5247\u901a\u5e38\u85c9\u7531\u6700\u5927\u5316\u76f8\u4f3c\u5ea6\u4f30\u6e2c(MLE)\u4f86\u6c42\u5f97\u3002 \u5c0d\u4e00\u500b\u8a9e\u53e5\u7684\u91cd\u8981\u6027\u4f30\u6e2c\u662f\u900f\u904e\u8a08\u7b97\u5728\u8a9e\u53e5\u4e2d\u6240\u51fa\u73fe\u7684\u8a5e\u7684\u689d\u4ef6\u6a5f\u7387\u4e4b\u4e58\u7a4d\uff0c\u901a\u5e38\u63a1\u7528 \u4e8c\u9023(Bigram)\u8207\u4e09\u9023(Trigram)\u8a9e\u8a00\u6a21\u578b\u3002 \u70ba\u4e86\u907f\u514d\u5728\u8a08\u7b97\u6642\u56e0\u8a9e\u53e5\u9577\u5ea6\u7684\u5f71\u97ff\uff0c\u900f\u904e\u8a72\u8a9e\u53e5\u9577\u5ea6\u5c07\u5176\u96d9\u9023\u8a9e\u8a00\u6a21\u578b\u5206\u6578\u9032\u884c\u6b63\u898f \u65bd\u51f1\u6587 \u7b49 \u5316\u4e26\u505a\u70ba\u53e6\u4e00\u9805\u7279\u5fb5\u3002 C. \u5c08\u6709\u540d\u8a5e(Named Entities)\u500b\u6578 \u6839\u64da\u5c08\u6709\u540d\u8a5e\u8a5e\u5178(Lexicon)\u8a08\u7b97\u8a9e\uf906\u4e2d\u7684\u8a5e\u8207\u5c08\u6709\u540d\u8a5e\u8a5e\u5178\u91cd\u8907\u7684\u6578\u91cf\uff1b\u5176\u4e3b\u8981\u60f3\u6cd5\u662f \u542b\u62ec\u6108\u591a\u5c08\u6709\u540d\u8a5e\u7684\u8a9e\uf906\u6108\u53ef\u80fd\u70ba\u91cd\u8981\u8a9e\uf906\u3002\u800c\u5c08\u6709\u540d\u8a5e\u5247\u5305\u542b\u516c\u53f8\u540d\u7a31\u3001 \u5730\u9ede\u3001\u4eba\u540d \u4ee5\u53ca\u6642\u9593\u7b49\u3002 D. \u505c\u7528\u8a5e(Stop Words)\u500b\u6578 \u8a08\u7b97\u8a9e\uf906\u4e2d\u6240\u5305\u542b\u505c\u7528\u8a5e\u7684\u6578\u91cf\uff0c\u5982\u4e2d\u6587\u8a5e\u7684\"\u4e86\"\u3001\"\u7684\"\u7b49\u8a5e\uff0c\u4ee5\u53ca\u82f1\u6587\u8a5e\u5982\"a\"\u3001 \"the\"\u7b49\u8a5e\uff0c\u5373\u4f7f\u51fa\u73fe\u7684\u983b\uf961\u5f88\u9ad8\uff0c\u4f46\u901a\u5e38\uf967\u5177\u6709\u592a\u591a\u8cc7\u8a0a\uff0c\u56e0\u6b64\u5728\u6aa2\uf96a\u904e\u7a0b\u4e2d\u7d93\u5e38\u88ab \uf984\u9664\uff0c\uf967\uf99c\u5165\u641c\u5c0b\u7684\u8003\u616e\u7bc4\u570d\u3002 5.3 \u95dc\u806f\u7279\u5fb5(Relevance Features) \u901a\u5e38\u70ba\uf92d\u81ea\uf967\u540c\u6587\u4ef6\u6458\u8981\u6a21\u578b\u6240\u7522\u751f\u7684\u6458\u8981\u7279\u5fb5\u5206\u6578\uff0c\u5982\u4ee5\u7d71\u8a08\u503c\u70ba\u57fa\u790e\u7684\u5411\u91cf\u7a7a\u9593\u6a21 \u578b(Vector Space Model, VSM)\u3001\u4ee5\u5716\u8ad6\u70ba\u57fa\u790e\u7684\u99ac\u53ef\u592b\u96a8\u6a5f\u6f2b\u6b65(Markov Random Walk, MRW)\u4ee5\u53ca\u4ee5\u6a5f\u7387\u751f\u6210\u6a21\u578b\u70ba\u57fa\u790e\u7684\u8a9e\u8a00\u6a21\u578b(Language Model, LM)\u7b49\u3002 6. \u5be6\u9a57\u8a9e\u6599\u53ca\u8a55\u4f30\u65b9\u6cd5 6.1 \u5be6\u9a57\u8a9e\u6599 \u672c\uf941\u6587\u5be6\u9a57\u8a9e\u6599\u70ba\u516c\u8996\u65b0\u805e\u8a9e\u6599(Mandarin Chinese Broadcast News Corpus, MATBN)\uff0c\u7531 \u4e2d\u592e\u7814\u7a76\u9662\u8cc7\u8a0a\u6240\u8207\u516c\u5171\u96fb\u8996\u53f0\u5408\u4f5c\u9304\u88fd\u6574\u7406\uff0c\u5176\uf93f\u88fd\u5167\u5bb9\u70ba\u6bcf\u5929\u4e00\u500b\u5c0f\u6642\u7684\u516c\u8996\u665a\u9593 \u65b0\u805e\u6df1\u5ea6\u5831\u5c0e(Wang et al., 2005)\u3002\u6211\u5011\u9078\u53d6\u5176\u4e2d\u5f9e 2001 \uf98e 11 \u6708\u81f3 2002 \uf98e 8 \u6708\u5171 205 \u7bc7 \u7684\u65b0\u805e\u5831\u5c0e\uff0c\u4e26\u5340\u5206\u70ba\u767c\u5c55\u96c6(185 \u7bc7)\u8207\u6e2c\u8a66\u96c6(20 \u7bc7)\u5169\u500b\u90e8\u5206\u3002\u5168\u90e8 205 \u7bc7\u8a9e\u97f3\u6587\u4ef6\u9577 \u5ea6\u7d04\u70ba 7.5 \u500b\u5c0f\u6642\u3002\u6211\u5011\u5c07\u8a9e\u97f3\u6587\u4ef6\u9032\u884c\u4eba\u5de5\u5207\u97f3\u8655\u7406\uff0c\u5f97\u5230\u771f\u6b63\u542b\u6709\u8b1b\u8a71\u5167\u5bb9\u7684\u97f3\u8a0a \u6bb5\u843d\uff0c\u518d\u900f\u904e\u81ea\u52d5\u8a9e\u97f3\u8fa8\u8b58\u7cfb\u7d71\u9032\u884c\u8f49\u5beb\uff0c\u6211\u5011\u7a31\u4e4b\u70ba\u8a9e\u97f3\u6587\u4ef6(Spoken Document, SD)\uff0c \u542b\u6709\u8a9e\u97f3\u8fa8\u8b58\u932f\u8aa4\u8207\u8a9e\u53e5\u908a\u754c\u5075\u6e2c\u932f\u8aa4\u3002\u6b64\u5916\uff0c\u6211\u5011\u4ea6\u5c07\u6b64 205 \u7bc7\u8a9e\u97f3\u6587\u4ef6\u900f\u904e\u4eba\u5de5\u807d \u5beb\u7684\u65b9\u5f0f\u7522\u751f\u51fa\u6c92\u6709\u8fa8\u8b58\u932f\u8aa4\u7684\u5c0d\u61c9\u6587\u5b57\u5167\u5bb9\uff0c\u6211\u5011\u7a31\u4e4b\u70ba\u6587\u5b57\u6587\u4ef6(Text Document, TD)\u3002\u6bcf\u4e00\u7bc7\u6587\u5b57\u6587\u4ef6\u7686\u6709\u4e09\u4f4d\u6a19\u8a18\u5c08\u5bb6\u6240\u63d0\u4f9b\u7684\u4e09\u4efd\u6458\u8981\u7d50\u679c\uff0c\u6211\u5011\u5c07\u6b64\u4f5c\u70ba\u8a9e\u97f3\u6587 \u4ef6\u8207\u6587\u5b57\u6587\u4ef6\u7684\u6b63\u78ba\u6458\u8981\u7b54\u6848\u3002\u900f\u904e\u6bd4\u8f03\u8a9e\u97f3\u6587\u4ef6\u548c\u6587\u5b57\u6587\u4ef6\u7684\u6458\u8981\u6548\u80fd\uff0c\u6211\u5011\u53ef\u4ee5\u89c0 \u5bdf\u8a9e\u97f3\u8fa8\u8b58\u932f\u8aa4\u5c0d\u65bc\u5404\u7a2e\u6458\u8981\u65b9\u6cd5\u7684\u5f71\u97ff\u3002\u672c\u7814\u7a76\u7684\u80cc\u666f\u8a9e\u8a00\u6a21\u578b\u8a13\u7df4\u8a9e\u6599\u53d6\u6750\u81ea 2001 \u81f3 2002 \uf98e\u7684\u4e2d\u592e\u793e\u65b0\u805e\u6587\u5b57\u8a9e\u6599(Central News Agency, CNA)\uff0c\u4e26\u4e14\u4ee5 SRI \u8a9e\u8a00\u6a21\u578b\u5de5 \u5177\u8a13\u7df4\u51fa\u7d93\u5e73\u6ed1\u5316\u7684\u55ae\u9023\u8a9e\u8a00\u6a21\u578b\u3002\u6b64\u5916\uff0c\u672c\u8ad6\u6587\u8490\u96c6 2002 \uf98e\u4e2d\u592e\u901a\u8a0a\u793e\u7684 101,268 \u7bc7 \u540c\u6642\u671f\u65b0\u805e\u6587\u4ef6\u4f5c\u70ba\u8a5e\u8868\u793a\u6cd5\u4ee5\u53ca\u8a9e\u53e5\u8868\u793a\u6cd5\u7684\u8a13\u7df4\u8a9e\u6599\u4ee5\u53ca\u865b\u64ec\u95dc\u806f\u6587\u4ef6\u3002\u6211\u5011\u8a2d\u5b9a \u6458\u8981\u6bd4\u4f8b\u70ba 10%\uff0c\u5176\u5b9a\u7fa9\u662f\u6458\u8981\u5b57\u6578\u5360\u6574\u7bc7\u6587\u4ef6\u5b57\u6578\u7684\u6bd4\u4f8b\uff0c\u5176\u8a73\u7d30\u7684\u7d71\u8a08\u8cc7\u8a0a\u5982\u8868 1 \u6240\u793a\u3002 \u8868 1. \u5ee3\u64ad\u65b0\u805e\u6587\u4ef6\u4e4b\u7d71\u8a08\u8cc7\u8a0a \u8a13\u7df4\u96c6 \u6e2c\u8a66\u96c6 \u7d00\u9304\u6642\u6bb5 2001/11/07-2002/08/22 2002/01/24-2002/08/20 \u6587\u4ef6\u500b\u6578 185 20 \u6587\u4ef6\u5e73\u5747\u6301\u7e8c\u79d2\u6578 129.4 141.3 \u6587\u4ef6\u5e73\u5747\u8a5e\u500b\u6578 326.0 290.3 \u6587\u4ef6\u5e73\u5747\u8a9e\u53e5\u500b\u6578 20.0 23.3 \u6587\u4ef6\u5e73\u5747\u8a5e\u932f\u8aa4\u7387 38.0% 39.4% 6.2 \u8a55\u4f30\u65b9\u6cd5 \u672c\u8ad6\u6587\u63a1\u7528 ROUGE \u4f5c\u70ba\u6587\u4ef6\u6458\u8981\u7684\u8a55\u4f30\u65b9\u5f0f\u3002\u8a72\u65b9\u6cd5\u662f\u8a08\u7b97\u81ea\u52d5\u6458\u8981\u7d50\u679c\u8207\u4eba\u5de5\u6458\u8981 \u4e4b\u9593\u7684\u91cd\u758a\u55ae\u4f4d\u5143(Overlap Units)\u6578\u76ee\u5360\u4eba\u5de5\u6458\u8981\u9577\u5ea6\u7684\u6bd4\u4f8b\u3002\u7531\u65bc\u8a72\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\u4e14\u9069\u5408\u7528\u65bc\u591a\u4efd\u4eba\u5de5\u6458\u8981\u7684\u8a55\u4f30\u3002\u6211\u5011\u4f7f \u7528\u4e86\u8f03\u666e\u904d\u7684 ROUGE-1(Unigram)\u3001ROUGE-2(Bigram)\u4ee5\u53ca ROUGE-L(Longest Common Subsequence, LCS)\u5206\u6578\uff0c\u5176\u4e2d ROUGE-1 \u662f\u8a55\u4f30\u81ea\u52d5\u6458\u8981\u7684\u8a0a\u606f\u91cf\uff0cROUGE-2 \u662f\u8a55\u4f30\u81ea \u52d5\u6458\u8981\u7684\u6d41\u66a2\u6027\uff0cROUGE-L \u662f\u6700\u9577\u5171\u540c\u5b57\uf905\u3002ROUGE-N \u662f\u81ea\u52d5\u6458\u8981\u548c\u4eba\u5de5\u6458\u8981\u4e4b\u9593 N \u9023\u8a5e(N-gram)\u7684\u53ec\u56de\u7387\uff0c\u4eba\u5de5\u6a19\u8a18\u7684\u53c3\u8003\u6458\u8981\u70ba\u4e00\u96c6\u5408 R\uff0c\u6545 ROUGE-N \u8a08\u7b97\u516c\u5f0f\u5982\u4e0b(Lin, 2004)\uff1a ROUGE \u2211 \u2211 \u2208 \u2208 \u2211 \u2211 \u2208 \u2208 (18) \u5176\u4e2d sum \u70ba\u4eba\u5de5\u6458\u8981\u96c6\u5408 R \u4e2d\u7684\u4efb\u4e00\u500b\u6458\u8981\uff0cN \u4ee3\u8868\u8a5e\u5f59\u4e32\u4e4b\u9023\u7e8c\u9577\u5ea6\uff0c\u800c \u662f N \u9023\u8a5e\u540c\u6642\u51fa\u73fe\u65bc\u81ea\u52d5\u6458\u8981\u8207\u4eba\u5de5\u6458\u8981\u7684\u6700\u5927\u6578\u91cf\u3002ROUGE-L \u7684\u8a08\u7b97\u65b9\u5f0f\u8207 ROUGE-N \u76f8\u4f3c\uff0c\u4f46\u524d\u8005\u50c5\u8003\u616e\u81ea\u52d5\u6458\u8981\u8207\u53c3\u8003\u6458\u8981\u7684\u6700\u9577\u5171\u540c\u5b57\u4e32\u3002 7. \u5be6\u9a57\u7d50\u679c 7.1 \u57fa\u790e\u6587\u4ef6\u6458\u8981\u4e4b\u5be6\u9a57\u7d50\u679c \u8868 2 \u70ba\u6e2c\u8a66\u96c6\u4e2d\u7684\u6587\u5b57\u6587\u4ef6(TD)\u8207\u8a9e\u97f3\u6587\u4ef6(SD)\u5728 ROUGE-1\u3001ROUGE-2 \u4ee5\u53ca ROUGE-L \u8a55\u4f30\u4e0b\u7684\u6458\u8981\u7d50\u679c\uff1b\u5728\u6b64\u6211\u5011\u9032\u884c\u5404\u5f0f\u7684\u57fa\u790e\u6458\u8981\u65b9\u6cd5\u7684\u6bd4\u8f03\uff0c\u5305\u542b\u524d\u5c0e\u65b9\u6cd5(LEAD)\u3001 \u5411\u91cf\u7a7a\u9593\u6a21\u578b(VSM)\u3001\u6700\u5927\u908a\u969b\u95dc\u806f\u6cd5(MMR)\u3001\u6f5b\u85cf\u8a9e\u610f\u5206\u6790(LSA)\u3001\u55ae\u9023\u8a9e\u8a00\u6a21\u578b(ULM)\u3001 \u95dc\u806f\u6a21\u578b(RM)\u3001Okapi Best Match 25(BM25)\u3001\u8a5e\u6b0a\u91cd-\u9006\u5411\u6587\u4ef6\u983b\u7387(TW-IDF)\u4ee5\u53ca\u99ac\u53ef\u592b \u96a8\u6a5f\u6f2b\u6b65(MRW)\u3002\u9996\u5148\u5728 TD \u7684\u5be6\u9a57\u4e2d\uff0cRM \u7684\u6458\u8981\u6548\u679c\u662f\u6240\u6709\u6a21\u578b\u4e2d\u6700\u4f73\u7684\uff0c\u8868\u793a\u4f7f \u7528\u984d\u5916\u7684\u95dc\u806f\u6587\u4ef6\u53ef\u4ee5\u6709\u6548\u5730\u5f4c\u88dc\u8a9e\u53e5\u5167\u5bb9\u7684\uf967\u8db3\uff0c\u63d0\u9ad8\u8a9e\u53e5\u7684\u4f30\u6e2c\u80fd\u529b\u3002\u5176\u6b21\u70ba BM25\uff0c \u6211\u5011\u8a8d\u70ba\u5728\u6587\u4ef6\u6458\u8981\u7684\u554f\u984c\u4e2d\uff0c\u8a5e\u5f59\u7684\u983b\u7387(TF)\u3001\u53cd\u6587\u4ef6\u983b\u7387(IDF)\u4ee5\u53ca\u6587\u4ef6\u9577\u5ea6\u7684\u6b63\u898f \u5316(Normalized)\u662f\u91cd\u8981\u4e14\u4e0d\u53ef\u6216\u7f3a\u7684\u7279\u5fb5\u8cc7\u8a0a\u3002ULM \u7121\u8ad6\u5728 TD \u6216\u662f SD \u4e0a\u7684\u6458\u8981\u6210\u6548\u7686 \u65bd\u51f1\u6587 \u7b49 \u512a\u65bc\u5716\u8ad6\u5f0f\u6a21\u578b TW-IDF \u8207 MRW\u3002TW-IDF \u5728\u8a08\u7b97\u8a5e\u983b(TF)\u6642\uff0c\u591a\u8003\u616e\u4e86\u4e0a\u4e0b\u6587(Context) \u7684\u8cc7\u8a0a\uff0c\u800c MRW \u5728\u8a08\u7b97\u91cd\u8981\u8a9e\u53e5\u6642\uff0c\u9664\u4e86\u4f7f\u7528\u5176\u5b83\u8a9e\u53e5\u7684\u5206\u6578\u4e4b\u5916\uff0c\u4ea6\u8003\u616e\u5230\u8a9e\u53e5\u5f7c \u6b64\u4e4b\u9593\u7684\u76f8\u95dc\u5ea6\u4f5c\u70ba\u6b0a\u91cd\u4f86\u8abf\u6574\uff0c\u56e0\u6b64\u5169\u8005\u6548\u679c\u7686\u6703\u8f03\u50c5\u8003\u616e\u8a5e\u983b\u7684 VSM \u70ba\u4f73\u3002MMR \u5728\u9032\u884c\u8a9e\u53e5\u9078\u53d6\u6642\u591a\u8003\u616e\u4e86\u5197\u9918\u8cc7\u8a0a\uff0c\u56e0\u6b64\u6458\u8981\u6548\u679c\u8f03 VSM \u4f73\u3002 \u8868 2. \u57fa\u790e\u5be6\u9a57\u65bc\u6587\u5b57\u6587\u4ef6\u8207\u8a9e\u97f3\u6587\u4ef6\u4e4b\u6458\u8981\u7d50\u679c \u6587\u5b57\u6587\u4ef6(TD) \u8a9e\u97f3\u6587\u4ef6(SD) \u65b9\u6cd5 ROUGE-1 ROUGE-2 ROUGE-L ROUGE-1 ROUGE-2 ROUGE-L LEAD 0.312 0.196 0.278 0.254 0.117 0.220 VSM 0.347 0.228 0.290 0.343 0.189 0.288 MMR 0.365 0.242 0.316 0.360 0.206 0.309 LSA 0.362 0.233 0.316 0.345 0.201 0.301 ULM 0.411 0.299 0.362 0.364 0.218 0.313 RM 0.458 0.345 0.408 0.384 0.236 0.330 BM25 0.422 0.317 0.380 0.394 0.251 0.341 TW-IDF 0.374 0.260 0.317 0.322 0.164 0.270 MRW 0.415 0.296 0.357 0.339 0.194 0.289 LSA \u5728\u6f5b\u85cf\u8a9e\u610f\u7a7a\u9593\u8a08\u7b97\u6587\u4ef6\u8207\u8a9e\u53e5\u7684\u9918\u5f26\u76f8\u4f3c\u5ea6\uff0c\u5176\u7d50\u679c\u4ea6\u986f\u793a\u8f03 VSM \u70ba\u4f73\u3002 \u800c VSM \u6bcf\u500b\u8a5e\u5f59\u6240\u69cb\u6210\u7684\u5411\u91cf\u7dad\u5ea6\u7686\u70ba\u7368\u7acb\uff0c\u56e0\u6b64\u7121\u6cd5\u5f97\u77e5\u51fa\u6587\u4ef6\u4e2d\u8a5e\u5f59\u4e4b\u9593\u7684\u95dc\u806f \u6027\uff0c\u4f7f\u5f97\u9032\u884c\u6587\u4ef6\u76f8\u4f3c\u5ea6\u7684\u6bd4\u5c0d\u6642\u53ef\u80fd\u9020\u6210\u8aa4\u5224\u7684\u60c5\u6cc1\u3002 \u5728 7.2 \u8a5e\u8868\u793a\u6cd5\u8207\u8a9e\u53e5\u8868\u793a\u6cd5\u65bc\u7bc0\u9304\u5f0f\u8a9e\u97f3\u6587\u4ef6\u6458\u8981\u4e4b\u5be6\u9a57\u7d50\u679c \u5728\u6b64\u6211\u5011\u5229\u7528\u76ee\u524d\u5169\u7a2e\u6700\u5148\u9032\u7684\u8a5e\u8868\u793a\u6cd5\u2500\u9023\u7e8c\u578b\u8a5e\u888b\u6a21\u578b(CBOW)\u548c\u8df3\u8e8d\u5f0f\u6a21\u578b(SG)\uff0c \u8207\u6700\u5148\u9032\u7684\u5169\u7a2e\u8a9e\u53e5\u8868\u793a\u6cd5\u2500\u5206\u6563\u5f0f\u5132\u5b58\u6a21\u578b(PV-DM) \u548c\u5206\u6563\u5f0f\u8a5e\u888b\u6a21\u578b(PV-DBOW) \u4e4b\u6280\u8853\u4f86\u5f9e\u4e8b\u8a9e\u97f3\u6587\u4ef6\u6458\u8981\uff1b\u5be6\u9a57\u5171\u5206\u4e09\u7d44\u4f86\u9032\u884c\uff0c\u5206\u5225\u7d50\u5408\u65bc\u9918\u5f26\u76f8\u4f3c\u5ea6(Cosine \u7bc0\u9304\u5f0f\u8a9e\u97f3\u6587\u4ef6\u6458\u8981\u4f7f\u7528\u8868\u793a\u6cd5\u5b78\u7fd2\u6280\u8853 79 \u8868 3. \u8a5e\u8868\u793a\u6cd5\u7d50\u5408\u65bc\u9918\u5f26\u76f8\u4f3c\u5ea6\u4e4b\u6458\u8981\u7d50\u679c \u6587\u5b57\u6587\u4ef6(TD) \u8a9e\u97f3\u6587\u4ef6(SD) \u65b9\u6cd5 ROUGE-1 ROUGE-2 ROUGE-L ROUGE-1 ROUGE-2 ROUGE-L CBOW 0.402 0.280 0.349 0.377 0.228 0.327 SG 0.401 0.265 0.347 0.361 0.214 0.312 \u9996\u5148\uff0c\u6211\u5011\u5c07\u8a5e\u8868\u793a\u6cd5\u7d50\u5408\u65bc\u9918\u5f26\u76f8\u4f3c\u5ea6(Cosine Similarity)\u4f5c\u70ba\u9078\u53d6\u6458\u8981\u8a9e\u53e5\u7684\u65b9 \u6cd5\uff0c\u5176\u7d50\u679c\u793a\u65bc\u8868 3\u3002\u5f9e\u5be6\u9a57\u7d50\u679c\u4e2d\u89c0\u5bdf\u5230\uff0c\u7531\u65bc\u9019\u5169\u7a2e\u8a5e\u8868\u793a\u6cd5\u5404\u6709\u8457\u4e0d\u540c\u7684\u6a21\u578b\u7d50 \u69cb\u8207\u5b78\u7fd2\u65b9\u5f0f\uff0c\u56e0\u6b64\u5728\u6587\u5b57\u6587\u4ef6(TD)\u6216\u662f\u8a9e\u97f3\u6587\u4ef6(SD)\u4e2d\uff0c\u8a72\u5169\u7a2e\u6a21\u578b\u7684\u6458\u8981\u6210\u6548\u6709\u7a0d \u5fae\u7684\u5dee\u7570\u3002\u6839\u64da TD \u7684\u7d50\u679c\u986f\u793a\uff0cCBOW \u7684\u6458\u8981\u6548\u80fd\u8f03 SG \u4f73\uff0c\u5728 SD \u4e2d\u4ecd\u4fdd\u6301\u76f8\u540c\u7684 \u60c5\u6cc1\u3002\u5118\u7ba1\u8a72\u5169\u7a2e\u8a5e\u8868\u793a\u6cd5\u7686\u512a\u65bc\u5411\u91cf\u7a7a\u9593\u6a21\u578b(VSM)\u8207\u6f5b\u85cf\u8a9e\u610f\u5206\u6790(LSA)\uff0c\u537b\u50c5\u9054\u5230 \u8a5e\u6b0a\u91cd-\u9006\u5411\u6587\u4ef6\u983b\u7387(TW-IDF)\u5dee\u4e0d\u591a\u7684\u6c34\u5e73\uff0c\u800c\u4e14\u5728 SD \u7684\u60c5\u6cc1\u4e0b\u7684\u8868\u73fe SG \u4e0d\u53ca\u55ae\u9023 \u8a9e\u8a00\u6a21\u578b(ULM)(\u8868 2)\u3002 \u8868 4. \u8a9e\u53e5\u8868\u793a\u6cd5\u7d50\u5408\u65bc\u9918\u5f26\u76f8\u4f3c\u5ea6\u4e4b\u6458\u8981\u7d50\u679c \u6587\u5b57\u6587\u4ef6(TD) \u8a9e\u97f3\u6587\u4ef6(SD) \u65b9\u6cd5 ROUGE-1 ROUGE-2 ROUGE-L ROUGE-1 ROUGE-2 ROUGE-L PV-DM 0.429 0.313 0.382 0.387 0.236 0.335 PV-DBOW 0.398 0.277 0.348 0.368 0.227 0.329 \u540c\u6a23\u5730\uff0c\u6211\u5011\u5c07\u8a9e\u53e5\u8868\u793a\u6cd5\u7d50\u5408\u65bc\u9918\u5f26\u76f8\u4f3c\u5ea6\u4f5c\u70ba\u9078\u53d6\u6458\u8981\u8a9e\u53e5\u7684\u65b9\u6cd5\uff0c\u5176\u7d50\u679c\u793a \u65bc\u8868 4\u3002\u5728 TD \u7684\u7d50\u679c\u4e2d\uff0cPV-DM \u8207 PV-DBOW \u8a72\u5169\u7a2e\u8a9e\u53e5\u8868\u793a\u6cd5\u7684\u6458\u8981\u6548\u679c\u5206\u5225\u8d85\u8d8a CBOW \u53ca SG \u8a5e\u8868\u793a\u6cd5\u6a21\u578b(\u8868 3) \u3002PV-DM \u6458\u8981\u6210\u6548\u8f03\u50b3\u7d71\u7684\u99ac\u53ef\u592b\u96a8\u6a5f\u6f2b\u6b65(MRW)\u4f73\uff0c \u4f46\u8f03 BM25 \u5dee\u3002\u800c\u5728 SD \u7684\u7d50\u679c\u4e2d\uff0c\u5169\u7a2e\u8a9e\u53e5\u8868\u793a\u6cd5\u7684\u6458\u8981\u6210\u6548\u6bd4\u8d77\u8a5e\u8868\u793a\u6cd5\u6c92\u6709\u592a\u5927 \u7684\u9032\u6b65\uff0c\u6211\u5011\u8a8d\u70ba\u8a9e\u53e5\u8868\u793a\u6cd5\u642d\u914d\u9918\u5f26\u76f8\u4f3c\u5ea6\u9078\u53d6\u8a9e\u53e5\u7684\u65b9\u5f0f\u4ea6\u53d7\u8a9e\u97f3\u8fa8\u8b58\u7684\u5f71\u97ff\u3002 \u8868 5. \u8a5e\u8868\u793a\u6cd5\u7d50\u5408\u65bc\u99ac\u53ef\u592b\u96a8\u6a5f\u6f2b\u6b65\u4e4b\u6458\u8981\u7d50\u679c \u6587\u5b57\u6587\u4ef6(TD) \u8a9e\u97f3\u6587\u4ef6(SD) \u65b9\u6cd5 ROUGE-1 ROUGE-2 ROUGE-L ROUGE-1 ROUGE-2 ROUGE-L CBOW 0.436 0.310 0.384 0.393 0.246 0.346 SG 0.316 0.283 0.351 0.372 0.233 0.325 \u5728\u7b2c\u4e8c\u7d44\u5be6\u9a57\u4e2d\uff0c\u6211\u5011\u5c07\u8a5e\u8868\u793a\u6cd5\u7d50\u5408\u99ac\u53ef\u592b\u96a8\u6a5f\u6f2b\u6b65(MRW)\u4ee5\u5c0d\u8a9e\u53e5\u9032\u884c\u9078\u53d6\uff0c \u5176\u7d50\u679c\u5448\u73fe\u5728\u8868 5\u3002\u5f9e\u7d50\u679c\u4e2d\u53ef\u4ee5\u89c0\u5bdf\u5230\uff0c\u7121\u8ad6\u5728 TD \u6216\u662f SD \u4e0a\uff0c\u76f8\u8f03\u65bc\u540c\u6a23\u4ee5\u8a5e\u8868\u793a \u6cd5\u7684\u6280\u8853\u7d50\u5408\u9918\u5f26\u76f8\u4f3c\u5ea6\u7684\u65b9\u6cd5\uff0c\u4f7f\u7528\u8a72\u65b9\u6cd5\u6311\u9078\u8a9e\u53e5\u7684\u6458\u8981\u6210\u6548\u7686\u512a\u65bc\u4ee5\u9918\u5f26\u76f8\u4f3c\u5ea6 \u8868 6. \u8a9e\u53e5\u8868\u793a\u6cd5\u7d50\u5408\u65bc\u99ac\u53ef\u592b\u96a8\u6a5f\u6f2b\u6b65\u4e4b\u6458\u8981\u7d50\u679c \u6587\u5b57\u6587\u4ef6(TD) \u8a9e\u97f3\u6587\u4ef6(SD) \u65b9\u6cd5 ROUGE-1 ROUGE-2 ROUGE-L ROUGE-1 ROUGE-2 ROUGE-L PV-DM 0.446 0.343 0.400 0.395 0.253 0.347 PV-DBOW 0.451 0.336 0.398 0.387 0.243 0.337 \u540c\u6a23\u5730\uff0c\u6211\u5011\u4ee5\u8a9e\u53e5\u8868\u793a\u6cd5\u7d50\u5408\u99ac\u53ef\u592b\u96a8\u6a5f\u6f2b\u6b65(MRW)\u5c0d\u8a9e\u53e5\u9032\u884c\u9078\u53d6\uff0c\u5176\u7d50\u679c\u5c55 \u793a\u65bc\u8868 6\u3002\u5f9e\u7d50\u679c\u4e2d\u767c\u73fe\u5230\uff0c\u7121\u8ad6\u5728 TD \u6216\u662f SD \u4e0a\uff0c\u8a72\u65b9\u6cd5\u7684\u6458\u8981\u6210\u6548\uff0c\u986f\u8457\u5730\u512a\u8d8a\u4ee5 \u8a5e\u3001\u8a9e\u53e5\u8868\u793a\u6cd5\u7d50\u5408\u65bc\u9918\u5f26\u76f8\u4f3c\u5ea6(\u8868 3 \u548c 4)\u4e4b\u9078\u53d6\u6458\u8981\u8a9e\u53e5\u65b9\u6cd5\uff0c\u4ea6\u8d85\u8d8a\u4ee5\u8a5e\u8868\u793a\u6cd5\u7d50 \u5408\u65bc\u99ac\u53ef\u592b\u96a8\u6a5f\u6f2b\u6b65\u7684\u65b9\u5f0f(\u8868 5)\u3002\u5728 TD \u5be6\u9a57\u4e2d\uff0c\u5118\u7ba1\u8a72\u5169\u7a2e\u8a5e\u8868\u793a\u6cd5\u7684\u6458\u8981\u6210\u6548\u8f03 BM25 \u4f73\uff0c\u4f46\u7686\u4e0d\u53ca\u95dc\u806f\u6a21\u578b(RM)\u3002\u7136\u800c\u65bc SD \u5be6\u9a57\u4e2d\uff0cPV-DM \u7684\u6458\u8981\u6210\u6548\u8d85\u8d8a\u6240\u6709\u7684 \u50b3\u7d71\u6587\u4ef6\u6458\u8981\u6a21\u578b\u3002 \u8868 7. \u8a5e\u8868\u793a\u6cd5\u7d50\u5408\u65bc\u6587\u4ef6\u76f8\u4f3c\u5ea6\u91cf\u503c\u4e4b\u6458\u8981\u7d50\u679c \u6587\u5b57\u6587\u4ef6(TD) \u8a9e\u97f3\u6587\u4ef6(SD) \u65b9\u6cd5 ROUGE-1 ROUGE-2 ROUGE-L ROUGE-1 ROUGE-2 ROUGE-L CBOW 0.444 0.329 0.386 0.372 0.221 0.314 SG 0.436 0.323 0.385 0.343 0.197 0.295 \u5728\u6700\u5f8c\u4e00\u7d44\u5be6\u9a57\u4e2d\uff0c\u6211\u5011\u63a2\u8a0e\u4ee5\u8a5e\u8868\u793a\u6cd5\u7d50\u5408\u65bc\u6587\u4ef6\u76f8\u4f3c\u5ea6\u91cf\u503c(DLM)\u5c0d\u8a9e\u53e5\u9032\u884c \u9078\u53d6\uff0c\u5176\u7d50\u679c\u5c55\u793a\u65bc\u8868 7\u3002\u6211\u5011\u5c07\u7d50\u679c\u8207\u540c\u6a23\u4ee5\u8a5e\u8868\u793a\u6cd5\u7d50\u5408\u9918\u5f26\u76f8\u4f3c\u5ea6(\u8868 3)\u4ee5\u53ca\u99ac\u53ef \u592b\u96a8\u6a5f\u6f2b\u6b65\u7684\u65b9\u6cd5(\u8868 5)\u9032\u884c\u6bd4\u8f03\u3002\u5f9e TD \u5be6\u9a57\u7d50\u679c\u4e2d\u53ef\u4ee5\u89c0\u5bdf\u5230\uff0c\u6587\u4ef6\u76f8\u4f3c\u5ea6\u91cf\u503c\u5145\u5206 \u5730\u904b\u7528\u8a5e\u8868\u793a\u6cd5\u65bc\u6587\u4ef6\u6458\u8981\uff0c\u8868\u73fe\u986f\u7136\u8f03\u4f73\u3002\u6211\u5011\u4ea6\u6ce8\u610f\u5230 SG \u7684\u6458\u8981\u6210\u6548\u5e7e\u4e4e\u63a5\u8fd1 CBOW\u3002\u7136\u800c\u65bc TD \u8207 SD \u7684\u5be6\u9a57\u4e2d\uff0c\u8a72\u5169\u7a2e\u8a5e\u8868\u793a\u6cd5\u7686\u4ecd\u4e0d\u53ca RM \u7684\u6458\u8981\u6210\u6548\u3002 \u8868 8. \u8a9e\u53e5\u8868\u793a\u6cd5\u7d50\u5408\u65bc\u6587\u4ef6\u76f8\u4f3c\u5ea6\u91cf\u503c\u4e4b\u6458\u8981\u7d50\u679c \u6587\u5b57\u6587\u4ef6(TD) \u8a9e\u97f3\u6587\u4ef6(SD) \u65b9\u6cd5 ROUGE-1 ROUGE-2 ROUGE-L ROUGE-1 ROUGE-2 ROUGE-L PV-DM 0.480 0.375 0.430 0.384 0.240 0.333 PV-DBOW 0.433 0.323 0.384 0.364 0.236 0.321 \u540c\u6a23\u5730\uff0c\u6211\u5011\u4ee5\u8a9e\u53e5\u8868\u793a\u6cd5\u65bc\u6587\u4ef6\u76f8\u4f3c\u5ea6\u91cf\u503c\u5c0d\u8a9e\u53e5\u9032\u884c\u9078\u53d6\uff0c\u5176\u7d50\u679c\u986f\u793a\u5728\u8868 8\u3002 \u5f9e \u7bc0\u9304\u5f0f\u8a9e\u97f3\u6587\u4ef6\u6458\u8981\u4f7f\u7528\u8868\u793a\u6cd5\u5b78\u7fd2\u6280\u8853 81 7.3 \u5229\u7528\u8072\u5b78\u7279\u5fb5\u7d50\u5408\u652f\u6301\u5411\u91cf\u6a5f\u65bc\u6587\u4ef6\u6458\u8981 \u672c\u8ad6\u6587\u6240\u4f7f\u7528\u7684\u8a9e\u97f3\u8a9e\u6599\u662f\u7d93\u7531\u4eba\u5de5\u5207\u97f3\uff0c\u4e0d\u6703\u6709\u8a9e\u97f3\u908a\u754c\u932f\u8aa4\u7684\u554f\u984c\uff0c\u50c5\u9808\u8003\u91cf\u8a9e\u97f3 \u8fa8\u8b58\u932f\u8aa4\u65bc\u6587\u4ef6\u6458\u8981\u7684\u5f71\u97ff\uff0c\u56e0\u6b64\u6587\u5b57\u6587\u4ef6(TD)\u8207\u8a9e\u97f3\u6587\u4ef6(SD)\u5169\u8005\u6703\u6709\u76f8\u540c\u7684\u8a9e\u97f3\u908a \u754c\uff0c\u800c\u62bd\u53d6\u51fa\u7684\u97fb\u5f8b\u7279\u5fb5\u4ea6\u6703\u662f\u4e00\u81f4\u3002\u672c\u8ad6\u6587\u7e3d\u5171\u4f7f\u7528 12 \u7a2e\u4e0d\u540c\u7684\u6458\u8981\u7279\u5fb5\u4f5c\u70ba\u652f\u6301\u5411 \u91cf\u6a5f(Support Vector Machine, SVM)\u7684\u8f38\u5165\uff0c\u53ef\u6982\u7565\u5206\u6210\u4e09\u5927\u985e\uff0c\u5206\u5225\u70ba\u8a5e\u5f59\u7279\u5fb5(Lexical Features)\u3001\u97fb\u5f8b\u7279\u5fb5(Prosodic Features)\u4ee5\u53ca\u95dc\u806f\u7279\u5fb5(Relevance Features)\uff0c\u8a73\u7d30\u7684\u7279\u5fb5\u8cc7 \u8a0a\u5982\u8868 9 \u6240\u793a\u3002 \u8868 9. \u5be6\u9a57\u63a1\u7528\u4e4b\u5404\u5f0f\u7279\u5fb5 \u97fb\u5f8b\u7279\u5fb5(Prosodic Features) \u97f3\u9ad8(Pitch):\u6700\u5927\u3001\u6700\u5c0f\u3001\u5e73\u5747\u3001\u5dee\u503c \u80fd\u91cf(Energy):\u6700\u5927\u3001\u6700\u5c0f\u3001\u5e73\u5747\u3001\u5dee\u503c \u97f3\u6846\u9577\u5ea6(Duration):\u6700\u5927\u3001\u6700\u5c0f\u3001\u5e73\u5747\u3001\u5dee\u503c \u5171\u632f\u5cf0(Formant):\u6700\u5927\u3001\u6700\u5c0f\u3001\u5e73\u5747\u3001\u5dee\u503c \u983b\u8b5c\u5cf0\u503c(Peak):\u6700\u5927\u3001\u6700\u5c0f\u3001\u5e73\u5747\u3001\u5dee\u503c \u8a5e\u5f59\u7279\u5fb5(Lexical Features) \u5c08\u6709\u540d\u8a5e\u500b\u6578(Named Entity) \u505c\u7528\u8a5e\u500b\u6578(Stop Word) \u4e8c\u9023\u8a9e\u8a00\u6a21\u578b\u5206\u6578(Bigram) \u6b63\u898f\u5316\u4e8c\u9023\u8a9e\u8a00\u6a21\u578b\u5206\u6578(Normalized Bigram) \u95dc\u806f\u7279\u5fb5(Relevance Features) \u5411\u91cf\u7a7a\u9593\u6a21\u578b\u5206\u6578(VSM) \u99ac\u53ef\u592b\u96a8\u6a5f\u6f2b\u6b65\u5206\u6578(MRW) \u8a9e\u8a00\u6a21\u578b\u5206\u6578(LM) \u7531\u8868 10 \u4e2d\u5f97\u5230\uff0c\u7121\u8ad6\u5728\u6587\u5b57\u6587\u4ef6(TD)\u6216\u662f\u8a9e\u97f3\u6587\u4ef6(SD)\u4e2d\uff0c\u97fb\u5f8b\u7279\u5fb5(Prosodic Features)\u76f8\u5c0d\u65bc\u5176\u5b83\u5169\u7a2e\u7279\u5fb5\u7522\u751f\u8f03\u70ba\u986f\u8457\u7684\u6458\u8981\u6548\u80fd\uff0c\u56e0\u6b64\u97fb\u5f8b\u7279\u5fb5\u6bd4\u8d77\u5176\u5b83\u5169\u7a2e\u7279 \u5fb5\u66f4\u80fd\u5920\u5224\u65b7\u6458\u8981\u8a9e\u53e5\u7684\u91cd\u8981\u8cc7\u8a0a\u3002\u5728 TD \u5be6\u9a57\u4e2d\uff0c\u8a5e\u5f59\u7279\u5fb5(Lexical Features)\u5728\u9019\u4e09\u7a2e \u6458\u8981\u7279\u5fb5\u4e2d\u7684\u8868\u73fe\u6700\u5dee\uff0c\u5176\u539f\u56e0\u53ef\u80fd\u662f\u8a72\u7279\u5fb5\u63cf\u8ff0\u7684\u662f\u8868\u6dfa(Shallow)\u8a9e\u53e5\u6027\u8cea\uff0c\u5305\u542b\u5c08 \u6709\u540d\u8a5e\u7684\u6578\u91cf\u3001\u505c\u7528\u8a5e\u7684\u6578\u91cf\u4ee5\u53ca\u8a9e\u53e5\u7684\u6d41\u66a2\u6027\uff0c\u6c92\u6709\u8003\u616e\u8a9e\u53e5\u7684\u8a9e\u610f\u5167\u5bb9\uff0c\u56e0\u6b64\u55ae\u6191 \u8a72\u7279\u5fb5\u7121\u6cd5\u9078\u53d6\u51fa\u8f03\u6b63\u78ba\u7684\u6458\u8981\u8a9e\u53e5\u3002\u6b64\u5916\uff0c\u95dc\u806f\u7279\u5fb5(Relevance Features)\u6bd4\u8d77\u8a5e\u5f59\u7279\u5fb5 \u6709\u8f03\u597d\u7684\u6458\u8981\u6210\u6548\u3002\u5728 SD \u5be6\u9a57\u4e2d\u5f97\u5230\u7684\u7d50\u8ad6\uff0c\u8207 TD \u7684\u7d50\u8ad6\u5177\u4e00\u81f4\u6027\uff0c\u4f46\u95dc\u806f\u7279\u5fb5\u8207 \u97fb\u5f8b\u7279\u5fb5\u4e4b\u9593\u6548\u679c\u5dee\u7570\u8f03\u7121 TD \u4f86\u5f97\u986f\u8457\u3002 \u8868 10. \u55ae\u985e\u7279\u5fb5\u4e4b\u6458\u8981\u7d50\u679c \u6587\u5b57\u6587\u4ef6(TD) \u8a9e\u97f3\u6587\u4ef6(SD) ROUGE-1 ROUGE-2 ROUGE-L ROUGE-1 ROUGE-2 ROUGE-L \u65bd\u51f1\u6587 \u7b49 \u6211\u5011\u9032\u884c\u4f7f\u7528\u6240\u6709\u6458\u8981\u7279\u5fb5\u65bc\u652f\u6301\u5411\u91cf\u6a5f\u5668(Support Vector Machine, SVM)\u4e4b\u5be6\u9a57\uff0c \u5176\u7d50\u679c\u793a\u65bc\u8868 11\u3002\u5f9e\u5be6\u9a57\u7d50\u679c\u4e2d\u53ef\u4ee5\u767c\u73fe\uff0c\u7121\u8ad6\u65bc TD \u6216\u662f SD \u4e2d\uff0c\u7d93\u904e\u5404\u7a2e\u9762\u5411\u7684\u8003 \u91cf\u5f8c\uff0c\u78ba\u5be6\u53ef\u4ee5\u7372\u5f97\u8f03\u597d\u7684\u6458\u8981\u6210\u6548\u3002\u63a5\u8457\u9032\u884c\u63a2\u8a0e\u95dc\u806f\u7279\u5fb5\u4e2d\u4f7f\u7528\u5176\u5b83\u6a21\u578b\u5206\u6578\u5c0d\u6458 \u8981\u6548\u80fd\u7684\u5f71\u97ff\u3002\u56e0\u6b64\u6211\u5011\u5c07\u95dc\u806f\u7279\u5fb5\u4e2d\u7684\u5411\u91cf\u7a7a\u9593\u6a21\u578b(VSM)\u3001\u99ac\u53ef\u592b\u96a8\u6a5f\u6f2b\u6b65(MRW) \u4ee5\u53ca\u55ae\u9023\u8a9e\u8a00\u6a21\u578b(ULM)\u7684\u5206\u6578\uff0c\u4ee5\u8a5e\u8868\u793a\u6cd5\u6a21\u578b\u6458\u8981\u4e4b\u5206\u6578\u4f5c\u70ba\u66ff\u63db\uff0c\u5206\u5225\u6839\u64da\u65bc\u8868 3\u30015 \u548c 7 \u4e2d\u6700\u4f73\u7684\u6458\u8981\u8868\u73fe\uff0c\u5f9e\u5404\u8868\u4e2d\u53ef\u4ee5\u767c\u73fe CBOW \u7684\u6458\u8981\u6548\u679c\u59cb\u7d42\u6700\u4f73\u3002 \u8868 11. \u7d50\u5408\u6240\u6709\u7279\u5fb5\u4e4b\u6458\u8981\u7d50\u679c \u6587\u5b57\u6587\u4ef6(TD) \u8a9e\u97f3\u6587\u4ef6(SD) \u65b9\u6cd5 ROUGE-1 ROUGE-2 ROUGE-L ROUGE-1 ROUGE-2 ROUGE-L \u6240\u6709\u7279\u5fb5 0.484 0.384 0.440 0.387 0.247 0.348 \u540c\u6a23\u5730\u7d50\u5408\u6240\u6709\u7279\u5fb5\u4e00\u4f75\u505a\u70ba\u652f\u6301\u5411\u91cf\u6a5f\u7684\u8f38\u5165\uff0c\u5176\u6458\u8981\u6548\u80fd\u5982\u8868 12 \u6240\u793a\u3002\u5f9e\u5be6 \u9a57\u7d50\u679c\u4e2d\u767c\u73fe\u5230\uff0c\u7121\u8ad6\u5728 TD \u6216\u662f SD \u4e2d\uff0c\u4ee5\u8a5e\u8868\u793a\u6cd5\u6a21\u578b\u4f5c\u70ba\u95dc\u806f\u7279\u5fb5\uff0c\u7686\u4f7f\u5f97\u6458\u8981 \u6210\u6548\u975e\u5e38\u986f\u8457\uff0c\u5c24\u5176\u5728 TD \u4e2d\u7684\u5be6\u9a57\u7d50\u679c\uff0c\u7522\u751f\u6700\u4f73\u4e4b\u6458\u8981\u6210\u6548\u3002 \u8868 12. \u4ee5\u8a5e\u8868\u793a\u6cd5\u6a21\u578b\u6458\u8981\u5206\u6578\u70ba\u95dc\u806f\u7279\u5fb5\u4e4b\u6458\u8981\u7d50\u679c \u6587\u5b57\u6587\u4ef6(TD) \u8a9e\u97f3\u6587\u4ef6(SD) \u65b9\u6cd5 ROUGE-1 ROUGE-2 ROUGE-L ROUGE-1 ROUGE-2 ROUGE-L \u6240\u6709\u7279\u5fb5 0.497 0.406 0.451 0.396 0.254 0.353 \u6211\u5011\u4ea6\u8003\u616e\u8a9e\u53e5\u8868\u793a\u6cd5\u6a21\u578b\u5206\u6578\u5c0d\u6458\u8981\u6548\u80fd\u7684\u5f71\u97ff\u3002\u540c\u6a23\u5c07\u95dc\u806f\u7279\u5fb5\u4e2d\u7684\u6a21\u578b\u5206\u6578 \u66ff\u63db\u70ba\u8a9e\u53e5\u8868\u793a\u6cd5\u6a21\u578b\u6458\u8981\u4e4b\u5206\u6578\uff0c\u5206\u5225\u6839\u64da\u65bc\u8868 4\u30016 \u548c 8 \u4e2d\u6700\u4f73\u7684\u6458\u8981\u8868\u73fe\uff0c\u5f9e\u5404\u8868 \u4e2d\u7684\u7d50\u679c\u53ef\u89c0\u5bdf\u5230 PV-DM \u7684\u6458\u8981\u6548\u679c\u59cb\u7d42\u6700\u4f73\uff1b\u5176\u6458\u8981\u6210\u6548\u5982\u8868 13 \u6240\u793a\u3002\u5f9e TD \u7684\u5be6 \u9a57\u7d50\u679c\u4e2d\u53ef\u4ee5\u89c0\u5bdf\u5230\uff0c\u4f7f\u7528\u8a9e\u53e5\u8868\u793a\u6cd5\u6a21\u578b\u5206\u6578\u4f5c\u70ba\u7279\u5fb5\u4e4b\u6458\u8981\u6210\u6548\u8f03\u4f7f\u7528\u8a5e\u8868\u793a\u6cd5\u4f86 \u5f97\u5dee(\u8868 12)\u3002\u7136\u800c\u5728 SD \u4e2d\uff0c\u7d50\u5408\u4ee5\u8a9e\u53e5\u8868\u793a\u6cd5\u6a21\u578b\u5206\u6578\u4f5c\u70ba\u95dc\u806f\u7279\u5fb5\u53ef\u4ee5\u9054\u5230\u6700\u4f73\u4e4b \u6458\u8981\u6548\u679c\u3002 \u8868 13. \u4ee5\u8a9e\u53e5\u8868\u793a\u6cd5\u6a21\u578b\u6458\u8981\u5206\u6578\u70ba\u95dc\u806f\u7279\u5fb5\u4e4b\u6458\u8981\u7d50\u679c \u6587\u5b57\u6587\u4ef6(TD) \u8a9e\u97f3\u6587\u4ef6(SD) \u65b9\u6cd5 ROUGE-1 ROUGE-2 ROUGE-L ROUGE-1 ROUGE-2 ROUGE-L \u6240\u6709\u7279\u5fb5 0.487 0.393 0.446 0.385 0.255 0.350 8. \u7d50\u8ad6\u8207\u672a\u4f86\u5c55\u671b \u904e\u53bb\u5728\u81ea\u52d5\u6587\u4ef6\u6458\u8981\u7684\u7814\u7a76\u4e3b\u8981\u4ecd\u8457\u91cd\u65bc\u6587\u5b57\u6587\u4ef6\u6458\u8981\uff0c\u76f4\u5230 1990 \uf98e\u5f8c\u671f\uff0c\u7531\u65bc\u5f71\u97f3\u591a \u7684\u65b9\u5f0f(\u8868 3)\u65bd\u51f1\u6587 \u7b49 \u97fb\u5f8b\u7279\u5fb5 0.452 0.349 0.409 0.363 0.219 0.322 \u5a92\u9ad4\u6280\u8853\u7684\u9032\u6b65\u8207\u6210\u719f\uff0c\u624d\u9010\u6f38\u958b\u59cb\u6709\u8a9e\u97f3\u6587\u4ef6\u6458\u8981\u7684\u7814\u7a76\u3002\u6587\u4ef6\u6458\u8981\u53ef\u5206\u70ba\u7bc0\u9304\u5f0f\u6458 Similarity)\u3001\u99ac\u53ef\u592b\u96a8\u6a5f\u6f2b\u6b65(MRW)\u4ee5\u53ca\u6587\u4ef6\u76f8\u4f3c\u5ea6\u91cf\u503c(DLM)\u7684\u65b9\u6cd5\u4f5c\u70ba\u6311\u9078\u6458\u8981\u8a9e\u53e5 \u8a5e\u5f59\u7279\u5fb5 0.362 0.237 0.311 0.298 0.176 0.266 \u8981\u8207\u62bd\u8c61\u5f0f\u6458\u8981\uff0c\u672c\uf941\u6587\u65e8\u5728\u63a2\u8a0e\u7bc0\u9304\u5f0f\u4e2d\u6587\u5ee3\u64ad\u65b0\u805e\u6587\u4ef6\u6458\u8981\u65b9\u6cd5\u3002\u6211\u5011\u63d0\u51fa\u5169\u7a2e\u8a5e \u4e4b\u4f9d\u64da\u3002 \u95dc\u806f\u7279\u5fb5 0.389 0.254 0.332 0.355 0.200 0.300 \u8868\u793a\u6cd5\u2500\u9023\u7e8c\u578b\u8a5e\u888b\u6a21\u578b(CBOW)\u548c\u8df3\u8e8d\u5f0f\u6a21\u578b(SG)\uff0c\u4ee5\u53ca\u5169\u7a2e\u8a9e\u53e5\u8868\u793a\u6cd5\u2500\u5206\u6563\u5f0f\u5132</td></tr></table>" |
| } |
| } |
| } |
| } |
