Base_JSON/prefixO/json/O07/O07-1001.json

{
    "paper_id": "O07-1001",
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    "title": "",
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    "abstract": "Current language resource tools provide only limited help for ESL/EFL writing. This research proposes a language information retrieval approach to acquire referential sentences from corpus under various levels of users' language cognition. The approach includes a set of expression elements, such as exact words, prefix, suffix, part-of-speech, wildcard, and subsequence. Sentence retrieval involves both exact match and partial match with user query. Finally, the set of retrieved sentences are evaluated and ranked by multiple sequence alignment for relevance to user expression needs.",
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                "text": "Current language resource tools provide only limited help for ESL/EFL writing. This research proposes a language information retrieval approach to acquire referential sentences from corpus under various levels of users' language cognition. The approach includes a set of expression elements, such as exact words, prefix, suffix, part-of-speech, wildcard, and subsequence. Sentence retrieval involves both exact match and partial match with user query. Finally, the set of retrieved sentences are evaluated and ranked by multiple sequence alignment for relevance to user expression needs.",
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                "text": ") S 1 \u3001S 2 \u3001\u2026 S n \uff0cn\u22673\uff0c\u4e00\u500b\u591a\u91cd\u5e8f\uf99c\u6392\uf99c\u70ba\u76f8\u540c\u9577 \ufa01\u7684\u5e8f\uf99c A 1 \u3001A 2 \u3001\u2026 A n \uff0c\u5176\u4e2d A 1 \u5c0d\u61c9 S 1 \u3001A 2 \u5c0d\u61c9 S 2 \uff0c\u4f9d\u6b64\uf9d0\u63a8\u5230 n\u3002\u800c A 1 \u3001A 2 \u3001\u2026 A n \u5e8f\uf99c\u4e2d\u5141\u8a31\u5143\u7d20\u4e4b\u9593\u51fa\u73fe\u300c\u9593\u9694\u300d \uff0c\u4ee5\"\u2212\"\u8868\u793a\u3002 \u5047\u8a2d\u6709\uf978\u500b\u5e8f\uf99c S 1 = CCAATA\u3001S 2 = CCAT\uff0c\u5e8f\uf99c\u5143\u7d20\u70ba\u03a3={A,C,T}\uff0c\u5247\u6392\uf99c\u7684\u7d50\u679c\u53ef \u80fd\u70ba S 1 = ----C C A A T A\uff0cS 2 = C C A T ------\uff0c\u6216 S 1 = C C A A T A\uff0cS 2 = C C A - T -\u3002\uf978\u7a2e\u7d50\u679c\u5747\u70ba S 1 \u3001S 2 \u7684 MSA\uff0c\uf978\u7a2e\u6392\uf99c\u65b9\u5f0f\u4e4b\u9593\u7684\u512a\uf99d\u6216\u5408\u5b9c\u7a0b\ufa01\uff0c\u53ef\u7d93\u7531\u8a08\u7b97 \u6240\u6709\u4f4d\u65bc\u540c\u4e00\u76f8\u5c0d\u4f4d\u7f6e\u5143\u7d20\u7684\u6bd4\u5c0d\u5206\uf969\u7e3d\u548c\uf92d\u8a55 \u4f30 \u3002 \u901a \u5e38 \u6211 \u5011 \u4ee5 \u4e00 \u500b \u7f6e \u63db \u77e9 \u9663 (substitution matrix)\uf92d\u4ee3\u8868\u4e0a\u8ff0\u6240\u6709\u5143\u7d20\u9593\u7684\u6bd4\u5c0d\u5206\uf969\u3002\u800c\uf978\u500b\u5e8f\uf99c\u4e4b\u9593\u7684\u6700\u512a\u6392\uf99c\u7d50 \u679c(optimal MSA)\uff0c\u53ef\u4ee5\u900f\u904e",
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            {
                "text": ". This(X) posed(X) a(=) particular(X) problem(%) for(P) an(X) agent(X). S 2 . Listening(X) to(X) all(X) these(X) personal(X) accounts(X) has(X) had(X) a(=) profound(%) effect(X) on(P) us(X). S 3 . Increasingly(X) acid(X) rain(X) is(X) a(=) problem(%) in(P) Europe(X) too(X).",
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                "text": "\u5176\u4e2d S 1 \u3001S 2 \u3001S 3 \u7c97\u9ad4\u7684\u55ae\u5b57\u4f9d\u5e8f\u70ba\u9078\u53d6\u7684\"a\"(exact)\u3001\"pro%\"(prefix)\u548c\"P\"(\u4ecb\u4fc2\u8a5e)\uff0c\u62ec \u5f27\u5167\u7684\u7b26\u865f\u70ba S 1 \u3001S 2 \u3001S 3 \u7684 MSA \u4ee3\u8868\u5143\u7d20\u3002\u4ee5\u4ee3\u8868\u5b78\u7fd2\u8005\u7684\u8a9e\u610f\u8868\u9054\u9700\u6c42\u4e4b\u67e5\u8a62\u689d\u4ef6 (S 0 )\u70ba\u4e2d\u5fc3\u9ede\uff0c\u4f9d\u64da the center star algorithm ",
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            {
                "text": "EQUATION",
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                        "text": "EQUATION",
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        "bib_entries": {
            "BIBREF0": {
                "ref_id": "b0",
                "title": "Fixed Expressions in Written Texts: Implications for Assessing Sophistication, East Lancing, MI: National Center for Research on Teacher Learning, ERIC Document Reproduction Service No. ED 352808",
                "authors": [
                    {
                        "first": "A",
                        "middle": [],
                        "last": "Aghbar",
                        "suffix": ""
                    }
                ],
                "year": 1990,
                "venue": "",
                "volume": "",
                "issue": "",
                "pages": "",
                "other_ids": {},
                "num": null,
                "urls": [],
                "raw_text": "A. Aghbar, Fixed Expressions in Written Texts: Implications for Assessing Sophistication, East Lancing, MI: National Center for Research on Teacher Learning, ERIC Document Reproduction Service No. ED 352808, 1990.",
                "links": null
            },
            "BIBREF1": {
                "ref_id": "b1",
                "title": "Corpus Linguistics",
                "authors": [
                    {
                        "first": "T",
                        "middle": [],
                        "last": "Mcenery",
                        "suffix": ""
                    },
                    {
                        "first": "A",
                        "middle": [],
                        "last": "Wilson",
                        "suffix": ""
                    }
                ],
                "year": 1996,
                "venue": "",
                "volume": "",
                "issue": "",
                "pages": "",
                "other_ids": {},
                "num": null,
                "urls": [],
                "raw_text": "T. McEnery and A. Wilson, Corpus Linguistics, Edinburgh University Press, Edinburgh, 1996.",
                "links": null
            },
            "BIBREF2": {
                "ref_id": "b2",
                "title": "The Importance of Corpus-based Research for Language Teachers",
                "authors": [
                    {
                        "first": "S",
                        "middle": [],
                        "last": "Conrad",
                        "suffix": ""
                    }
                ],
                "year": 1999,
                "venue": "System",
                "volume": "27",
                "issue": "",
                "pages": "1--18",
                "other_ids": {},
                "num": null,
                "urls": [],
                "raw_text": "S. Conrad, The Importance of Corpus-based Research for Language Teachers, System 27: 1-18, 1999.",
                "links": null
            },
            "BIBREF3": {
                "ref_id": "b3",
                "title": "ESL Teachers' Questions and Corpus Evidence",
                "authors": [
                    {
                        "first": "A",
                        "middle": [
                            "B M"
                        ],
                        "last": "Tsui",
                        "suffix": ""
                    }
                ],
                "year": 2005,
                "venue": "International Journal of Corpus Linguistics",
                "volume": "10",
                "issue": "3",
                "pages": "335--356",
                "other_ids": {},
                "num": null,
                "urls": [],
                "raw_text": "A. B. M. Tsui, ESL Teachers' Questions and Corpus Evidence, International Journal of Corpus Linguistics 10(3): 335-356, 2005.",
                "links": null
            },
            "BIBREF4": {
                "ref_id": "b4",
                "title": "Learners' Writing Skills in French: Corpus Consultation and Learner Evaluation",
                "authors": [
                    {
                        "first": "I",
                        "middle": [],
                        "last": "De O'sullivan",
                        "suffix": ""
                    },
                    {
                        "first": "A",
                        "middle": [],
                        "last": "Chambers",
                        "suffix": ""
                    }
                ],
                "year": 2006,
                "venue": "Journal of Second Language Writing",
                "volume": "15",
                "issue": "1",
                "pages": "49--68",
                "other_ids": {},
                "num": null,
                "urls": [],
                "raw_text": "I. de O'Sullivan and A. Chambers, Learners' Writing Skills in French: Corpus Consultation and Learner Evaluation, Journal of Second Language Writing 15(1):49-68, 2006.",
                "links": null
            },
            "BIBREF5": {
                "ref_id": "b5",
                "title": "A Concordance and Genre-informed Approach to ESP Essay Writing",
                "authors": [
                    {
                        "first": "Jean-Jacques",
                        "middle": [],
                        "last": "Weber",
                        "suffix": ""
                    }
                ],
                "year": 2001,
                "venue": "ELT Journal",
                "volume": "55",
                "issue": "1",
                "pages": "14--20",
                "other_ids": {},
                "num": null,
                "urls": [],
                "raw_text": "Jean-Jacques Weber, A Concordance and Genre-informed Approach to ESP Essay Writing, ELT Journal 55(1): 14-20, 2001.",
                "links": null
            },
            "BIBREF6": {
                "ref_id": "b6",
                "title": "Learning Process, Strategies and Web-based Concordancers: a Case Study",
                "authors": [
                    {
                        "first": "Y",
                        "middle": [
                            "C"
                        ],
                        "last": "Sun",
                        "suffix": ""
                    }
                ],
                "year": 2003,
                "venue": "British Journal of Educational Technology",
                "volume": "34",
                "issue": "5",
                "pages": "601--613",
                "other_ids": {},
                "num": null,
                "urls": [],
                "raw_text": "Y. C. Sun, Learning Process, Strategies and Web-based Concordancers: a Case Study, British Journal of Educational Technology 34(5): 601-613, 2003.",
                "links": null
            },
            "BIBREF7": {
                "ref_id": "b7",
                "title": "An Investigation of Students' Experiences with Corpus Technology in Second Language Academic Writing",
                "authors": [
                    {
                        "first": "H",
                        "middle": [],
                        "last": "Yoon",
                        "suffix": ""
                    }
                ],
                "year": 2005,
                "venue": "",
                "volume": "",
                "issue": "",
                "pages": "",
                "other_ids": {},
                "num": null,
                "urls": [],
                "raw_text": "H. Yoon, An Investigation of Students' Experiences with Corpus Technology in Second Language Academic Writing, Ph.D. Dissertation, The Ohio State University, USA, 2005.",
                "links": null
            },
            "BIBREF8": {
                "ref_id": "b8",
                "title": "An ESL Writer's Collocational Aid",
                "authors": [
                    {
                        "first": "C",
                        "middle": [
                            "C"
                        ],
                        "last": "Shei",
                        "suffix": ""
                    },
                    {
                        "first": "H",
                        "middle": [],
                        "last": "Pain",
                        "suffix": ""
                    }
                ],
                "year": 2000,
                "venue": "Computer Assisted Language Learning",
                "volume": "13",
                "issue": "2",
                "pages": "167--182",
                "other_ids": {},
                "num": null,
                "urls": [],
                "raw_text": "C. C. Shei and H. Pain, An ESL Writer's Collocational Aid, Computer Assisted Language Learning 13(2): 167-182, 2000.",
                "links": null
            },
            "BIBREF9": {
                "ref_id": "b9",
                "title": "The Grammatical and Lexical Pattern of MAKE in Native and Non-native Student Writing",
                "authors": [
                    {
                        "first": "B",
                        "middle": [],
                        "last": "Altenberg",
                        "suffix": ""
                    },
                    {
                        "first": "S",
                        "middle": [],
                        "last": "Granger",
                        "suffix": ""
                    }
                ],
                "year": 2001,
                "venue": "Applied Linguistics",
                "volume": "22",
                "issue": "2",
                "pages": "173--195",
                "other_ids": {},
                "num": null,
                "urls": [],
                "raw_text": "B. Altenberg and S. Granger, The Grammatical and Lexical Pattern of MAKE in Native and Non-native Student Writing, Applied Linguistics 22(2):173-195, 2001.",
                "links": null
            },
            "BIBREF10": {
                "ref_id": "b10",
                "title": "The BBI Dictionary of English Word Combinations",
                "authors": [
                    {
                        "first": "R",
                        "middle": [],
                        "last": "Ilson",
                        "suffix": ""
                    },
                    {
                        "first": "B",
                        "middle": [],
                        "last": "Morton",
                        "suffix": ""
                    },
                    {
                        "first": "B",
                        "middle": [],
                        "last": "Evelyn",
                        "suffix": ""
                    }
                ],
                "year": 1997,
                "venue": "",
                "volume": "",
                "issue": "",
                "pages": "",
                "other_ids": {},
                "num": null,
                "urls": [],
                "raw_text": "R. Ilson, B. Morton, and B. Evelyn, The BBI Dictionary of English Word Combinations, Amsterdam: John Benjamin Publishing Company, 1997.",
                "links": null
            },
            "BIBREF11": {
                "ref_id": "b11",
                "title": "Extraction and Display of Significant Collocations for Lexicography",
                "authors": [
                    {
                        "first": "A",
                        "middle": [],
                        "last": "Kilgarriff",
                        "suffix": ""
                    },
                    {
                        "first": "D",
                        "middle": [],
                        "last": "Tugwell",
                        "suffix": ""
                    },
                    {
                        "first": "Word",
                        "middle": [],
                        "last": "Sketch",
                        "suffix": ""
                    }
                ],
                "year": 2001,
                "venue": "Proceedings of the Collocations Workshop",
                "volume": "",
                "issue": "",
                "pages": "32--38",
                "other_ids": {},
                "num": null,
                "urls": [],
                "raw_text": "A. Kilgarriff and D. Tugwell, WORD SKETCH: Extraction and Display of Significant Collocations for Lexicography, Proceedings of the Collocations Workshop, ACL 2001, pp. 32-38.",
                "links": null
            },
            "BIBREF12": {
                "ref_id": "b12",
                "title": "The Advantage of Using Relational Databases for Large Corpora",
                "authors": [
                    {
                        "first": "M",
                        "middle": [],
                        "last": "Davies",
                        "suffix": ""
                    }
                ],
                "year": 2005,
                "venue": "International Journal of Corpus Linguistics",
                "volume": "10",
                "issue": "3",
                "pages": "307--334",
                "other_ids": {},
                "num": null,
                "urls": [],
                "raw_text": "M. Davies, The Advantage of Using Relational Databases for Large Corpora, International Journal of Corpus Linguistics 10(3): 307-334, 2005.",
                "links": null
            },
            "BIBREF13": {
                "ref_id": "b13",
                "title": "A General Method Applicable to the Search for Similarities in the Amino Acid Sequence of Two Proteins",
                "authors": [
                    {
                        "first": "S",
                        "middle": [
                            "B"
                        ],
                        "last": "Needleman",
                        "suffix": ""
                    },
                    {
                        "first": "C",
                        "middle": [
                            "D"
                        ],
                        "last": "Wunsh",
                        "suffix": ""
                    }
                ],
                "year": 1970,
                "venue": "Journal of Molecular Biology",
                "volume": "48",
                "issue": "",
                "pages": "443--453",
                "other_ids": {},
                "num": null,
                "urls": [],
                "raw_text": "S. B. Needleman and C. D. Wunsh, A General Method Applicable to the Search for Similarities in the Amino Acid Sequence of Two Proteins, Journal of Molecular Biology 48: 443-453, 1970.",
                "links": null
            },
            "BIBREF14": {
                "ref_id": "b14",
                "title": "Efficient Constrained Multiple Sequence Alignment with Performance Guarantee",
                "authors": [
                    {
                        "first": "Y",
                        "middle": [
                            "L"
                        ],
                        "last": "Francis",
                        "suffix": ""
                    },
                    {
                        "first": "N",
                        "middle": [
                            "L"
                        ],
                        "last": "Ho",
                        "suffix": ""
                    },
                    {
                        "first": "T",
                        "middle": [
                            "W"
                        ],
                        "last": "Lam",
                        "suffix": ""
                    },
                    {
                        "first": "W",
                        "middle": [
                            "H"
                        ],
                        "last": "Wong",
                        "suffix": ""
                    },
                    {
                        "first": "M",
                        "middle": [
                            "Y"
                        ],
                        "last": "Chan",
                        "suffix": ""
                    }
                ],
                "year": 2003,
                "venue": "IEEE Computational Systems Bioinformatics",
                "volume": "2",
                "issue": "",
                "pages": "337--346",
                "other_ids": {},
                "num": null,
                "urls": [],
                "raw_text": "Y. L. Francis, N. L. Ho, T. W. Lam, W. H. Wong, and M. Y. Chan, Efficient Constrained Multiple Sequence Alignment with Performance Guarantee, IEEE Computational Systems Bioinformatics 2: 337-346, 2003.",
                "links": null
            },
            "BIBREF15": {
                "ref_id": "b15",
                "title": "Analysis of Recommendation Algorithms for E-Commerce",
                "authors": [
                    {
                        "first": "B",
                        "middle": [],
                        "last": "Sarwar",
                        "suffix": ""
                    },
                    {
                        "first": "G",
                        "middle": [],
                        "last": "Karypis",
                        "suffix": ""
                    },
                    {
                        "first": "J",
                        "middle": [],
                        "last": "Konstan",
                        "suffix": ""
                    },
                    {
                        "first": "J",
                        "middle": [],
                        "last": "Riedl",
                        "suffix": ""
                    }
                ],
                "year": 2000,
                "venue": "Proceedings of ACM Conference on Electronic Commerce",
                "volume": "",
                "issue": "",
                "pages": "",
                "other_ids": {},
                "num": null,
                "urls": [],
                "raw_text": "B. Sarwar, G. Karypis, J. Konstan, and J. Riedl, Analysis of Recommendation Algorithms for E-Commerce, Proceedings of ACM Conference on Electronic Commerce, Minneapolis, Minnesota, U.S.A., 2000.",
                "links": null
            },
            "BIBREF17": {
                "ref_id": "b17",
                "title": "Measuring Readiness for Simplified Material: A Test of the First 1,000 Words of English",
                "authors": [],
                "year": 1993,
                "venue": "Simplification: Theory and Application RELC",
                "volume": "31",
                "issue": "",
                "pages": "193--203",
                "other_ids": {},
                "num": null,
                "urls": [],
                "raw_text": "Paul Nation, Measuring Readiness for Simplified Material: A Test of the First 1,000 Words of English, In Simplification: Theory and Application RELC 31:193-203, 1993.",
                "links": null
            },
            "BIBREF18": {
                "ref_id": "b18",
                "title": "Classroom Success of an Intelligent Tutoring System for Lexical Practice and Reading Comprehension",
                "authors": [
                    {
                        "first": "M",
                        "middle": [],
                        "last": "Heilman",
                        "suffix": ""
                    },
                    {
                        "first": "K",
                        "middle": [],
                        "last": "Collins-Thompson",
                        "suffix": ""
                    },
                    {
                        "first": "J",
                        "middle": [],
                        "last": "Callan",
                        "suffix": ""
                    },
                    {
                        "first": "M",
                        "middle": [],
                        "last": "Eskenazi",
                        "suffix": ""
                    }
                ],
                "year": 2006,
                "venue": "Proceedings of the Ninth International Conference on Spoken Language Processing",
                "volume": "",
                "issue": "",
                "pages": "",
                "other_ids": {},
                "num": null,
                "urls": [],
                "raw_text": "M. Heilman, K. Collins-Thompson, J. Callan, and M. Eskenazi, Classroom Success of an Intelligent Tutoring System for Lexical Practice and Reading Comprehension, Proceedings of the Ninth International Conference on Spoken Language Processing, Pittsburgh, USA, 2006.",
                "links": null
            },
            "BIBREF19": {
                "ref_id": "b19",
                "title": "Finding Syntactic Structure in Unparsed Corpora: The Gsearch Corpus Query System",
                "authors": [
                    {
                        "first": "S",
                        "middle": [],
                        "last": "Corley",
                        "suffix": ""
                    },
                    {
                        "first": "M",
                        "middle": [],
                        "last": "Corley",
                        "suffix": ""
                    },
                    {
                        "first": "F",
                        "middle": [],
                        "last": "Keller",
                        "suffix": ""
                    },
                    {
                        "first": "M",
                        "middle": [],
                        "last": "Crocker",
                        "suffix": ""
                    },
                    {
                        "first": "S",
                        "middle": [],
                        "last": "Trewin",
                        "suffix": ""
                    }
                ],
                "year": 2001,
                "venue": "Computers and the Humanities",
                "volume": "35",
                "issue": "2",
                "pages": "81--94",
                "other_ids": {},
                "num": null,
                "urls": [],
                "raw_text": "S. Corley, M. Corley, F. Keller, M. Crocker, and S. Trewin, Finding Syntactic Structure in Unparsed Corpora: The Gsearch Corpus Query System, Computers and the Humanities 35(2): 81-94, 2001.",
                "links": null
            },
            "BIBREF20": {
                "ref_id": "b20",
                "title": "Collocational Translation Memory Extraction based on Statistical and Linguistic Information, Computational Linguistics and Chinese Language Processing",
                "authors": [
                    {
                        "first": "T",
                        "middle": [
                            "C"
                        ],
                        "last": "Chuang",
                        "suffix": ""
                    },
                    {
                        "first": "J",
                        "middle": [
                            "Y"
                        ],
                        "last": "Jian",
                        "suffix": ""
                    },
                    {
                        "first": "Y",
                        "middle": [
                            "C"
                        ],
                        "last": "Chang",
                        "suffix": ""
                    },
                    {
                        "first": "J",
                        "middle": [
                            "S"
                        ],
                        "last": "Chang",
                        "suffix": ""
                    }
                ],
                "year": 2005,
                "venue": "",
                "volume": "10",
                "issue": "",
                "pages": "329--346",
                "other_ids": {},
                "num": null,
                "urls": [],
                "raw_text": "T. C. Chuang, J. Y. Jian, Y. C. Chang, and J. S. Chang, Collocational Translation Memory Extraction based on Statistical and Linguistic Information, Computational Linguistics and Chinese Language Processing, Vol. 10, No. 3, September 2005, pp. 329-346.",
                "links": null
            }
        },
        "ref_entries": {
            "FIGREF0": {
                "uris": null,
                "text": "1 =S(-,-) + \u2026 + S(=,=) + S(%,X) + S(P, %) + S(-,P) + ... + S(-,X) = 93 C 2 =S(-,X) + \u2026 + S(=,=) + S(%,%) + S(P, X) + S(-,P) + ... + S(-,-) =139 C 3 =S(-,-) + \u2026 + S(=,=) + S(%,%) + S(P, P) + S(-,X) + ... + S(-,-) = 169 \u6839\u64da",
                "type_str": "figure",
                "num": null
            },
            "TABREF0": {
                "num": null,
                "type_str": "table",
                "html": null,
                "content": "<table><tr><td>\u5f62\u6210\uf9ba\u81ea\u7136\u800c\u771f\u5be6\u7684\u8a9e\u8a00\u4f7f\u7528\u73fe\u8c61\u5448\u73fe[2]\u3002\u56e0\u6b64\uff0c\u8a9e\uf9be\u5eab\u4ee3\u8868\u8457\u8c50\u5bcc\u7684\u8a9e\u8a00\u8cc7\u6e90\uff0c\uf967 \u4e8c\u3001\u82f1\u8a9e\u5beb\u4f5c\u53ca\u8a9e\u8a00\u8cc7\u6e90\u5de5\u5177 \u7531\u76f8\u95dc\u7814\u7a76\u53ef\u77e5\uff0c\u8a9e\u8a00\u8cc7\u6e90\u5de5\u5177\u5c0d\u65bc ESL/EFL \u4f5c\u8005\u7684\u5beb\u4f5c\u5354\u52a9\u662f\u76f8\u7576\u91cd\u8981\u7684\uff0c\u7136\u800c\uf967 4. \u82f1\u8a9e\u8a5e\u6027(POS)\uff1a\u7576\u8a9e\uf9be\u5eab\u542b\u6709\u82f1\u8a9e\u8a5e\u6027\u6a19\u7c64\u6642\uff0c\u4f7f\u7528\u8005\u53ef\u4ee5\u904b\u7528\u82f1\u8a9e\u7684\u8a5e\u6027\u6a19\u7c64\u7576</td></tr><tr><td>\u50c5\u53ef\u4ee5\u5c0d\u8a9e\u8a00\u5b78\u7684\u7814\u7a76\uff0c\u63d0\u4f9b\u8a31\u591a\u7d71\u8a08\u5206\u6790\u4e0a\u7684\u8cc7\u8a0a\u8207\uf9e4\u89e3\u8a6e\u91cb\u4e0a\u7684\u4f9d\u64da\uff0c\u4e5f\u53ef\u4ee5\u505a\u70ba \u8a9e\u8a00\u5b78\u7fd2\u4e0a\u5404\u7a2e\u8a9e\u8a00\u4f7f\u7528\u65b9\u5f0f\u7684\uf96b\u7167\u8cc7\uf9be[3][4]\u3002\u540c\u6a23\u7684\uff0c\u4ee5\u82f1\u8a9e\u5beb\u4f5c\u70ba\u76ee\u7684\u7684\u89c0\u9ede\u800c \u8a00\uff0c\u8a9e\uf9be\u5eab\u7684\u5b58\u5728\u63d0\u4f9b\uf9ba\u8cc7\u8a0a\u8655\uf9e4\u5de5\u4f5c\u4e0a\u76f8\u7576\u5927\u7684\uf9dd\u7528\u7a7a\u9593\uff0c\uf974\u80fd\u767c\u5c55\u4e00\u500b\u6709\u6548\u7684\u8a9e\u8a00 \u8cc7\u8a0a\u4f7f\u7528\u5de5\u5177\uff0c\u5728 ESL/EFL 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\u52a9\u4ecd\u7136\uf967\u8db3[8]\uff0c\u5176\u4e3b\u8981\u9650\u5236\u5305\u62ec\uff1a(1)\u8f03\u6709\u9650\u7684\u67e5\u8a62\u6a5f\u5236\uff0c\u53ea\u63d0\u4f9b\u8a5e\u5f59\u8207\u8a5e\u6027\u7684\u689d\u4ef6\u6aa2 \uf96a\uff0c\u7121\u6cd5\u5145\u5206\u5c0d\u61c9 ESL/EFL \u4f5c\u8005\u5728\u8a9e\u8a00\u77e5\uf9fc\u4e0a\u611f\u5230\uf967\u8db3\u6216\uf967\u78ba\u5b9a\u7684\u60c5\u5f62\u4e0b\uff0c\u6240\u9700\u8981\u7684 \u5de5\u5177\u7684\u6210\u6548\u537b\u4ee5\u8a9e\u8a00\u77e5\uf9fc\u638c\u63e1\u7684\u7a0b\ufa01\u70ba\u9580\u6abb\uff0c\u9020\u6210\u4e2d\u7b49\u7a0b\ufa01\u4ee5\u4e0b\u7684 ESL/EFL \u4f5c\u8005\u4e26\u7121 \u4ee3\u8868\u90e8\u4efd\u78ba\u8a8d\u800c\u90e8\u5206\u8a2d\u5b9a\u7bc4\u570d\u7684\u67e5\u8a62\u689d\u4ef6\u3002\uf9b5\u5982\uff0c\u4f7f\u7528\u8005\u8981\u8868\u9054\u4e2d\u6587\u4e4b\u300c\u6fc3\uf9fe\u300d\u4e4b\u610f\u601d\uff0c 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\u5e38\u548c cause\u3001create\u3001solve \u5916\uff0c\u9019\u4e9b\u8868\u9054\u5143\u7d20\u4e5f\u53ef\u4ee5\u88ab\u5f48\u6027\u7684\u7d44\u5408\uff0c\u5f62\u6210\u4e00\u500b\u8868\u9054\u5143\u7d20\u5e8f\uf99c\uff0c\uf9b5\u5982\uff0ca pro% P \u6216 \u4e09\u3001\uf906\u5b50\u6aa2\uf96a\u8207\u63a8\u85a6 would rather V than V\u3002\u9019\u4ee3\u8868\u4f7f\u7528\u8005\u5728\u8f03\u591a\u7684\u8a9e\u8a00\u8a8d\u77e5\u4e0b\uff0c\u6240\u63d0\u4f9b\u7684\u8f03\u5145\u5206\u7684\u8868\u9054\u610f \u4e00\u540c\u642d\u914d\uff0c\u800c\u8f03\u5c11\u7528 make\uff0c\u539f\u56e0\u70ba\u8a5e\u8a9e\u642d\u914d\u70ba\u4e00\u7a2e\u7279\u5b9a\u7684\u7d50\u5408\u65b9\u5f0f\uff0c\u8fd1\u4f3c\u65bc\u8a9e\u8a00\u4f7f\u7528 \u4e0a\u7684\u7d04\u5b9a\u8207\u7fd2\u6163\u3002Ilson \u7b49\u5b78\u8005\u5c07\u8907\u96dc\u7684\u82f1\u8a9e\u8a5e\u8a9e\u642d\u914d\u5206\u70ba\u6587\u6cd5\u642d\u914d(grammatical \u70ba\uf9ba\u89e3\u6c7a\u5728\u8a9e\u8a00\u77e5\uf9fc\uf967\u8db3\u7684\u60c5\u5f62\u4e0b\u4f7f\u7528\u73fe\u6709\u8a9e\u8a00\u8cc7\u6e90\u5de5\u5177\u7684\u6210\u6548\uf967\u5f70\u7684\u554f\u984c\uff0c\u6211\u5011\u63d0\u51fa \u5716\uff0c\u800c\u69cb\u6210\uf901\u5f37\u7684\u67e5\u8a62\u689d\u4ef6\u9650\u5236\u3002 \u67e5\u8a62\u65b9\u5f0f\uff1b(2)\u8f03\u7121\u5f48\u6027\u7684\u6aa2\uf96a\u6a5f\u5236\uff0c\u53ea\u63d0\u4f9b\u5b8c\u5168\u7b26\u5408\u689d\u4ef6\u7684\u6aa2\uf96a\uff0c\u5728\u4f7f\u7528\u8005\u8a8d\u77e5\u932f\u8aa4 collocation)\u8207\u8a5e\u5f59\u642d\u914d(lexical collocation)\uf978\uf9d0[11]\u3002\u6587\u6cd5\u642d\u914d\u662f\u6307\u5305\u542b\u4e00\u500b\u4e3b\u8981\u55ae\u5b57 \uf9ba\u4e00\u500b\u8a9e\u8a00\u8cc7\u8a0a\u6aa2\uf96a\u65b9\u6cd5\uff0c\u80fd\u63a5\u7d0d\uf967\u6b63\u78ba\u7684\u6216\u662f\u6a21\u7cca\u7684\u67e5\u8a62\u689d\u4ef6\uff0c\u76e1\u53ef\u80fd\u7684\u627e\u51fa\u53ef\u4ee5\uf96b (\u4e8c)\u6aa2\uf96a\u6a21\u7d44 \u800c\u4f7f\u7528\uf967\u9069\u7576\u7684\u67e5\u8a62\u689d\u4ef6\u6642\uff0c\u5c07\u7121\u6cd5\u63d0\u4f9b\u4efb\u4f55\u67e5\u8a62\u7d50\u679c\uff1b(3)\u6aa2\uf96a\u7d50\u679c\u7684\u5448\u73fe\u4ee5\u4e0a\u4e0b\u6587 \u70ba\u4e3b\uff0c\u800c\u975e\u4ee5\u5b8c\u6574\u7684\uf9b5\uf906\u5448\u73fe\uff0c\u540c\u6642\uff0c\u5728\u7d50\u679c\u7684\u63d0\u4f9b\u4e0a\uff0c\u4e26\u672a\u8003\uf97e\u4f7f\u7528\u8005\u5728\u5beb\u4f5c\u4e0a\u6240\u9700 \u8cc7\u8a0a\u7684\u512a\u5148\u6b21\u5e8f\u3002 \u4ee5\u8cc7\u8a0a\u8655\uf9e4\u6280\u8853\u7684\u89c0\u9ede\u800c\u8a00\uff0cconcordancer \u662f\u4e00\u7a2e\u76f8\u7576\u521d\u968e\u7684\u8cc7\u8a0a\u6aa2\uf96a\u529f\u80fd\uff0c\u53ea\u80fd\u91dd\u5c0d \u6709\u9650\u5f62\u5f0f\u7684\u67e5\u8a62\u689d\u4ef6\uff0c\u9032\ufa08\u7c21\u55ae\u7684\u6bd4\u5c0d\u6aa2\uf96a\uff0c\u5c0d\u6aa2\uf96a\u7d50\u679c\u4e5f\u6c92\u6709\u8a55\u4f30\u8207\u6392\u5e8f\u7684\u6982\uf9a3\u3002\u56e0 \u6b64\uff0c\u5728\u8a9e\uf9be\u5eab\u7684\u4f7f\u7528\u4e0a\uff0cconcordancer \u53ea\u80fd\u91dd\u5c0d\u660e\u78ba\u7684\u6aa2\uf96a\u76ee\u6a19\u63d0\u4f9b\u5c11\uf97e\u7684\u6709\u7528\u8cc7\u8a0a\u3002 (dominant word)\u53ca\u4e00\u500b\u4ecb\u4fc2\u8a5e\u3001\u51a0\u8a5e\u6216\uf99a\u63a5\u8a5e\u7684\u7247\u8a9e\uff0c\uf9b5\u5982\uff0c determined by\u3002\u800c\u8a5e\u5f59\u642d \u914d\u662f\u6307\u540d\u8a5e\u3001\u52d5\u8a5e\u3001\u5f62\u5bb9\u8a5e\u3001\u526f\u8a5e\u7b49\u7279\u5b9a\u5b57\u8a5e\u4e4b\u9593\u7684\u6163\u7528\u7d44\u5408\uff0c\uf9b5\u5982\uff0ccompose music\u3002 \u8a5e\u5f59\u642d\u914d\u6c92\u6709\u4e3b\u8981\u55ae\u5b57\uff0c\u4e5f\u662f ESL/EFL \u4f5c\u8005\u611f\u5230\u8f03\u70ba\u56f0\u96e3\u53ca\u5bb9\uf9e0\u767c\u751f\u932f\u8aa4\u7684\u90e8\u5206\u3002 \u8a9e\uf9be\u5eab\u4f7f\u7528\u5de5\u5177\u6cdb\u6307\u7d50\u5408\u8a9e\uf9be\u5eab\u8cc7\u6e90\u5c0d\u65bc\u8a9e\u8a00\u4f7f\u7528\u4e0a\u63d0\u4f9b\u8cc7\u8a0a\u7684\u5de5\u5177\u3002\u8a9e\uf9be\u5eab\u8cc7\u6e90\uff0c\u662f \u5b78\u7fd2\u8a9e\u8a00\u4e0a\u6700\u6709\u6210\u6548\u7684\uf96b\u8003\u8cc7\u6e90\uff0c\u8a9e\uf9be\u5eab\u4f7f\u7528\u5de5\u5177\u80fd\u91dd\u5c0d\u6307\u5b9a\u7684\u8a5e\u8a9e\uff0c\u63d0\u4f9b\u8a31\u591a\u7684\u4f7f\u7528 \u65b9\u5f0f\u8207\u60c5\u5883\u7bc4\uf9b5\uff0c\u8b93\u4f7f\u7528\u8005\u6709\u8f03\u4f73\u7684\uf96b\u7167\u8207\u5b78\u7fd2\u6210\u6548\u3002\u4ee5 Word Sketch \u70ba\uf9b5[12]\uff0c\u8a72\u7cfb \u7167\u7684\u4f7f\u7528\u7bc4\uf9b5\uff0c\u8b93\u4f7f\u7528\u8005\u63a2\uf96a\u6216\u78ba\u8a8d\u5176\u771f\u6b63\u9700\u8981\u7684\u8868\u9054\u65b9\u5f0f\u3002\u540c\u6642\uff0c\u5728\u6aa2\uf96a\u7d50\u679c\u7684\u63d0\u4f9b \u6aa2\uf96a\u6a21\u7d44\u7684\u76ee\u7684\u70ba\u5f9e\u8a9e\uf9be\u5eab\u4e2d\u9078\u53d6\u7b26\u5408\u4f7f\u7528\u8005\u8868\u9054\u9700\u6c42\u7684\uf9b5\uf906\u3002\u672c\u7814\u7a76\u540c\u6642\u63a1\u7528\u5b8c\u6574\u6bd4 \u4e0a\uff0c\u6211\u5011\u4e5f\u5c0d\u9078\u53d6\u51fa\uf92d\u7684\u4f7f\u7528\u7bc4\uf9b5\uff0c\u9032\ufa08\u8207\u4f7f\u7528\u8005\u8a9e\u8a00\u8cc7\u8a0a\u9700\u6c42\u7684\u76f8\u95dc\u6027\u8a55\u4f30\uff0c\u518d\u6839\u64da \u5c0d\u548c\u90e8\u5206\u6bd4\u5c0d\u7684\u9078\u53d6\u6cd5\u5247\uff0c\u5b8c\u6574\u6bd4\u5c0d\u662f\u8981\u6c42\u8a9e\uf9be\u5eab\u7684\uf9b5\uf906\u9078\u53d6\u548c\u4f7f\u7528\u8005\u9650\u5b9a\u7684\u67e5\u8a62\u689d\u4ef6 \u5176\u500b\u5225\u76f8\u95dc\u7a0b\ufa01\u5efa\uf9f7\u6392\u5e8f\uff0c\u4ee5\u63a8\u85a6\u7684\u8aee\u8a62\uf96b\u7167\u5148\u5f8c\u6b21\u5e8f\uff0c\u5617\u8a66\u8b93\u4f7f\u7528\u8005\u80fd\u4ee5\u6700\u6709\u6548\uf961\u7684 \u5fc5\u9700\u5b8c\u6574\u7684\u543b\u5408\u3002\u90e8\u5206\u6bd4\u5c0d\u5247\u5bb9\u8a31\uf9be\u5eab\u7684\uf9b5\uf906\u9078\u53d6\u548c\u4f7f\u7528\u8005\u9650\u5b9a\u7684\u67e5\u8a62\u689d\u4ef6\u6709\u90e8\u5206\u7684\u5dee \u65b9\u5f0f\u78ba\u8a8d\u5176\u9069\u5408\u7684\u8868\u9054\u65b9\u5f0f\u3002\u6b64\u65b9\u6cd5\u8b93\u4f7f\u7528\u8005\u4ee5\u5f48\u6027\u7684\u67e5\u8a62\u689d\u4ef6\u8868\u9054\u5176\uf967\u540c\u7a0b\ufa01\u7684\u8a9e\u8a00 \uf962\u3002\u6211\u5011\u9810\u671f ESL/EFL \u4f5c\u8005\u7d93\u5e38\u6703\u6709\u8a9e\u610f\u8a8d\u77e5\uf967\u5b8c\u6574\u6216\uf967\u6b63\u78ba\u7684\u60c5\u5f62\uff0c\uf974\u53ea\u4f9d\u7167\u4f7f\u7528 \u8a8d\u77e5\uff0c\u641c\u5c0b\u6aa2\uf96a\u7684\u5c0d\u8c61\u662f\u8a9e\uf9be\u5eab\uff0c\u67e5\u8a62\u7d50\u679c\u662f\u4ee5\u8a9e\uf9be\u5eab\u4e2d\u7684\u500b\u5225\uf906\u5b50\u70ba\u55ae\u5143\u6392\u5e8f\u5448\u73fe\uff0c \u8005\u7684\u9650\u5b9a\u689d\u4ef6\u9032\ufa08\u5b8c\u6574\u6bd4\u5c0d\uff0c\u5c07\u5c0e\u81f4\u65bc\u8aee\u8a62\u6210\u6548\u53d6\u6c7a\u65bc\u4f7f\u7528\u8005\u8a8d\u77e5\u7a0b\ufa01\u7684\u77db\u76fe\u3002\u90e8\u5206\u6bd4 \u63d0\u4f9b\u4f7f\u7528\u8005\u5728\u4e00\u500b\u5b8c\u6574\u7684\uf9b5\uf906\u4e2d\u89c0\u5bdf\u3001\u5b78\u7fd2\u53ca\u78ba\u8a8d\u7279\u5b9a\u7684\u8868\u9054\u65b9\u5f0f\u3002 \u5c0d\u7684\u9078\u53d6\u65b9\u5f0f\u63d0\u4f9b\uf9ba\u8f03\u5927\u7684\u5f48\u6027\u7a7a\u9593\uff0c\u5bb9\u8a31\u4f7f\u7528\u8005\u5728\uf967\u5b8c\u6574\u6216\uf967\u6b63\u78ba\u7684\u8a8d\u77e5\u4e0b\uff0c\u4e5f\u80fd\u7372 \u9019\u6a23\u7684\u529f\u80fd\u7279\u6027\u662f\u7121\u6cd5\u6eff\u8db3\uf967\u540c\u7a0b\ufa01\u7684 ESL/EFL \u4f5c\u8005\uff0c\u5728\u5beb\u4f5c\u4e0a\u6240\u9700\u7684\u591a\u6a23\u5316\u8cc7\u8a0a\u6307 \u6392\u5e8f\u904e\u7684\uf9b5\uf906\u96c6\u5408\u7d66\u4f7f\u7528\u8005\u3002 \u54c1\u8cea\u3002 \u6a21\u7d44\u6240\u9078\u53d6\u7684\uf9b5\uf906\uff0c\u8a55\u4f30\u5176\u500b\u5225\u7b26\u5408\u4f7f\u7528\u8005\u8a9e\u8a00\u8cc7\u8a0a\u9700\u6c42\u7684\u7a0b\ufa01\u4e26\u5efa\uf9f7\u6392\u5e8f\uff0c\u518d\u56de\u50b3\u6b64 \u5448\u73fe\uf901\u80fd\u91dd\u5c0d\u4f5c\u8005\u8a9e\u8a00\u8cc7\u8a0a\u9700\u6c42\u7684\u6aa2\uf96a\u7d50\u679c\uff0c\u624d\u80fd\u6709\u6548\uf9dd\u7528\u8a9e\uf9be\u5eab\uff0c\u5354\u52a9\u4f5c\u8005\u63d0\u5347\u5beb\u4f5c \u4ef6\uff0c\u5f9e\u8a9e\uf9be\u5eab\u4e2d\u6bd4\u5c0d\u9078\u53d6\u53ef\u80fd\u7b26\u5408\u4f7f\u7528\u8005\u8a9e\u8a00\u8cc7\u8a0a\u9700\u6c42\u7684\uf9b5\uf906\u3002\u800c\u6392\u5e8f\u6a21\u7d44\u5247\u91dd\u5c0d\u6aa2\uf96a \u6b64\uff0c\u4e00\u500b\u4ee5\u5beb\u4f5c\u5354\u52a9\u70ba\u76ee\u6a19\u7684\u8a9e\uf9be\u5eab\u4f7f\u7528\u5de5\u5177\uff0c\u5fc5\u9808\u63d0\u4f9b\uf901\u6709\u5f48\u6027\u7684\u67e5\u8a62\u8207\u6aa2\uf96a\u6a5f\u5236\uff0c \u63d0\u4f9b\u5bec\u5ee3\u7684\u9700\u6c42\u8207\u8a8d\u77e5\u7684\u5c0d\u61c9\u3002\u6aa2\uf96a\u6a21\u7d44\u5f9e\u8868\u9054\u5143\u7d20\u7684\u7d44\u5408\u4e2d\u8f49\u63db\u6210\u76f8\u5c0d\u61c9\u7684\u67e5\u8a62\u689d \u6848\uff0c\u751a\u81f3\u5728\u53ef\u80fd\u7684\u8a8d\u77e5\u932f\u8aa4\u4e0b\uff0c\u4e5f\u5e0c\u671b\u5f97\u5230\u9069\u7576\u7684\u5f15\u5c0e\u800c\u7372\u53d6\u6b63\u78ba\u7684\u8a9e\u8a00\u4f7f\u7528\u8cc7\u8a0a\u3002\u56e0 \u4ee5 concordance \u7684\u65b9\u5f0f\u5448\u73fe\u3002\u5716\u4e00\u986f\u793a\u4ee5 determined by \u7684\u8a5e\u8a9e\u67e5\u8a62\u7684\u7d50\u679c\u3002 \u5b8c\u6574\u6216\u90e8\u5206\u8a8d\u77e5\uff0c\u8f49\u63db\u6210\u591a\u500b\u8868\u9054\u5143\u7d20(expression elements)\uff0c\u4e26\u85c9\u7531\u5f48\u6027\u7684\u7d44\u5408\u642d\u914d\uff0c \u5e38\u56e0\u8a9e\u8a00\u77e5\uf9fc\u7684\uf967\u8db3\uff0c\u5728\u6587\u5b57\u5167\u5bb9\u7684\u751f\u7522\u904e\u7a0b\u4e2d\uff0c\u5e38\u5e38\u5fc5\u9808\u91dd\u5c0d\u7591\u60d1\u9032\ufa08\u63a2\uf96a\u6216\u5c0b\u6c42\u7b54 VIEW[13]\u7cfb\u7d71\uff0c\u4f7f\u7528\u8005\u53ef\u8f38\u5165\u5b8c\u6574\u7684\u6216\u90e8\u5206\u7684\u5b57\u8a5e\u53ca\u8a5e\u6027\u6a19\u7c64(POS)\u7b49\uff0c\u67e5\u8a62\u7684\u7d50\u679c\u5247 \u9054\u5143\u7d20\u6a21\u7d44\u3001\u6aa2\uf96a\u6a21\u7d44\u3001\u53ca\u6392\u5e8f\u6a21\u7d44\u3002\u8868\u9054\u5143\u7d20\u6a21\u7d44\u662f\u8b93\u4f7f\u7528\u8005\u80fd\u5c07\u5176\u8a9e\u8a00\u8cc7\u8a0a\u9700\u6c42\u53ca \u78ba\u7684\u7528\u6b63\u78ba\u7684\u53ca\u9069\u7576\u7684\u8a9e\u5f59\u7d44\u5408\u6392\uf99c\uff0c\u7522\u751f\u5177\u9ad4\u7684\u6587\u5b57\u5167\u5bb9\u8207\u7d50\u69cb\u3002\u800c ESL/EFL \u4f5c\u8005 \u986f\u8457\u6027\u7684\u8cc7\u8a0a\u3002\u53e6\u4e00\u500b\u8a9e\uf9be\u5eab\u4f7f\u7528\u5de5\u5177\u7684\u7bc4\uf9b5\u70ba\u7f8e\u570b Brigham Young University \u7814\u767c\u7684 \u7684\u76ee\u7684\u662f\u56de\u50b3\u4e00\u500b\u7d93\u904e\u6392\u5e8f\u7684\u591a\u500b\uf9b5\uf906\u6240\u7d44\u6210\u7684\uf9b5\uf906\u96c6\u5408\u3002\u6b64\u65b9\u6cd5\u53ef\u5206\u70ba\u4e09\u500b\u6a21\u7d44\uff1a\u8868 \u5f15\u8207\uf96b\u7167\u5354\u52a9\u3002\u5beb\u4f5c\u662f\u4e00\u500b\u8a9e\u8a00\u77e5\uf9fc\u4f7f\u7528\u8207\u8f38\u51fa\u7684\u904e\u7a0b\uff0c\u4f5c\u8005\u5fc5\u9808\u5c07\u5176\u63cf\u8ff0\u7684\u610f\u5716\uff0c\u660e \u7d71\u85c9\u7531\u8a9e\uf9be\u5eab\u7684\u4f7f\u7528\uff0c\u63d0\u4f9b\u82f1\u8a9e\u8a5e\u8a9e\u4f7f\u7528\u60c5\u5f62\u63cf\u7e6a(lexical profiling)\u8207\u642d\u914d\u8a5e(collocation) \u6211\u5011\u5c07\u8a9e\u8a00\u8cc7\u8a0a\u6aa2\uf96a\u65b9\u6cd5\u5b9a\u7fa9\u5982\u4e0b\uff1a\u7d66\u5b9a\u4f7f\u7528\u8005\u7684\u8a9e\u8a00\u8cc7\u8a0a\u9700\u6c42\uff0c\uf906\u5b50\u6aa2\uf96a\u8207\u63a8\u85a6\u6a5f\u5236 \u5f97\u6709\u7528\u7684\uf96b\u7167\u8cc7\u8a0a\u3002</td></tr><tr><td>\u672c\u7814\u7a76\u63d0\u51fa\u4e00\u7a2e\u8a9e\u8a00\u8cc7\u8a0a\u6aa2\uf96a\u65b9\u6cd5\uff0c\u91dd\u5c0d ESL/EFL \u4f5c\u8005\u5728\u5beb\u4f5c\u904e\u7a0b\u4e0a\u7684\u8a9e\u8a00\u77e5\uf9fc\uf967\u8db3 \u6216\uf967\u78ba\u5b9a\uff0c\u5f9e\u8a9e\uf9be\u5eab\u4e2d\u5c0b\u627e\u51fa\u53ef\u7528\u7684\u8a9e\u8a00\u4f7f\u7528\u7bc4\uf9b5\uff0c\u9032\u800c\u63d0\u4f9b\u91dd\u5c0d\u6027\u7684\u7bc4\uf9b5\u5354\u52a9\u3002\u6211\u5011 (\u4e00) \u8868\u9054\u5143\u7d20\u6a21\u7d44 university\uff0c\u53ef\u80fd\u542b\u6709\u8a5e\u8a9e\u7528\u6cd5\u642d\u914d\u4e0a\u7684\u932f\u8aa4\uff0c\u4ee5\u81f4\u65bc\u9078\u53d6\u51fa\u7684\u7d50\u679c\u4e26\u7121 an university \u7dca</td></tr><tr><td>\u7684\u8a9e\u8a00\u8cc7\u8a0a\u6aa2\uf96a\u65b9\u6cd5\u5305\u542b\u4e09\u500b\u6a21\u7d44\uff0c\u7b2c\u4e00\u500b\u6a21\u7d44\u662f\u4e00\u500b\u591a\u5143\u7684\u8868\u9054\u5143\u7d20\u6a21\u578b\uff0c\u80fd\u91dd\u5c0d\u5beb \u5bc6\u76f8\uf99a\u7684\uf9b5\uf906\uff0c\u800c\u9593\u63a5\u5f97\u5230\u8aee\u8a62\u7684\u5354\u52a9\u3002 \u8868\u9054\u5143\u7d20\u6a21\u7d44\u5fc5\u9808\u5f48\u6027\u7684\u5141\u8a31\uf967\u540c\u8a9e\u8a00\u7a0b\ufa01\u7684\u4f7f\u7528\u8005\u8868\u793a\u5176\u5b8c\u6574\u6216\u90e8\u5206\u8a8d\u77e5\uff0c\u6211\u5011\u7684\u65b9 \u4f5c\u904e\u7a0b\u4e2d\u7684\u8a9e\u8a00\u8cc7\u8a0a\u9700\u6c42\uff0c\u5305\u62ec\u55ae\u5b57\u3001\u642d\u914d\u5b57\u3001\u5e38\u7528\u7247\u8a9e\u3001\uf906\u578b\u7d50\u69cb\u7b49\uff0c\u63d0\u4f9b\u4f5c\u8005\u4ee5\uf901 \u6cd5\u76ee\u524d\u6db5\u84cb\u4e0b\uf99c\u8868\u9054\u5143\u7d20(expression elements)\uff1a (\u4e09)\u6392\u5e8f\u6a21\u7d44 \u591a\u6a23\u7684\u90e8\u4efd\u8cc7\u8a0a\u7684\u65b9\u5f0f\uf92d\u8868\u793a\u5176\u8cc7\u8a0a\u9700\u6c42\u3002\u7b2c\u4e8c\u500b\u6a21\u7d44\u662f\u4e00\u500b\u5f48\u6027\u7684\u6aa2\uf96a\u6a5f\u5236\uff0c\u5177\u5099\u7cbe \u78ba\u6bd4\u5c0d\u8207\u90e8\u5206\u6bd4\u5c0d\uf978\u7a2e\u529f\u80fd\uff0c\u80fd\u4f9d\u7167\u4f5c\u8005\u7684\u8cc7\u8a0a\u638c\u63e1\u7a0b\ufa01\u800c\u8abf\u6574\uff0c\u5f9e\u8a9e\uf9be\u5eab\u4e2d\u6bd4\u5c0d\u5c0b\u627e \u7b26\u5408\u4f5c\u8005\u8a9e\u610f\u8868\u9054\u9700\u6c42\u7684\u7bc4\uf9b5\u3002\u7b2c\u4e09\u500b\u6a21\u7d44\u662f\u4e00\u500b\u8a55\u4f30\u6392\u5e8f\u7684\u6a5f\u5236\uff0c\u91dd\u5c0d\u627e\u51fa\u7684\u7bc4\uf9b5\uff0c 1. \u6e96\u78ba\u5b57\u8a5e(exact words)\uff1a\u6e96\u78ba\u5b57\u8a5e\u8b93\u4f7f\u7528\u8005\u63d0\u4f9b\u62fc\u5b57\u7121\u8aa4\u7684\u82f1\u6587\u55ae\u5b57\u6216\u55ae\u5b57\u7684\u7d44\u5408\u8868 \u6392\u5e8f\u6a21\u7d44\u7684\u76ee\u7684\u70ba\u91dd\u5c0d\u9078\u53d6\u51fa\uf92d\u7684\uf9b5\uf906\uff0c\u8a55\u4f30\u5176\u7b26\u5408\u4f7f\u7528\u8005\u7684\u8a9e\u610f\u8868\u9054\u9700\u6c42\u7a0b\ufa01\uff0c\u4e26\u5c07 \u9054\u5176\u8a9e\u8a00\u8cc7\u8a0a\u9700\u6c42\u7684\u8a8d\u77e5\u3002 \u4e4b\u6392\u5e8f\uff0c\u4ee5\u63d0\u5347\u4f7f\u7528\u8005\uf96b\u7167\uf9b5\uf906\u7684\u6210\u6548\u3002\u672c\u7814\u7a76\u63a1\u7528\u591a\u91cd\u5e8f\uf99c\u6392\uf99c(Multiple Sequence</td></tr><tr><td>\u8a55\u4f30\u5176\u7b26\u5408\u4f5c\u8005\u8a9e\u610f\u8868\u9054\u9700\u6c42\u7684\u7a0b\ufa01\uff0c\u4e26\u5c07\u4e4b\u6392\u5e8f\u5448\u73fe\uff0c\u4ee5\u63d0\u5347\u7bc4\uf9b5\uf96b\u7167\u7684\u4f7f\u7528\u6548\uf961\u3002 2. \u55ae\u5b57\u7684\u958b\u982d/\u7d50\u5c3e(prefix/suffix)\uff1a\u55ae\u5b57\u7684\u958b\u982d/\u7d50\u5c3e\u8b93\u4f7f\u7528\u8005\u4ee5\u55ae\u5b57\u7684\u90e8\u4efd\u8cc7\u8a0a\uf92d\u8868\u9054 Alignment -MSA)\u7684\u6280\u8853[14]\uff0c\u9032\ufa08\u67e5\u8a62\u689d\u4ef6\u8207\u9078\u53d6\uf9b5\uf906\u4e4b\u9593\u7684\u76f8\u95dc\u7a0b\ufa01\u8a55\u4f30\u8207\u6392\u5e8f\u3002</td></tr><tr><td>\u65b9\u5f0f\uf92d\u8a55\uf97e SAW \u7cfb\u7d71\u7684\u6210\u6548\u3002\u9019\uf978\u7a2e\u8a55\u4f30\u65b9\u5f0f\u90fd\u9a57\u8b49 SAW \u7cfb\u7d71\u80fd\u91dd\u5c0d ESL/EFL \u4f5c\u8005 3. \u4e00\u500b\u55ae\u5b57\u7684\u842c\u7528\u5b57(wildcard)\uff1a\u4e00\u500b\u55ae\u5b57\u7684\u842c\u7528\u5b57\u8b93\u4f7f\u7528\u8005\u8868\u793a\uf967\u78ba\u5b9a\u6216\uf967\u7279\u5b9a\u7684\u4e00 \u8a0a\u53d6\u5f97\u6210\u672c\u7684\u904e\u9ad8\u3001\u53ca\u8cc7\u8a0a\u4f7f\u7528\u7684\u6548\u76ca\u6709\u9650\u3002 (Sentence Assistance for Writing)\u7cfb\u7d71\uff0c\u4e26\u4ee5\u5ba2\u89c0\u7684\u6307\u6a19\uf97e\u6e2c\u548c\u4e3b\u89c0\u7684\u554f\u5377\u8abf\u67e5\uf978\u7a2e\u8a55\u4f30 \u7121\u6cd5\u6709\u6548\uf961\u7684\u9032\ufa08\u8aee\u8a62\u3002\u6211\u5011\u4ee5\"%\"\u7b26\u865f\uf92d\u4ee3\u8868\u55ae\u5b57\u958b\u982d\u6216\u7d50\u5c3e\u7684\u8868\u9054\u65b9\u5f0f\u3002 \u4f5c\u8005\u7684\u82f1\u8a9e\u5beb\u4f5c\u8996\u70ba\u4e00\u500b\u8cc7\u8a0a\u8655\uf9e4\u7684\u904e\u7a0b\uff0c\u9019\u9805\u5de5\u4f5c\u7684\u56f0\u96e3\u7665\u7d50\u5728\u65bc\u8cc7\u8a0a\uf97e\u7684\uf967\u8db3\u3001\u8cc7 \u6211\u5011\u4ee5\u4e0a\u8ff0\u65b9\u6cd5\u70ba\u57fa\u790e\uff0c\u767c\u5c55\uf9ba\u4e00\u500b\u4ee5\u5beb\u4f5c\u5354\u52a9\u70ba\u76ee\u7684\u7684\u8a9e\uf9be\u5eab\u4f7f\u7528\u5de5\u5177 -SAW \u5176\uf967\u5b8c\u6574\u7684\u8a8d\u77e5\u3002\u5728 ESL/EFL \u5beb\u4f5c\u904e\u7a0b\u4e2d\uff0c\u4f7f\u7528\u8005\u5e38\u56e0\u55ae\u5b57\u7684\u5b8c\u6574\u62fc\u5b57\u65b9\u6cd5\u907a\u5fd8\u800c \u7d66\u4e88\u591a\u500b\u9577\ufa01\uf967\u540c\u7684\u5e8f\uf99c(sequence</td></tr><tr><td>\u8fd1\uf98e\uf92d\uff0c\u8a9e\uf9be\u5eab\u7684\u767c\u5c55\u5e36\uf92d\u8a9e\u8a00\u7814\u7a76\u4e0a\u7684\u65b0\u9762\u5411\u3002\u8a9e\uf9be\u5eab\u662f\u5927\uf97e\u8a9e\u8a00\u4f7f\u7528\u60c5\u5f62\u7684\u7d00\uf93f\uff0c \u5728\u5176\u5beb\u4f5c\u904e\u7a0b\u4e2d\u7684\u8a9e\u8a00\u4f7f\u7528\u8cc7\u8a0a\u9700\u6c42\uff0c\u7d66\u4e88\u4e00\u5b9a\u7a0b\ufa01\u7684\u6eff\u8db3\u800c\u9054\u5230\u8aee\u8a62\u6307\u5f15\u7684\u6548\u679c\uff0c\u8b49 \u500b\u55ae\u5b57\uff0c\u7576\u6210\u6b32\u8aee\u8a62\u7684\u8868\u9054\u65b9\u5f0f\u7684\u4e00\u90e8\u5206\u3002\u6211\u5011\u4ee5\"#\"\u7b26\u865f\uf92d\u4ee3\u8868\u4e00\u500b\u55ae\u5b57\u7684\u842c\u7528\u5b57</td></tr><tr><td>\uf967\uf941\u662f\u4ee5\u6587\u5b57\u6216\u662f\u4ee5\u53e3\u8a9e\u65b9\u5f0f\u7684\u8a9e\u8a00\u8868\u9054\u5167\u5bb9\uff0c\u90fd\u53ef\u4ee5\u88ab\u5927\uf97e\u8490\u96c6\u5f59\u6574\u3001\u63cf\u8ff0\u6a19\u8a3b\uff0c\u800c \u660e\u672c\u7814\u7a76\u7684\u8a9e\u8a00\u8cc7\u8a0a\u6aa2\uf96a\u65b9\u6cd5\u78ba\u5be6\u80fd\u9054\u6210\u5beb\u4f5c\u5354\u52a9\u7684\u65e2\u5b9a\u76ee\u6a19\u3002 \u5716\u4e00\uff1aVIEW concordancer \u7684\u8a9e\u8a00\u8cc7\u8a0a\u63d0\u4f9b\u7bc4\uf9b5 \u5143\u7684\u8868\u9054\u65b9\u5f0f\u3002</td></tr></table>",
                "text": "\u4e00\u3001\u7dd2\uf941 \u5c0d\u65bc\u5927\u90e8\u5206\u7684\u5beb\u4f5c\u76ee\u7684\u800c\u8a00\uff0c\u5beb\u4f5c\u662f\u4e00\u7a2e\u6df1\ufa01\u7684\u8868\u9054\u904e\u7a0b\uff0c\u9700\u8981\u8a73\u76e1\u800c\u6df1\u5165\u7684\u63cf\u8ff0\u80fd\uf98a\uff0c \u7cbe\u6e96\u800c\u56b4\u8b39\u7684\u8a9e\u610f\u5448\u73fe\uff0c\u53ca\u9069\u7576\u7684\u8a5e\u8a9e\u9078\u64c7\u5b89\u6392\u3002\u5c0d\u65bc\u82f1\u8a9e\u70ba\u975e\u6bcd\u8a9e\u7684\u5b78\u7fd2\u8005(English as Second Language -ESL/English as Foreign Language -EFL)\uf92d\uf96f\uff0c\u82f1\u8a9e\u5beb\u4f5c\u5c24\u5176\u662f\u4e00\u500b\u56f0 \u96e3\u7684\u904e\u7a0b\uff0c\u5e38\u5e38\u6703\u56e0\u70ba\u55ae\u5b57\u3001\u642d\u914d\u5b57(collocation)\u3001\u8a5e\u6027\u7d44\u5408\u3001\u5e38\u7528\u7247\u8a9e\u3001\uf906\u578b\u7d50\u69cb\u7b49 \u65b9\u9762\u7684\u8a9e\u8a00\u77e5\uf9fc\uf967\u8db3\uff0c\u6216\u662f\u53d7\u5230\u6bcd\u8a9e\u8a8d\u77e5\u8207\u7fd2\u6163\u7684\u727d\u5236\u5f71\u97ff\uff0c\u800c\u9020\u6210\u7528\u8a5e\u3001\u8a9e\u6cd5\u3001\u751a\u81f3 \u8a9e\u610f\u4e0a\u7684\u932f\u8aa4[1]\u3002\u540c\u6642\uff0c\u82f1\u8a9e\u5beb\u4f5c\u4e5f\u662f\u4e00\u500b\u8cbb\u6642\u7684\u904e\u7a0b\uff0c ESL/EFL \u7684\u4f5c\u8005\u5e38\u5e38\u8981\u82b1\u8cbb \u8a31\u591a\u6642\u9593\uff0c\u5728\u81ea\u8eab\uf967\u8db3\u7684\u8a9e\u8a00\u77e5\uf9fc\u4e2d\u641c\u5c0b\u9069\u7576\u7684\u8868\u9054\u65b9\u5f0f\uff0c\u6216\u662f\u8981\u501f\u52a9\u65bc\u5b57\u5178\u3001\u8fad\u5178\u7b49 \u5404\u7a2e\u8a9e\u8a00\u8cc7\u6e90\u5de5\u5177\uff0c\u53d6\u5f97\u521d\u968e\u7684\u8a5e\u8a9e\u8cc7\u8a0a\u3002\u4f46\u662f\uff0c\u5927\uf97e\u6642\u9593\u7684\u6295\u5165\u5f80\u5f80\u50c5\u80fd\u7372\u5f97\u6709\u9650\u7684 \u5beb\u4f5c\u54c1\u8cea\u7684\u63d0\u5347\uff0c\u800c\u9020\u6210\u8a31\u591a ESL/EFL \u4f5c\u8005\u7684\u632b\u6298\u8207\u969c\u7919\u3002\u7e3d\u7d50\u800c\u8a00\uff0c\uf974\u628a ESL/EFL"
            },
            "TABREF2": {
                "num": null,
                "type_str": "table",
                "html": null,
                "content": "<table><tr><td>(\u56db)\u5be6\u4f5c\u7cfb\u7d71\u8207\u8655\uf9e4\uf9ca\u7a0b\u7bc4\uf9b5 \u6211\u5011\u4ee5\u4e0a\u8ff0\u7684\u8a9e\u8a00\u8cc7\u8a0a\u6aa2\uf96a\u65b9\u6cd5\u5be6\u969b\u958b\u767c\u5b8c\u6210\u4e00\u500b\u7a31\u70ba SAW(Sentence Assistance for Writing)\u7684\u96db\u578b\u7cfb\u7d71\uff0c\u5176\u8868\u9054\u5143\u7d20\u6a21\u7d44\u7684\u90e8\u5206\u5305\u542b\uf9ba\uf9d1\u7a2e\u8a8d\u77e5\u8868\u793a\u7684\u57fa\u672c\u65b9\u5f0f\uff1b\u6aa2\uf96a\u6a21 \u7d44\u5247\u53ef\u5206\u5225\u9032\ufa08\u5b8c\u6574\u6bd4\u5c0d\u8207\u90e8\u5206\u6bd4\u5c0d\uff0c\u4ee5\u9078\u53d6\u7b26\u5408\u67e5\u8a62\u689d\u4ef6\u7684\uf9b5\uf906\uff1b\u6700\u5f8c\uff0c\u6392\u5e8f\u6a21\u7d44\u5247 \u63a1\u7528 MSA \u6f14\u7b97\u6cd5\u9032\ufa08\u6aa2\uf96a\u51fa\uf92d\u7684\uf9b5\uf906\u8207\u67e5\u8a62\u689d\u4ef6\u4e4b\u9593\u7684\u6392\uf99c\u65b9\u5f0f\u4e26\u8a55\u4f30\u5176\u76f8\u95dc\u7a0b\ufa01\uff0c \u518d\u4f9d\u7167\u76f8\u95dc\u6027\u4f9d\u5e8f\u63a8\u85a6\u5448\u73fe\u7d66\u4f7f\u7528\u8005\u3002SAW \u7cfb\u7d71\u7684\u6574\u9ad4\u67b6\u69cb\u5982\u5716\u4e8c\u6240\u793a\u3002 \u5716\u4e8c\uff1aSAW \u96db\u578b\u7cfb\u7d71\u67b6\u69cb\u5716 \u5b8c\u6574\u6bd4\u5c0d MSA \u4f7f\u7528\u8005\u8a8d\u77e5\u8868\u793a \u6392\u5e8f\u6a21\u7d44 \u6aa2\uf96a\u6a21\u7d44 \u90e8\u5206\u6bd4\u5c0d \u7d50\u679c\u63a8\u85a6\u8207\uf96b\u7167 \u8868\u9054\u5143\u7d20\u6a21\u7d44 prefix suffix POS wildcard exact subsequence (\u4e00)\u8a55\u4f30\u65b9\u6cd5 \u70ba\uf9ba\u5ba2\u89c0\u8a55\u4f30 SAW \u7cfb\u7d71\u5c0d\u65bc ESL/EFL \u4f5c\u8005\u7684\uf96b\u7167\u6210\u6548\uff0c\u6211\u5011\u9032\ufa08\uf9ba\uf978\u7a2e\u8a55\u4f30\u65b9\u6cd5\uff0c\u5206 \u5225\u70ba\u6a21\u64ec\u6e2c\u8a66(\u5be6\u9a57\u5ba2\u89c0\u8a55\u4f30)\u548c\u554f\u5377\u8abf\u67e5(\u4f7f\u7528\u8005\u4e3b\u89c0\u8a55\u4f30)\u3002\u5728\u6a21\u64ec\u6e2c\u8a66\u65b9\u9762\uff0c\u6211\u5011\u5f9e \u82f1\u8a9e\u80fd\uf98a\u6e2c\u9a57\u7684\u8a66\u984c\u4e2d\uff0c\u9078\u53d6\u91cd\u8981\u7684\u82f1\u8a9e\u8868\u9054\u65b9\u5f0f(\u5982\u55ae\u5b57\u3001\u7247\u8a9e\u3001\uf906\u578b\u7d50\u69cb\u7b49)\uff0c\u6a21\u64ec ESL/EFL \u4f5c\u8005\u65bc\u5beb\u4f5c\u4e0a\u53ef\u80fd\u906d\u9047\u7684\u56f0\u96e3\uff0c\u518d\u7531\u5be6\u9a57\u8005\u63d0\u4f9b\uf969\u7a2e\uf967\u540c\u7684\u67e5\u8a62\u65b9\u5f0f\uff0c\u6e2c\u8a66 SAW \u7cfb\u7d71\u7684\u6aa2\uf96a\u7d50\u679c\uff0c\u4e26\u89c0\u5bdf\u8a55\u4f30\u5176\u6548\u80fd\u3002\u8a66\u984c\uf92d\u6e90\u5305\u62ec\u5927\u5b78\uf997\u62db\u548c\u5927\u5b78\u5b78\u6e2c\u7684\u8003\u984c\uff0c \u5171\u8a08 16 \u671f\uff0c\u53ca TOFEL \u7684\u8003\u984c\uff0c\u5171\u8a08 11 \u671f\u3002\u7e3d\u5171\u9078\u53d6 45 \u500b\u55ae\u5b57\u7247\u8a9e\u300112 \u500b\uf906\u578b\u7d50\u69cb (\u542b \u6709 subsequence \u8868\u9054\u5143\u7d20\u7684\uf906\u578b)\u7576\u8a66\u984c\u6a23\u672c\u3002\u672c\u5be6\u9a57\u5f9e\u4e8c\u500b\u89d2\ufa01\u5206\u6790\u6210\u6548\uff0c\u7b2c\u4e00\u500b\u89d2\ufa01 \u70ba\u55ae\u5b57\u7247\u8a9e\u7684\u8a55\u4f30\uff1a\u91dd\u5c0d 18 \u500b\u55ae\u5b57\u7247\u8a9e\uff0c\u4ee5 SAW \u7cfb\u7d71\uf967\u540c\u7684\u8868\u9054\u5143\u7d20(exact\u3001prefix\u3001 POS\u3001wildcard)\uff0c\u6a21\u64ec\uf967\u540c\u8a8d\u77e5\u7a0b\ufa01\u7684\u4f7f\u7528\u8005\uff0c\u4ee5\u8a55\u4f30\u5c0d\u6aa2\uf96a\u7d50\u679c\u6240\u80fd\u5f97\u5230\u7684\uf96b\u7167\u8aee\u8a62 \u6210\u6548\u3002\u7b2c\u4e8c\u500b\u89d2\ufa01\u70ba\uf906\u578b\u7d50\u69cb\u548c\u8a8d\u77e5\u932f\u8aa4\u7684\u8a55\u4f30\uff1a\u91dd\u5c0d 12 \u500b\uf906\u578b\u7d50\u69cb\uff0c\u6a21\u64ec\u4f7f\u7528\u8005\u5728 \u8a8d\u77e5\u6709\u90e8\u5206\u932f\u8aa4\u7684\u60c5\u6cc1\u4e0b\uff0cSAW \u7cfb\u7d71\u80fd\u7d66\u4e88\u4f7f\u7528\u8005\uf96b\u7167\u8aee\u8a62\u4e0a\u7684\u6210\u6548\u3002 \u554f\u5377\u8abf\u67e5\u7684\u4e3b\u8981\u76ee\u7684\u70ba\u4ee5\u4f7f\u7528\u8005\u7684\u89c0\u9ede\u8a55\u4f30 SAW \u7cfb\u7d71\u6240\u63d0\u4f9b\u7684\uf96b\u7167\u8aee\u8a62\u6210\u6548\u3002\u6211\u5011\u9078 \u53d6\u90e8\u5206\u82f1\u8a9e\u80fd\uf98a\u6e2c\u9a57\u8a66\u984c\uff0c\u5305\u62ec\u55ae\u9078\u984c 16 \u984c\u53ca\u7ffb\u8b6f\u984c 3 \u984c\uff0c\u7531\u53d7\u6e2c\u8005\u6a21\u64ec\u82f1\u8a9e\u80fd\uf98a\u6e2c \u9a57\uff0c\u518d\u4ee5\u5206\u7d44\u6e2c\u9a57\u7684\u7d50\u679c\uff0c\u6bd4\u8f03\u53d7\u6e2c\u8005\u65bc\u4f7f\u7528 SAW \u7cfb\u7d71\u5354\u52a9\u548c\u672a\u4f7f\u7528 SAW \u7cfb\u7d71\u5354\u52a9 \u4e4b\u9593\u6e2c\u9a57\u6210\u7e3e\u4e0a\u7684\u5dee\uf962\u3002\u53e6\u4e00\u90e8\u4efd\u7684\u6bd4\u8f03\u70ba\u540c\u4e00\u7d44\u7684\u53d7\u6e2c\u8005\uff0c\u5728\u672a\u4f7f\u7528 SAW \u7cfb\u7d71\u524d\u548c \u4f7f\u7528 SAW \u7cfb\u7d71\u5f8c\u7684\u6e2c\u9a57\u6210\u7e3e\u5dee\uf962\u3002 \u70ba\u5ba2\u89c0\u4e26\u4e14\u78ba\u5be6\u7684\u8a55\u4f30 SAW \u7cfb\u7d71\u7684\u6210\u6548\uff0c\u672c\u7814\u7a76\uf96b\u8003\u63a8\u85a6\u7cfb\u7d71\u7684\u8a55\u4f30\u65b9\u5f0f[16]\uff0c\u64ec\u5b9a 5 \u9805\u89c0\u5bdf\u6307\u6a19\uff0c\u8a73\u8ff0\u5982\u4e0b\uff1a 1. \u8a66\u984c\u6e2c\u9a57\u5206\uf969\uff1a\u672c\u7814\u7a76\u85c9\u7531\u8a66\u984c\u6a21\u64ec\u5b78\u7fd2\u8005\u65bc\u5beb\u4f5c\u4e0a\u7684\u56f0\u96e3\uff0c\u800c\u8a66\u984c\u5177\u5099\uf9ba\u932f\u8aa4\u9078 \u9805\u53ca\u6b63\u78ba\u7684\u7b54\u6848\uff0c\u56e0\u6b64\uff0c\u53ef\u85c9\u7531\u5b78\u7fd2\u8005\u7684\u6e2c\u9a57\u5206\uf969\uf92d\u8a55\u4f30\u7cfb\u7d71\u7684\uf96b\u7167\u8aee\u8a62\u6210\u6548\u3002 2. \u63a8\u85a6\ufa01\uff1a\u5c0d\u65bc\u7cfb\u7d71\u63d0\u4f9b\u7684\u8a9e\u8a00\u4f7f\u7528\u8cc7\u8a0a\uff0c\u63a8\u85a6\ufa01\u662f\u4f7f\u7528\u8005\u6240\u8a8d\u70ba\u771f\u6b63\u5be6\u7528\u7684\u7a0b\ufa01\u3002 \u672c\u7814\u7a76\u5c0d\u65bc\u63a8\u85a6\ufa01\u63a1\u53d6 4 \u7a2e\u9ad8\u4f4e\u7a0b\ufa01\u7684\uf97e\ufa01\uff0c\u5373\u9ad8\u63a8\u85a6\ufa01\u3001\u4e2d\u63a8\u85a6\ufa01\u3001\u4f4e\u63a8\u85a6\ufa01\u3001 \u7121\u63a8\u85a6\ufa01\u3002\u63a8\u85a6\ufa01\u662f\u7531\u4f7f\u7528\u8005\u4e3b\u89c0\u8a55\u4f30\u7684\uff0c\u672c\u7814\u7a76\u5c0d\u65bc\u63a8\u85a6\ufa01\u7684\u8861\uf97e\u5c07\u4ee5\u4f7f\u7528\u8005\u7684 \u56de\u61c9\u65b9\u5f0f\u53d6\u5f97\u3002 3. \u6eff\u610f\ufa01\uff1a\u5c0d\u65bc\u7cfb\u7d71\u63d0\u4f9b\u7684\u8a9e\u8a00\u4f7f\u7528\u8cc7\u8a0a\uff0c\u6eff\u610f\ufa01\u662f\u4f7f\u7528\u8005\u5c0d\u5176\u6574\u9ad4\u6eff\u610f\u7684\u7a0b\ufa01\u3002\u672c \u7814\u7a76\u5c0d\u65bc\u6eff\u610f\ufa01\u63a1\u53d6 5 \u500b\u9ad8\u4f4e\u7a0b\ufa01\u7684\uf97e\ufa01\uff0c\u5373\u975e\u5e38\u6eff\u610f\u3001\u6eff\u610f\u3001\u666e\u901a\u3001\uf967\u6eff\u610f\u3001\u975e \u5e38\uf967\u6eff\u610f\u3002\u6eff\u610f\ufa01\u4ea6\u662f\u7531\u4f7f\u7528\u8005\u4e3b\u89c0\u8a55\u4f30\u7684\uff0c\u672c\u7814\u7a76\u5c0d\u65bc\u6eff\u610f\ufa01\u7684\u8861\uf97e\u5c07\u4ee5\u4f7f\u7528\u8005 \u7684\u56de\u61c9\u65b9\u5f0f\u53d6\u5f97\u3002 4. \u7b26\u5408\ufa01\uff1a\u7b26\u5408\ufa01\u662f\u4f9d\u64da\u4f7f\u7528\u8005\u63d0\u4f9b\u7684\u67e5\u8a62\u689d\u4ef6\uff0c\u7cfb\u7d71\u6bd4\u5c0d\u9078\u53d6\u51fa\u7684\u7d50\u679c\u548c\u4f7f\u7528\u8005\u9810 \u671f\u7b54\u6848\u4e0a\u7684\u76f8\u4f3c\ufa01\u3002\u672c\u7814\u7a76\u5c0d\u65bc\u7b26\u5408\ufa01\u7684\u8a55\u4f30\u65b9\u5f0f\u70ba\u5728\u4e00\u5b9a\u9078\u53d6\uf969\uf97e\u7684\uf9b5\uf906\u4e2d\uff0c\u771f \u6b63\u7b26\u5408\u5b78\u7fd2\u8005\u9810\u671f\u7b54\u6848\u7684\u6bd4\u503c(number of relevant retrieved / number of retrieved)\uff0c\u4e5f \u5c31\u662f\u4e00\u822c\u8cc7\u8a0a\u6aa2\uf96a\u4e2d\u7684\u6e96\u78ba\ufa01(precision)\u4e4b\u5b9a\u7fa9\u3002 5. \uf9b5\uf906\u6210\u672c\uff1a\uf9b5\uf906\u6210\u672c\u70ba\u4f7f\u7528\u8005\u95b1\uf95a\u5b8c\u4e00\u5b9a\uf969\uf97e\u7684\u55ae\u5b57\u5e8f\uf99c\u6240\u9700\u82b1\u8cbb\u7684\u6210\u672c\uff0c\u5be6\u9a57\u4e0a \u4ee5\u6240\u9700\u95b1\uf95a\u7684\u82f1\u8a9e\u55ae\u5b57\u5b57\uf969\u70ba\u6210\u672c\uf97e\u3002\uf9b5\uf906\u6210\u672c\u7684\u8a55\u4f30\u70ba\u53cd\u61c9\u7c21\u6f54\u6709\uf98a\u7684\uf9b5\uf906\uff0c\u5c0d \u65bc\uf96b\u7167\u8aee\u8a62\u4e0a\u7684\u6548\u679c\u53ef\u80fd\u8f03\u597d\u3002 (\u4e8c)\u5be6\u9a57\u7d50\u679c 1. \u6a21\u64ec\u6e2c\u8a66\u4e4b\u55ae\u5b57\u7247\u8a9e\u8a55\u4f30 \u55ae\u5b57\u7247\u8a9e\u7684\u8a55\u4f30\u4e3b\u8981\u85c9\u7531\u8a66\u984c\u6a21\u64ec\u5b78\u7fd2\u8005\u65bc\u5beb\u4f5c\u4e0a\u6240\u9700\u7684\u5354\u52a9\uff0c\u4e26\u5047\u8a2d\uf967\u540c\u7b26\u5408\ufa01\u7684\u67e5 \u8a62\u689d\u4ef6\uff0c\u8a55\u4f30\u5c0d\u8a8d\u77e5\u7a0b\ufa01\uf967\u540c\u7684\u5b78\u7fd2\u8005\u6240\u80fd\u63d0\u4f9b\u7684\u5354\u52a9\u6210\u6548\u3002\u5be6\u9a57\u4ee5 10 \u500b\u7531\uf978\u500b\u82f1\u8a9e \u55ae\u5b57\u7d44\u6210\u7684\u7247\u8a9e\u53ca 8 \u500b\u7531\u591a\u500b\u82f1\u8a9e\u55ae\u5b57\u7d44\u6210\u7684\u7247\u8a9e\u7576\u6e2c\u8a66\u6a23\u672c\uff0c\u4e26\u63a1\u7528 exact\u3001prefix\u3001 POS\u3001wildcard \u7b49\u8868\u9054\u5143\u7d20\u5f62\u6210\uf967\u540c\u7684\u67e5\u8a62\u689d\u4ef6\uff0c\u6a21\u64ec\u5b78\u7fd2\u8005\u53ef\u80fd\u7684\uf967\u540c\u8a8d\u77e5\u53ca\u6240\u5f97\u5230 \u7684\uf967\u540c\uf96b\u7167\u7d50\u679c\uff0c\u518d\u4ee5\u7d50\u679c\u7684\u7b26\u5408\ufa01\u9032\ufa08\u8a55\uf97e\u3002 \u672c\u5be6\u9a57\u63a1\u7528 4 \u5927\uf9d0\u578b\u7684\u67e5\u8a62\u65b9\u5f0f\uff1a(1)exact\uff1a\u5b8c\u6574\u63d0\u4f9b\uf978\u500b\u82f1\u8a9e\u55ae\u5b57\uff0c\u5982 specialize in\u3002 (2)prefix\uff1a\u5c07\uf978\u500b\u82f1\u8a9e\u55ae\u5b57\u5176\u4e2d\u4e00\u500b\u7684\u7b2c\u4e00\u500b\u82f1\u8a9e\u5b57\u6bcd\u7576\u6210 prefix \u7684\u5f15\u5c0e\u539f\u5247\uff0c\u5982\u4ee5 specialize in \u9664\uf9ba\uf978\u500b\u82f1\u8a9e\u55ae\u5b57\u7d44\u6210\u7684\u7247\u8a9e\u4e4b\u5916\uff0c\u672c\u7814\u7a76\u4ea6\u6e2c\u8a66\uf9ba\u4e09\u500b\u548c\u56db\u500b\u82f1\u8a9e\u55ae\u5b57\u7d44\u6210\u7684\u7247\u8a9e\uff0c \u8a55\u4f30\u4e0a\u8ff0\uf967\u540c\u67e5\u8a62\u689d\u4ef6\u7684\u6aa2\uf96a\u7d50\u679c\u4e4b\u7b26\u5408\ufa01\u3002\u548c\u4e0a\u8ff0\uf978\u500b\u82f1\u8a9e\u55ae\u5b57\u7247\u8a9e\u67e5\u8a62\u65b9\u5f0f\u552f\u4e00\uf967 \u540c\u7684\u662f\uff0c\u4e09\u500b\u82f1\u8a9e\u55ae\u5b57\u7684 prefix \u67e5\u8a62\u65b9\u5f0f\u53ef\u80fd\u7684\u67e5\u8a62\u689d\u4ef6\u6709\u4e09\u500b\uff0c\uf9b5\u5982\u4ee5 a proportion of \u70ba\uf9b5\uff0c\u53ef\u80fd\u6709 a% proportion of\u3001a p% of\u3001a proportion o% \u4e09\u7a2e\uf967\u540c\u7684\u67e5\u8a62\u689d\u4ef6\uff0c\u6b64\u6642\uff0c \u5be6\u9a57\u662f\u53d6\u6b64\u4e09\u7a2e\u67e5\u8a62\u689d\u4ef6\u6240\u5f97\u6aa2\uf96a\u7d50\u679c\u7684\u5e73\u5747\u7b26\u5408\ufa01\u3002\u540c\u6a23\u7684\uff0c\u4ee5 a proportion of \u70ba\uf9b5\uff0c POS \u67e5\u8a62\u65b9\u5f0f\u53ef\u80fd\u6709 O proportion of\u3001a N of\u3001a proportion P \u4e09\u7a2e\uf967\u540c\u7684\u67e5\u8a62\u689d\u4ef6\uff1b\u800c wildcard \u67e5\u8a62\u65b9\u5f0f\u53ef\u80fd\u6709 # proportion of\u3001a # of\u3001a proportion # \u4e09\u7a2e\uf967\u540c\u7684\u67e5\u8a62\u689d\u4ef6\uff0c \u6b64\u6642\uff0c\u5be6\u9a57\u662f\u53d6\u6b64\u4e09\u7a2e\u67e5\u8a62\u689d\u4ef6\u6240\u5f97\u6aa2\uf96a\u7d50\u679c\u7684\u5e73\u5747\u7b26\u5408\ufa01\u3002\u53e6\u5916\uff0c\u95dc\u65bc\u56db\u500b\u82f1\u8a9e\u55ae\u5b57 \u7684 prefix\u3001POS\u3001wildcard \u67e5\u8a62\u65b9\u5f0f\u53ef\u80fd\u7684\u67e5\u8a62\u689d\u4ef6\u6709\u56db\u500b\uff0c\u6b64\u6642\uff0c\u5be6\u9a57\u662f\u53d6\u6b64\u56db\u7a2e\u67e5 \u8a62\u689d\u4ef6\u6240\u5f97\u6aa2\uf96a\u7d50\u679c\u7684\u5e73\u5747\u7b26\u5408\ufa01\u3002 SAW Recommendation(first 10 sentences) 0 20 40 60 80 100 specialize in responsible for familiarity with relevance to deal with Match(%) Exact_Match AVG_Prefix AVG_POS AVG_Wildcard SAW Recommendation(first 10 sentences) 0 20 40 60 80 100 essential to native to composed of determined by guard against Match(%) Exact_Match AVG_Prefix AVG_POS AVG_Wildcard \u5716\u4e94\uff1a\u55ae\u5b57\u7247\u8a9e\u7684\u7b26\u5408\ufa01\u8a55\u4f30 \u5be6\u9a57\u7d50\u679c\u986f\u793a\uff0c\u67e5\u8a62\u689d\u4ef6\u4e2d\u5305\u542b\u7684\u5b57\uf969\u8d8a\u591a\uff0cSAW \u7cfb\u7d71\u63d0\u4f9b\u7684\uf96b\u7167\u8cc7\u8a0a\u7684\u7b26\u5408\ufa01\u8d8a\u9ad8\uff0c \u539f\u56e0\u5728\u65bc\u591a\u500b\u82f1\u8a9e\u55ae\u5b57\u7d44\u6210\u7684\u7247\u8a9e\uff0c\u5176\u672c\u8eab\u7d50\u69cb\u6027\u8f03\u5f37\uff0c\u5728\u5176\u4e2d\u4e00\u500b\u5b57\u70ba\u6a21\u7cca\u7684\u60c5\u6cc1 \u4e0b\uff0c\u5b78\u7fd2\u8005\u9084\u662f\u80fd\u5f97\u5230\u8f03\u91dd\u5c0d\u5176\u8868\u9054\u9700\u6c42\u7684\uf96b\u7167\u5354\u52a9\u3002\u800c\u7d50\u679c\u4ea6\u986f\u793a\uff0cprefix \u67e5\u8a62\u65b9\u5f0f \u7684\u6210\u6548\u9ad8\u65bc POS \u67e5\u8a62\u65b9\u5f0f\uff0cPOS \u67e5\u8a62\u65b9\u5f0f\u6210\u6548\u9ad8\u65bc wildcard \u67e5\u8a62\u65b9\u5f0f\uff0c\u986f\u793a prefix \u8868 \u9054\u5143\u7d20\u70ba\u9650\u5236\u689d\u4ef6\u8f03\u5f37\u7684\u67e5\u8a62\u689d\u4ef6\uff0c\u800c wildcard \u8868\u9054\u5143\u7d20\u7684\u9650\u5236\u689d\u4ef6\u6700\u70ba\u5bec\u9b06\u3002 2. \uf906\u578b\u7d50\u69cb\u548c\u8a8d\u77e5\u932f\u8aa4\u7684\u8a55\u4f30 \u672c\u5be6\u9a57\u9078\u53d6 12 \u500b\uf906\u578b\u7d50\u69cb\uff0c\u5305\u62ec\uff1aenable * to\uff1bderive * from\uff1bexpose * to\uff1bnot only * but also\uff1bwould rather * than\uff1bdistinguish * from\uff1bdivide * from\uff1bexpand * into\uff1bprovide * with\uff1b \u4f9b\u7684\u70ba\u4f4e\u7b26\u5408\ufa01\u7684\uf96b\u7167\uf9b5\uf906\u3002\u7b2c\u56db\u7d44\u65bd\u6e2c\u6642\u5148\u5728\u7121\uf96b\u7167\uf9b5\uf906\u7684\u60c5\u5f62\u4e0b\u586b\u7b54\u540c\u6a23\u7684 19 \u9053 \u8a66\u984c\uff0c\u6536\u56de\u8a66\u984c\u5f8c\uff0c\u518d\u65bc\u63d0\u4f9b\u9ad8\u7b26\u5408\ufa01\uf96b\u7167\uf9b5\uf906\u7684\u60c5\u5f62\u4e0b\uff0c\u586b\u7b54\u540c\u6a23\u7684 19 \u9053\u8a66\u984c\u3002\u554f \u5377\u65bd\u6e2c\u7684\u5c0d\u8c61\u70ba 125 \u4f4d\u7684\u4e94\u5c08\u751f\u3002\u800c\u4f9d\u64da\u82f1\u8a9e\u5b57\u5f59\u7a0b\ufa01\u6e2c\u8a66\u554f\u5377[17]\uff0c\u53ef\u77ad\u89e3\u53d7\u6e2c\u8005\u5c0d \u65bc\u82f1\u8a9e\u5b57\u5f59\u7684\u8a8d\u77e5\ufa01\uff0c\u4ea6\u7d04\uf976\u4ee3\u8868\u5176\u82f1\u6587\u7a0b\ufa01\u3002\u53d7\u6e2c\u7684\u4e94\u5c08\u751f\u5c0d\u65bc\u82f1\u8a9e\u5e38\u7528 1000 \u55ae\u5b57 \u7d04\u6709 80.44%\u7684\u8a8d\u77e5\ufa01\uff1b\u5c0d\u65bc\u82f1\u8a9e\u5e38\u7528 2000 \u55ae\u5b57\u6709 46.10%\u7684\u8a8d\u77e5\ufa01\u3002\u800c\u6839\u64da Nation \u7684 \u7814\u7a76[18]\uff0c\u82f1\u8a9e\u5e38\u7528 1000 \u55ae\u5b57\u51fa\u73fe\u65bc\u82f1\u8a9e\u6587\u7ae0\u7684\u983b\uf961\u70ba 74%\uff0c\u82f1\u8a9e\u5e38\u7528 2000 \u55ae\u5b57\u983b\uf961 \u70ba 81%\u3002\u63db\u800c\u8a00\u4e4b\uff0c\u554f\u5377\u8abf\u67e5\u6e2c\u8a66\u5c0d\u8c61\u5e73\u5747\uf967\u8d85\u904e 2000 \u55ae\u5b57\u7684\u5b57\u5f59\u7a0b\ufa01\uff0c\u4ea6\u5373\u5e73\u5747\u6bcf \u9664\u55ae\u9078\u8a66\u984c\u5916\uff0c\u554f\u5377\u6709\u4e09\u984c\u7ffb\u8b6f\u8a66\u984c\uff0c\u7ffb\u8b6f\u8a66\u984c\u80fd\uf901\u78ba\u5be6\u5730\u6a21\u64ec\u5b78\u7fd2\u8005\u65bc\u5beb\u4f5c\u4e0a\u6240\u9047\u5230 \u7684\u56f0\u96e3\u3002\u5728\u4e0a\u8ff0 86 \u4efd\u554f\u5377\u4e2d\uff0c\u6709 44 \u4efd\u554f\u5377\u70ba\u53d7\u6e2c\u8005\u6c92\u6709\u4f7f\u7528\u7cfb\u7d71\u7684\u5354\u52a9\u800c\u586b\u7b54\u8a66\u984c\uff0c 42 \u4efd\u70ba\u7cfb\u7d71\u7d66\u4e88\u540c\u6a23\u7684\uf96b\u7167\uf9b5\uf906\u800c\u586b\u7b54\u8a66\u984c\u3002\u7ffb\u8b6f\u8a66\u984c\u7684\u8a55\u5206\u6a19\u6e96\u70ba\uf906\u578b\u7d50\u69cb 50%\u3001 \u7528\u8a5e 25%\u3001\u5176\u4ed6 25%(\u5305\u542b\u8a9e\u610f\u3001\u6587\u6cd5\u7b49)\uff0c\u4e09\u984c\u8a66\u984c\u4ee5\u6eff\u5206 10 \u5206\u70ba\u6a19\u6e96\u3002\u7d50\u679c\u986f\u793a\uff0c \u7121\u4f7f\u7528\u7cfb\u7d71\uf96b\u7167\u7684\u5e73\u5747\u5206\uf969\u70ba 4.20\uff0c\u6a19\u6e96\u5dee\u70ba 2.56\uff1b\u800c\u6709\u4f7f\u7528\u7cfb\u7d71\uf96b\u7167\u7684\u5e73\u5747\u5206\uf969\u70ba 5.83\uff0c\u6a19\u6e96\u5dee\u70ba 2.46\uff0c\u5206\uf969\u9ad8\u65bc\u7121\u4f7f\u7528\u7cfb\u7d71\u7684\u554f\u5377 1.63 \u5206\u3002\u540c\u6a23\u4f9d\u64da\u7d71\u8a08\u7684\u986f\u8457\u6027\u5206\u6790\uff0c \u5047\u8a2d\u4ee5\u7121\u4f7f\u7528\u7cfb\u7d71\uf96b\u7167\u7684\u5c0d\u8c61\u70ba\u6bcd\u9ad4\uff0c\u800c\u4f7f\u7528\u7cfb\u7d71\uf96b\u7167\u7684\u5c0d\u8c61\u70ba\u6a23\u672c\uff0c\u5728\u986f\u8457\u6c34\u6e96 5% the same * \u5be6\u9a57\u7d50\u679c\u5982\u5716\uf9d1\u986f\u793a\uff0c\u5728\uf906\u578b\u7d50\u69cb\u67e5\u8a62\u689d\u4ef6\u4e0b SAW \u7cfb\u7d71\u5747\u80fd\u5c0d\u5b78\u7fd2\u8005\u63d0\u4f9b\u91dd\u5c0d\u6027\u7684\uf96b \u7167\u5354\u52a9(\u7b26\u5408\ufa01 100%)\u3002\u5728\u5b78\u7fd2\u8005\u90e8\u5206\u932f\u8aa4\u7684\u8a8d\u77e5\u65b9\u9762\uff0c\u67e5\u8a62\u7d50\u679c\u6703\u56e0\uf906\u578b\u7d50\u69cb\u95dc\u9375\u5b57 \u5f7c\u6b64\u9593\u7684\u5f37\ufa01\u800c\u6709\uf967\u540c\u7684\u6210\u6548\uff0c\uf9b5\u5982 enable * to \u56e0\u672c\u8eab\u95dc\u9375\u5b57\u8f03\u5f37\uff0c\u6545\u5373\u4f7f\u5c07 to \u8f38\u5165 \u6210 and \u6216 but\uff0c\u4ea6\u80fd\u85c9\u7531\u7cfb\u7d71\u7684\u90e8\u5206\u6bd4\u5c0d\u5f97\u5230\u4e00\u5b9a\u7684\uf96b\u7167\u5354\u52a9\u3002\u76f8\u53cd\u7684\uff0c\u8af8\u5982 either * or \u548c so * that \u5b50\uf906\u4e2d\u7684\u95dc\u9375\u5b57\u5f7c\u6b64\u7684\u95dc\uf99a\u5f37\ufa01\uf967\u5f37\uff0c\u7e31\u4f7f\u7d93\u904e\u90e8\u5206\u6bd4\u5c0d\u7684\u9078\u53d6\u65b9\u5f0f\uff0c\u63d0\u4f9b \u7d66\u5b78\u7fd2\u8005\u7684\uf96b\u7167\u7b26\u5408\ufa01\u4ea6\u6709\u9650\u3002\u4f46\u662f\uff0c\u7531\u554f\u5377\u8abf\u67e5\u7684\u7d50\u679c\u986f\u793a\uff0c\u5b78\u7fd2\u8005\u4ea6\u80fd\u65bc\u4f4e\u7b26\u5408\ufa01 \u7684\u60c5\u6cc1\u4e0b\uff0c\u89c0\u5bdf\u63a8\u85a6\uf9b5\uf906\u7d50\u679c\u800c\u8a55\u4f30\u672c\u8eab\u662f\u5426\u6709\u932f\u8aa4(false query)\u6216\u8005\uf967\u5920\u56b4\u8b39(vague query)\u7684\u67e5\u8a62\u689d\u4ef6\u767c\u751f\uff0c\u53ef\u4ee5\uf96f\u9593\u63a5\u8b93\u5b78\u7fd2\u8005\u5f97\u5230\u4e00\u5b9a\u7684\uf96b\u7167\u5354\u52a9\u3002 SAW Recommendation(first 10 sentences) 0 20 40 60 80 100 enable * to derive * from expose * to not only * but also would rather * than distinguish * from Match(%) Subsequence Partial_Errors SAW Recommendation(first 10 sentences) 0 20 40 60 80 100 divide * from expand * into provide * with the same * as either * or so * that Match(%) Subsequence Partial_Errors \u5716\uf9d1\uff1asubsequence \u548c partial errors \u67e5\u8a62\u689d\u4ef6\u7d50\u679c\u4e4b\u7b26\u5408\ufa01\u8a55\u4f30 3. \u554f\u5377\u8abf\u67e5\u8a55\u4f30 \u8a66\u984c\u56de\u61c9\u6709 19 \u9053\u8a66\u984c(16 \u984c\u55ae\u9078 3 \u984c\u7ffb\u8b6f)\uff0c SAW \u7cfb\u7d71\u5206\u5225\u63d0\u4f9b\u8207\u500b\u5225\u8a66\u984c\u76f8\u5c0d\u61c9\u7684 BNC \u8a9e\uf9be\u5eab\u4e2d\u7684\u76f8\u95dc\uf9b5\uf906\uff0c\u505a\u70ba\u53d7\u6e2c\u8005\u4f5c\u7b54\u6642\u7684\uf96b\u7167\u3002\u85c9\u7531\u8a66\u984c\u6e2c\u9a57\u5206\uf969\u4e0a\u7684\u5dee\uf962\uff0c \u4ee5\u53ca\u53d7\u6e2c\u8005\u63d0\u4f9b\u7684\uf96b\u7167\u63a8\u85a6\ufa01\u8207\u6eff\u610f\ufa01\uff0c\u8a55\u4f30 SAW \u7cfb\u7d71\u7d66\u4e88\u53d7\u6e2c\u8005\u5beb\u4f5c\uf96b\u7167\u5354\u52a9\u7684\u7a0b \ufa01\u3002\u5be6\u9a57\u5c07\u53d7\u6e2c\u8005\u5206\u70ba\u56db\u7d44\uff1a\u7b2c\u4e00\u7d44\u586b\u7b54 19 \u9053\u8a66\u984c\u6642\uff0c\u4e26\u7121\u4efb\u4f55\uf96b\u7167\uf9b5\uf906\uff1b\u7b2c\u4e8c\u7d44\u586b \u7b54\u540c\u6a23\u7684 19 \u9053\u8a66\u984c\uff0c\u800c\u4e14\u6709\u9ad8\u7b26\u5408\ufa01\u7684\uf96b\u7167\uf9b5\uf906\uff1b\u7b2c\u4e09\u7d44\u586b\u7b54\u540c\u6a23\u7684 19 \u9053\u8a66\u984c\uff0c\u4f46\u63d0 \u4e94\u500b\u55ae\u5b57\u4e2d\u53d7\u6e2c\u8005\u7d04\u6709\u4e00\u500b\u55ae\u5b57\uf967\u719f\uf9fc\u3002 \u554f\u5377\u56de\u6536\u7684\u4efd\uf969\u70ba 125 \u4efd\uff0c\u5176\u4e2d\u7121\u6548\u7684\u554f\u5377\u6709 12 \u4efd\uff0c\u5305\u62ec\u7e73\u4ea4\u767d\u5377\u548c\u731c\u984c\u4f5c\u7b54(\u5982\u9078\u9805 \u53ea\u6709 1 \u548c 2\uff0c\u53d7\u6e2c\u8005\u9078 3 \u6216 4\uff1b\u6291\u6216\u53d7\u6e2c\u8005\u5c0d\u65bc\u9078\u9805\u5168\u4f5c\u7b54 1\u30012\u30013 \u6216 4)\u3002\u7e3d\u8a08\u6709\u6548\u554f \u5377\u70ba 113 \u4efd\uff0c\u5176\u4e2d\u6709\u7121\u4f7f\u7528\u7cfb\u7d71\u4e0a\u7684\u5340\u5206\u554f\u5377\u5171 86 \u4efd\uff0c\u7d30\u5206\u70ba\u7121\u4f7f\u7528\u7cfb\u7d71\uf96b\u7167\u7684 44 \u4efd\u3001 \u4f7f\u7528\u9ad8\u7b26\u5408\ufa01\uf96b\u7167\uf9b5\uf906\u7684 20 \u4efd\u3001\u4f7f\u7528\u4f4e\u7b26\u5408\ufa01\uf96b\u7167\uf9b5\uf906\u7684 22 \u4efd\u554f\u5377\uff1b\u800c\u4f7f\u7528\u7cfb\u7d71\u524d\u548c \u4f7f\u7528\u7cfb\u7d71\u5f8c\u7684\u554f\u5377\u5171 27 \u4efd\u3002 \u5728\u6709\u7121\u4f7f\u7528\u7cfb\u7d71\u4e0a\u7684\u5340\u5225\u6709\u6548\u554f\u5377 86 \u4efd\u4e2d\uff0c\u55ae\u9078\u8a66\u984c\u6709 44 \u4efd\u554f\u5377\u70ba\u53d7\u6e2c\u8005\u6c92\u6709\u4f7f\u7528\u7cfb \u7d71\u7684\u5354\u52a9\u800c\u586b\u7b54\u8a66\u984c\u300120 \u4efd\u70ba\u4f7f\u7528\u7cfb\u7d71\u8f03\u9ad8\u7b26\u5408\ufa01\u7684\uf96b\u7167\uf9b5\uf906\u586b\u7b54\u8a66\u984c(\u5e73\u5747\u7b26\u5408\ufa01\u70ba 62.48%)\u300122 \u4efd\u70ba\u4f7f\u7528\u7cfb\u7d71\u8f03\u4f4e\u7b26\u5408\ufa01\u7684\uf96b\u7167\uf9b5\uf906\u586b\u7b54\u8a66\u984c(\u5e73\u5747\u7b26\u5408\ufa01\u70ba 34.69%)\u3002\u5176 \u4e2d\u55ae\u9078\u8a66\u984c\u5171\u6709 16 \u984c\uff0c\u6bcf\u984c\u70ba 0.625 \u5206\uff0c\u6eff\u5206\u70ba 10 \u5206\u3002\u800c\u7531\u7d50\u679c\u767c\u73fe\u6709\u4f7f\u7528\u7cfb\u7d71\uf96b\u7167 \u7684\u5206\uf969\u5747\u9ad8\u65bc\u672a\u4f7f\u7528\u7cfb\u7d71\u5354\u52a9\u7684\u5206\uf969\uff0c\u5176\u4e2d\u7121\u4f7f\u7528\u7cfb\u7d71\uf96b\u7167\u7684\u5206\uf969\u70ba 4.42 \u5206\uff0c\u6a19\u6e96\u5dee \u70ba 1.95\uff1b\u800c\u4f7f\u7528\u9ad8\u7b26\u5408\ufa01\u7684\uf96b\u7167\uf9b5\uf906\u5176\u5206\uf969\u70ba 6.69 \u5206\uff0c\u6a19\u6e96\u5dee\u70ba 2.23\uff0c\u6bd4\u7121\u4f7f\u7528\u7cfb\u7d71 \uf96b\u7167\u7684\u5206\uf969\u9ad8 2.27 \u5206\uff1b\u800c\u4f7f\u7528\u4f4e\u7b26\u5408\ufa01\u7684\uf96b\u7167\uf9b5\uf906\u5176\u5206\uf969\u70ba 5.97 \u5206\uff0c\u6a19\u6e96\u5dee\u70ba 1.85\uff0c \u6bd4\u7121\u4f7f\u7528\u7cfb\u7d71\uf96b\u7167\u7684\u5206\uf969\u9ad8 1.55 \u5206\uff0c\u8b49\u5be6 SAW \u7cfb\u7d71\u80fd\u7d66\u4e88\u5b78\u7fd2\u8005\u4e00\u5b9a\u7a0b\ufa01\u7684\u5beb\u4f5c\u4e0a\u7684 \uf96b\u7167\u5354\u52a9\u3002\u6b64\u90e8\u5206\u7684\u76f8\u95dc\u554f\u5377\u5206\u6790\u7d50\u679c\u5f59\u6574\u65bc\u8868\u4e00\u4e2d\u3002 \u8868\u4e00\uff1a\u6709\u7121\u4f7f\u7528\u7cfb\u7d71\u4e4b\u554f\u5377\u7d50\u679c\u5206\u6790 \u7121\u4f7f\u7528\u7cfb\u7d71 \u7b26\u5408\ufa01\u9ad8\u7684\uf96b\u7167 \u7b26\u5408\ufa01\u4f4e\u7684\uf96b\u7167 \u55ae\u9078\u8a66\u984c\u5206\uf969(0~10) 4.42 6.69 5.97 \u7ffb\u8b6f\u8a66\u984c\u5206\uf969(0~10) 4.2 5.83 \u55ae\u9078\u8a66\u984c\u63a8\u85a6\ufa01(1~4) ---2.78 2.70 \u7ffb\u8b6f\u8a66\u984c\u63a8\u85a6\ufa01(1~4) ---2.80 \uf906\u578b\u9577\ufa01(1~3) ---2.09 \u6eff\u610f\ufa01(1~5) ---2.76 \u70ba\uf9ba\u9a57\u8b49\u4e0a\u8ff0\u7d50\u679c\u4e26\u975e\u5076\u7136\u767c\u751f\uff0c\u6211\u5011\u4ee5\u7d71\u8a08\u5b78\u7684\u986f\u8457\u6c34\u6e96(significance level)\uf96f\u660e SAW \u7cfb\u7d71\u78ba\u5be6\u6709\u4e00\u5b9a\u7684\u5354\u52a9\u6210\u6548\u986f\u8457\u6027\u3002\u5047\u8a2d\u6bcd\u9ad4\u7684\u53d7\u6e2c\u5206\uf969\u70ba\u5e38\u614b\u5206\u4f48\uff0c\u4e26\u4ee5\u7121\u4f7f\u7528\u7cfb\u7d71 \uf96b\u7167\u7684\u8a66\u984c\u5206\uf969\u7576\u6bcd\u9ad4\u7684\u53d7\u6e2c\u5206\uf969\u3002\u5047\u8a2d Null Hypothesis (H 0 ) \u70ba SAW \u7cfb\u7d71\u6c92\u6709\u6210\u6548(\u5e73 \u5747\u5206\uf969\u70ba 4.42)\uff0cAlternative Hypothesis (H 1 )\u70ba SAW \u7cfb\u7d71\u6709\u6210\u6548(\u5e73\u5747\u5206\uf969\u5927\u65bc 4.42)\uff0c\u5728 \u986f\u8457\u6c34\u6e96 5%\u4e0b\uff0c\u5c0d\u65bc\u9ad8\u7b26\u5408\ufa01\uf96b\u7167\uf9b5\uf906\u7684 20 \u4efd\u6a23\u672c\uff0c\u53ef\u5f97\u62d2\u7d55 H 0 \u7684\uf9f6\u754c\u5206\uf969\u70ba 4.42+(1.65*1.95/(20) 1/2 ) =5.14\uff0c\u56e0\u5be6\u9a57\u6240\u5f97\u7684\u5e73\u5747\u5206\uf969\u70ba 6.69\uff0c\u5927\u65bc\uf9f6\u754c\u5206\uf969 5.14\uff0c\u6240 \u4ee5\u53ef\u4ee5\u5426\u5b9a H 0 \uff0c\u4ea6\u5373\u5728 5%\u986f\u8457\u6c34\u6e96\u4e0b SAW \u7cfb\u7d71\u5c0d\u65bc\u53d7\u6e2c\u8005\u662f\u6709\u5354\u52a9\u6210\u6548\u7684\u3002\u4f9d\u64da\u540c \u6a23 \u7684 \u7d71 \u8a08 \u5206 \u6790 \u65b9 \u6cd5 \uff0c \u5c0d \u65bc \u4f4e \u7b26 \u5408 \ufa01 \uf96b \u7167 \uf9b5 \uf906 \u7684 22 \u500b \u6a23 \u672c \uff0c \u5176 \uf9f6 \u754c \u5206 \uf969 \u70ba 4.42+(1.65*1.95/(22) 1/2 )=5.10\uff0c\u800c\u4f7f\u7528 SAW \u7cfb\u7d71\u5f97\u5230\u7684\u5206\uf969\u70ba 5.97\uff0c\u56e0 5.97 \u5927\u65bc\uf9f6\u754c\u5206 \uf969 5.10\uff0c\u8b49\u660e\u5728 5%\u986f\u8457\u6c34\u6e96\u4e0b SAW \u7cfb\u7d71\u5c0d\u65bc\u53d7\u6e2c\u8005\u4ecd\u7136\u662f\u6709\u5354\u52a9\u6210\u6548\u7684\u3002 \u4e0b\uf9f6\u754c\u5206\uf969\u70ba 4.97\uff0c\u56e0 5.83 \u5927\u65bc 4.97\uff0c\u5373\u5728 5%\u986f\u8457\u6c34\u6e96\u4e0b SAW \u7cfb\u7d71\u5c0d\u65bc\u53d7\u6e2c\u8005\u7ffb\u8b6f \u8a66\u984c\u662f\u6709\u5354\u52a9\u6210\u6548\u7684\u3002 \u63a8\u85a6\ufa01\u662f\u7531\u4f7f\u7528\u8005\u8a55\u4f30\u7cfb\u7d71\u662f\u5426\u6709\u5be6\u7528\u6027(usefulness)\uff0c\u554f\u5377\u4ee5\u5206\uf969 1~4 \u5206\u70ba\u6a19\u6e96\uff0c\u5206\uf969 \u8d8a\u9ad8\u4ee3\u8868\u53d7\u6e2c\u8005\u8d8a\u89ba\u5f97\u6709\u5be6\u7528\u6027\u3002\u800c\u7d50\u679c\u986f\u793a\u5728\u4f7f\u7528\u7cfb\u7d71\uf96b\u7167\u7684 44 \u4efd\u554f\u5377\u4e2d\uff0c\u5176\u4e2d\u55ae \u9078\u8a66\u984c\u4f7f\u7528\u9ad8\u7b26\u5408\ufa01\uf96b\u7167\uf9b5\uf906\u7684\u554f\u5377\u5e73\u5747\u70ba 2.78\u3001\u4f7f\u7528\u4f4e\u7b26\u5408\ufa01\uf96b\u7167\uf9b5\uf906\u7684\u554f\u5377\u5e73\u5747\u70ba 2.70\uff0c\u800c\u7ffb\u8b6f\u8a66\u984c\u5e73\u5747\u70ba 2.80\u3002\u6574\u9ad4\u800c\u8a00\uff0c\u6709\u4f7f\u7528\u7cfb\u7d71\u7684\u5e73\u5747\u63a8\u85a6\ufa01\u4ecb\u65bc 2.7~2.8 \u4e4b\u9593\uff0c \u8868\u793a\u53d7\u6e2c\u8005\u8a8d\u70ba SAW \u7cfb\u7d71\u662f\u6709\u4e00\u5b9a\u7684\u5354\u52a9\u5be6\u7528\u6027\u3002\u7136\u6b64\uf969\u503c\uf967\u7b97\u592a\u9ad8\u7684\u539f\u56e0\u662f\u53d7\u9650\u65bc \u53d7\u6e2c\u8005\u5e73\u5747\u5b57\u5f59\u7a0b\ufa01\uf967\u5230 2000 \u5b57\uff0cBNC \u8a9e\uf9be\u5eab\u7684\uf9b5\uf906\u7684\u7a0b\ufa01\u8f03\u6df1\uff0c\u9020\u6210\u53d7\u6e2c\u8005\u767c\u751f\uf967 \u751a\u77ad\u89e3\uf9b5\uf906\u7684\uf9fa\u6cc1\uff0c\u4ee5\u81f4\u65bc\u53d7\u6e2c\u8005\u8a8d\u5b9a SAW \u7cfb\u7d71\u7684\uf96b\u7167\u7d50\u679c\u63a8\u85a6\ufa01\uf967\u9ad8\uff0c\u800c\u4e0a\u8ff0\u7684\u5047 \u8a2d\u4ea6\u7d93\u7531\u53d7\u6e2c\u8005\u7684\u554f\u5377\u56de\u994b\u8b49\u5be6\u6b64\u60c5\u6cc1\u662f\u767c\u751f\u7684\u3002 \u8868\u4e8c\uff1a\u4f7f\u7528\u7cfb\u7d71\u524d\u5f8c\u4e4b\u554f\u5377\u7d50\u679c\u5206\u6790 \u4f7f\u7528\u7cfb\u7d71\u524d \u4f7f\u7528\u7cfb\u7d71\u5f8c \u55ae\u9078\u8a66\u984c\u5206\uf969(0~10) 4.99 6.53 \u7ffb\u8b6f\u8a66\u984c\u5206\uf969(0~10) 4.56 5.42 \u55ae\u9078\u8a66\u984c\u63a8\u85a6\ufa01(1~4) ---2.59 \u7ffb\u8b6f\u8a66\u984c\u63a8\u85a6\ufa01(1~4) ---2.53 \uf906\u578b\u9577\ufa01(1~3) ---2.16 \u6eff\u610f\ufa01(1~5) ---3.16 \u5728\u4f7f\u7528\u7cfb\u7d71\u4e0a\u524d\u5f8c\u7684\u5340\u5225\u6709\u6548\u554f\u5377 27 \u4efd\u4e2d\uff0c\u55ae\u9078\u8a66\u984c\u5171\u6709 16 \u984c\uff0c\u6bcf\u984c\u70ba 0.625 \u5206\uff0c\u6eff \u5206\u70ba 10 \u5206\u3002\u76f8\u95dc\u554f\u5377\u5206\u6790\u7d50\u679c\u5f59\u6574\u65bc\u8868\u4e8c\u4e2d\u3002\u4f7f\u7528\u7cfb\u7d71\u524d\u53d7\u6e2c\u8005\u7684\u5e73\u5747\u5206\uf969\u70ba 4.99\uff0c \u6a19\u6e96\u5dee\u70ba 2.49\uff1b\u4f7f\u7528\u7cfb\u7d71\u5f8c\u7684\u5e73\u5747\u5206\uf969\u70ba 6.53\uff0c\u6a19\u6e96\u5dee\u70ba 2.00\uff0c\u5206\uf969\u589e\u52a0 1.54 \u5206\u3002\u540c \u6a23\u7684\uff0c\uf974\u4ee5\u4f7f\u7528\u7cfb\u7d71\u524d\u7684\u7d50\u679c\u70ba\u6bcd\u9ad4\uff0c\u4f7f\u7528\u7cfb\u7d71\u5f8c\u7684\u7d50\u679c\u70ba\u6a23\u672c\uff0c\u5728 5%\u986f\u8457\u6c34\u6e96\u4e0b\u7684 \uf9f6\u754c\u5206\uf969\u70ba 5.71\u3002\u56e0 6.53 \u5927\u65bc 5.71\uff0c\u8b49\u5be6 SAW \u7cfb\u7d71\u5c0d\u65bc\u55ae\u9078\u8a66\u984c\u662f\u6709\u5354\u52a9\u6210\u6548\u7684\u3002 \u5728\u4f7f\u7528\u7cfb\u7d71\u4e0a\u524d\u5f8c\u7684\u5340\u5225\u4e2d\uff0c\u7ffb\u8b6f\u8a66\u984c\u540c\u6a23\u4ee5\uf906\u578b\u7d50\u69cb 50%\u3001\u7528\u8a5e 25%\u3001\u5176\u4ed6 25%(\u5305 \u542b\u8a9e\u610f\u3001\u6587\u6cd5\u7b49)\u70ba\u8a55\u5206\u6a19\u6e96\uff0c\u6eff\u5206\u70ba 10 \u5206\u3002\u800c\u4f7f\u7528\u7cfb\u7d71\u524d\u7684\u5e73\u5747\u5206\uf969\u70ba 4.56\uff0c\u6a19\u6e96\u5dee \u70ba 2.59\uff1b\u4f7f\u7528\u7cfb\u7d71\u5f8c\u7684\u5e73\u5747\u5206\uf969\u70ba 5.42\uff0c\u6a19\u6e96\u5dee\u70ba 2.60\uff0c\u5206\uf969\u589e\u52a0\u70ba 0.86 \u5206\u3002\u540c\u6a23\u7684\uff0c \u4ee5\u4f7f\u7528\u7cfb\u7d71\u524d\u7684\u7d50\u679c\u7576\u6bcd\u9ad4\uff0c\u800c\u4f7f\u7528\u7cfb\u7d71\u5f8c\u7684\u7d50\u679c\u7576\u6a23\u672c\uff0c\u5728 5%\u986f\u8457\u6c34\u6e96\u4e0b\u7684\uf9f6\u754c\u5206 \uf969\u70ba 5.38\u3002\u56e0 5.42 \u5927\u65bc 5.38\uff0c\u8b49\u5be6 SAW \u7cfb\u7d71\u5c0d\u65bc\u7ffb\u8b6f\u8a66\u984c\u662f\u6709\u5354\u52a9\u6210\u6548\u7684\u3002 (\u4e09)\u7d50\u679c\u8a0e\uf941 \u5728\u554f\u5377\u8abf\u67e5\u4e2d\uff0c\u7814\u7a76\u8b49\u5be6\u7121\uf941\u5f9e\u6709\u7121\u4f7f\u7528\u7cfb\u7d71\u7684\u89d2\ufa01\u6216\u8005\u4f7f\u7528\u7cfb\u7d71\u524d\u5f8c\u7684\u89d2\ufa01\uff0cSAW \u7cfb\u7d71\u5982\u5f9e\u8a66\u984c\u5206\uf969\u7684\u89c0\u9ede\ufa00\u5165\uff0c\u80fd\u7d66\u4e88\u5b78\u7fd2\u8005\u5206\uf969\u4e0a\u7684\u5354\u52a9\uff1b\u800c\u5f9e\u63a8\u85a6\ufa01\u7684\u89c0\u9ede\ufa00\u5165\uff0c \u65bc\u524d\u4e94\uf906\u63a8\u85a6\uf9b5\uf906\u4e2d\u90fd\u53ef\u4ee5\u5f97\u77e5\u6b63\u78ba\u7684\u52d5\u8a5e\u5f62\u5f0f\uff0c\u7b26\u5408\ufa01\u70ba 5/5\uff1b\u800c\u4f7f\u7528 would rather * than *\u7684\u67e5\u8a62\u689d\u4ef6\u53ef\u4ee5\u65bc\u524d\u5341\uf906\u63a8\u85a6\uf9b5\uf906\u4e2d\u7684\u4e03\u500b\uf9b5\uf906\u5f97\u77e5\u6b63\u78ba\u7684\u52d5\u8a5e\u5f62\u5f0f\uff0c\u5176\u7b26\u5408\ufa01 \u70ba 7/10\uff0c\u8b49\u5be6\u4ee5\u4e0a\u8ff0\uf978\u7a2e\u67e5\u8a62\u65b9\u6cd5\uff0cSAW \u7cfb\u7d71\u5747\u80fd\u7d66\u4e88\u5b78\u7fd2\u8005\u76f8\u95dc\u7684\uf96b\u7167\u5354\u52a9\u3002 \u4e94\u3001\u76f8\u95dc\u7814\u7a76 \u8fd1\uf98e\uf92d\uff0c\u904b\u7528\u8cc7\u8a0a\u6280\u8853\u5354\u52a9\u82f1\u8a9e\u5b78\u7fd2\u7684\u7814\u7a76\u5f15\u8d77\u6975\u591a\u7684\u8208\u8da3\uff0c\u6211\u5011\u50c5\u8a0e\uf941\u8207\u672c\u7814\u7a76\u8f03\u70ba \u76f4\u63a5\u76f8\u95dc\u8005\u3002\u9996\u5148\u662f CMU \u7684 REAP \u7cfb\u7d71[19]\uff0c\u5176\u4e3b\u8981\u76ee\u7684\u70ba\u63d0\u4f9b\u5b78\u7fd2\u8005\u82f1\u8a9e\u95b1\uf95a\u7684\u5354 \u52a9\uff0c\u7814\u7a76\u65b9\u6cd5\u5305\u62ec\u4f9d\u7167\u5b78\u7fd2\u8005\u8a8d\u77e5\u7a0b\ufa01\uff0c\u4ee5\u6e2c\u9a57\u7684\u65b9\u5f0f\u8a55\u4f30\u5b78\u7fd2\u8005\u7684\u8a5e\u5f59\u7a0b\ufa01\uff0c\u5728\u4f7f\u7528 REAP \u7cfb\u7d71\u524d\u5f8c\uff0c\u662f\u5426\u6709\u986f\u8457\u6027\u7684\u5dee\uf962\uff0c\u4e26\u4ee5\u554f\u5377\u8abf\u67e5\u548c\u7d71\u8a08\uf969\u503c\u6574\u5408\u5206\u6790 REAP \u7cfb\u7d71 \u7684\u6210\u6548\u3002\u672c\u7814\u7a76\uf96b\u8003\uf9ba REAP \u7cfb\u7d71\u7684\u8a55\u4f30\u65b9\u5f0f\u8a55\u4f30 SAW \u7cfb\u7d71\u7684\u6210\u6548\u3002\u53e6\u5916\uff0cGsearch \u7cfb\u7d71\u4e3b\u8981\u76ee\u7684\u70ba\u5728\u8a9e\uf9be\u5eab\u4e2d\u5efa\uf9f7\uf906\u6cd5\u6e96\u5247(syntactic criteria)\uff0c\u800c\u5b78\u7fd2\u8005\u53ef\u4ee5\u4f9d\u64da\u5efa\uf9f7\u7684 \uf906\u6cd5\u6e96\u5247\u6709\u6548\uf961\u5730\u5f9e\u8a9e\uf9be\u5eab\u4e2d\u9078\u53d6\u5408\u5b9c\u7684\uf9b5\uf906[20]\u3002Gsearch \u7cfb\u7d71\u63d0\u4f9b\u7684\uf906\u6cd5\u6e96\u5247\u8f03\u70ba \u5f48\u6027\uff0c\u5c0d\u65bc\uf967\u540c\u7684\u5b78\u7fd2\u8005\u80fd\u4f9d\u64da\u591a\u6a23\u7684\uf906\u6cd5\u6e96\u5247\u6709\u6548\uf961\u5730\u67e5\u8a62\u3002\u672c\u7814\u7a76\u5ef6\u7e8c\u5176\uf9e4\uf9a3\uff0c\u4f46 \u7279\u5225\u95dc\u6ce8\u65bc\u8a8d\u77e5\uf967\u8db3\u60c5\u5f62\u4e0b\u4e4b\u8868\u9054\u65b9\u5f0f\u67e5\u8a62\uff0c\u800c\u9019\u4e5f\u662f ESL/EFL \u4f5c\u8005\u6700\u70ba\u95dc\u9375\u7684\u56f0\u96e3\u3002 \u6700\u5f8c\uff0cTANGO \u7cfb\u7d71[21]\u63d0\u4f9b\u4e2d\u82f1\u96d9\u8a9e\u7684\u8a5e\u8a9e\u642d\u914d\u95dc\u4fc2\u62bd\u53d6\u8207\u67e5\u8a62\uff0c\u5c24\u5176\u8457\u91cd\u5728\u52d5\u8a5e\u8207 \u540d\u8a5e\u7684\u76f8\u4e92\u642d\u914d\u8cc7\u8a0a\u3002\u672c\u7814\u7a76\u63d0\u4f9b\u8f03\u591a\u5143\u7684\u9700\u6c42\u8868\u9054\u8207\u67e5\u8a62\u65b9\u5f0f\uff0c\u4e26\u5c0d\u6aa2\uf96a\u7d50\u679c\u9032\ufa08\u8a55 \u4f30\u8207\u6392\u5e8f\uff0c\u4ee5\u63d0\u5347\u4f7f\u7528\u8005\uf96b\u7167\u7684\u6548\uf961\u3002\u540c\u6642\uff0c\u672c\u7814\u7a76\u4ee5\uf901\u56b4\u8b39\u7684\u65b9\u5f0f\u8a55\uf97e\u7cfb\u7d71\u7684\u6210\u6548\u3002 \uf9d1\u3001\u7d50\uf941 \u672c\u7814\u7a76\u9451\u65bc\u73fe\u6709\u8a9e\u8a00\u8cc7\u6e90\u5de5\u5177\u5c0d\u65bc ESL/EFL \u5b78\u7fd2\u8005\u65bc\u82f1\u8a9e\u5beb\u4f5c\u4e0a\u63d0\u4f9b\u7684\u5354\u52a9\u529f\u80fd\u6709 \u9650\uff0c\u81f4\uf98a\u65bc\u63d0\u4f9b\u4e00\u7a2e\u8a9e\u8a00\u8cc7\u8a0a\u6aa2\uf96a\u65b9\u6cd5\uff0c\u80fd\u6709\u6548\uf961\u5730\u6839\u64da\uf967\u540c\u5b78\u7fd2\u8005\u7684\u8a8d\u77e5\u7a0b\ufa01\u4e88\u4ee5\u4e00 \u5b9a\u7684\uf96b\u7167\u8aee\u8a62\u5354\u52a9\uff0c\u800c\u6b64\u65b9\u6cd5\u5206\u70ba\u8868\u9054\u5143\u7d20\u6a21\u7d44\u3001\u6aa2\uf96a\u6a21\u7d44\u3001\u6392\u5e8f\u6a21\u7d44\u4e09\u500b\u90e8\u4efd\u3002\u70ba\uf9ba \u8b49\u5be6\u6b64\u65b9\u6cd5\u7684\u6210\u6548\uff0c\u6211\u5011\u5be6\u4f5c\u5b8c\u6210 SAW \u7cfb\u7d71\u3002SAW \u7cfb\u7d71\u53ef\u4f7f\u7528\u6e96\u78ba\u5b57\u8a5e(exact)\u3001\u55ae\u5b57 \u7684\u958b\u982d/\u7d50\u5c3e(prefix/suffix)\u3001\u82f1\u8a9e\u8a5e\u6027(POS)\u3001\u4e00\u500b\u55ae\u5b57\u7684\u842c\u7528\u5b57\u5143(wildcard)\u3001\uf967\u9650\u5b9a\u7684 \u5b50\uf906(subsequence)\u7b49\u505a\u70ba\u8868\u9054\u5143\u7d20(expression elements)\uff0c\u4e26\u53ef\u4f7f\u7528\u5b8c\u6574\u6bd4\u5c0d\u6216\u90e8\u5206\u6bd4\u5c0d \u7684\uf978\u7a2e\u6bd4\u5c0d\u65b9\u5f0f\uff0c\u65bc\u6aa2\uf96a\u6a21\u7d44\u4e2d\u9078\u53d6\uf9b5\uf906\uff0c\u800c\u5f8c\u5c0d\u65bc\u9078\u53d6\u7684\uf9b5\uf906\u4f7f\u7528 MSA \u6280\u8853\u70ba\u6392\u5e8f \u6a21\u7d44\u7684\u8a55\u4f30\u65b9\u6cd5\uff0c\u6700\u5f8c\u63d0\u4f9b\u63a8\u85a6\u7684\u76f8\u95dc\uf9b5\uf906\u4f9b\u5b78\u7fd2\u8005\uf96b\u7167\u3002 \u70ba\uf9ba\u8a55\u4f30 SAW \u7cfb\u7d71\u7684\u6210\u6548\uff0c\u5be6\u9a57\u5206\u70ba\u6a21\u64ec\u6e2c\u8a66(\u5be6\u9a57\u5ba2\u89c0\u8a55\u4f30)\u548c\u554f\u5377\u8abf\u67e5(\u4f7f\u7528\u8005\u4e3b\u89c0 \u8a55\u4f30)\uf978\u7a2e\u65b9\u5f0f\uff0c\u85c9\u7531\u8a66\u984c\u5206\uf969\u3001\u63a8\u85a6\ufa01\u3001\u6eff\u610f\ufa01\u3001\u7b26\u5408\ufa01\u3001\uf906\u578b\u6210\u672c\u4e94\u9805\u6307\u6a19\uff0c\u9a57\u8b49 \u5728\u4e0a\u8ff0\uf978\u7a2e\u8a55\u4f30\u65b9\u6cd5\u4e0b\uff0cSAW \u7cfb\u7d71\u5747\u80fd\u5f97\u5230\u4e00\u5b9a\u7a0b\ufa01\u7684\u5354\u52a9\u6210\u6548\u3002\u9664\u6b64\u4e4b\u5916\uff0c\u6211\u5011\u4e26 \u4f9d\u64da\u5b78\u751f\u7684\u4f5c\u6587\u7bc4\uf9b5\uff0c\u5c07\u7bc4\uf9b5\u6a21\u64ec\u8f38\u5165\u65bc SAW \u7cfb\u7d71\u4e2d\uff0c\u767c\u73fe SAW \u7cfb\u7d71\u4ea6\u80fd\u7d66\u4e88\u5b78\u7fd2 \u8005\u4e00\u5b9a\u7684\uf96b\u7167\u5354\u52a9\uff0c\uf901\u52a0\u9a57\u8b49 SAW \u7cfb\u7d71\u7684\u5354\u52a9\u6210\u6548\u3002\u7d9c\u89c0\u4ee5\u4e0a\u5e7e\u9ede\uff0c\u672c\u7814\u7a76\u7684\u8a9e\u8a00\u8cc7 \u5b78\u7fd2\u8005\u4ea6\u8a8d\u70ba\u80fd\u5f9e would rather V than V \u7684\u67e5\u8a62\u689d\u4ef6\u53ef\u4ee5 \u8a0a\u6aa2\uf96a\u65b9\u6cd5\u78ba\u5be6\u80fd\u91dd\u5c0d ESL/EFL \u5b78\u7fd2\u8005\u65bc\u82f1\u8a9e\u5beb\u4f5c\u6642\u7d66\u4e88\u9069\ufa01\u7684\u5354\u52a9\u3002</td></tr></table>",
                "text": "sum-of-pair score \u7684\u5206\uf969\uff0cSAW \u7cfb\u7d71\u6703\u63a8\u85a6\u7d66\u5b78\u7fd2\u8005\u7684\uf9b5\uf906\u4f9d\u5e8f\u70ba S 3 \u3001S 2 \u3001S 1 \uff1a S 3 . Increasingly acid rain is a problem in Europe too. \uf9b5\uf906\uff0c\u800c\u975e\u5982\u5716\u4e00\u6240\u793a\u4ee5\u4e0a\u4e0b\u6587\u70ba\u4e3b\u7684 concordance\uff0c\u7cfb\u7d71\u63d0\u4f9b\u6bcf\u9801 10 \u500b\uf9b5\uf906\uff0c\u4f7f\u7528\u8005 \u53ef\u8996\u9700\u8981\u6aa2\u8996\u4e0b\u9801\u7684\uf9b5\uf906\u3002\u53e6\u5916\uff0cSAW \u7cfb\u7d71\u4e5f\u63d0\u4f9b\uf9ba\u6aa2\uf96a\u7d50\u679c\u7684\u7d71\u8a08\u8cc7\u8a0a\u3002\uf9b5\u5982\uff0cdeter% by \u7684\u6aa2\uf96a\u7d50\u679c\uf9b5\uf906\u4e2d\uff0c\u4f7f\u7528 determined by \u7684\uf969\uf97e\u6709 676 \u500b\uff0c\u4f7f\u7528 deterred by \u7684\uf969\uf97e\u6709 27 \u500b\uff0c\u4f7f\u7528 determination by \u7684\uf969\uf97e\u6709 12 \u500b\uff0c\u4f7f\u7528 deterrence by \u7684\uf969\uf97e\u6709 2 \u500b\uff0c\u4f7f\u7528 determine by \u7684\uf969\uf97e\u6709 2 \u500b\uff0c\u4f7f\u7528 determining by \u7684\uf969\uf97e\u6709 1 \u500b\uff0c\u7b49\u7b49\u3002\u9019\u4e9b\u7d71\u8a08\u8cc7\u8a0a\u4e5f \u5c07\u63d0\u4f9b\u7d66\u4f7f\u7528\u8005\uf901\u9032\u4e00\u6b65\u5224\u65b7\u6216\u63a2\uf96a\u7684\u6307\u5f15\u3002 \u70ba\uf9b5\uff0c\u5c07\u5206\u70ba s% in \u548c specialize i% \uf978\u7a2e\u53ef\u80fd\u7684\u67e5\u8a62\u689d\u4ef6\uff0c\u518d\u53d6\u6b64\uf978\u7a2e\u67e5 \u8a62\u689d\u4ef6\u6240\u5f97\u6aa2\uf96a\u7d50\u679c\u7684\u5e73\u5747\u7b26\u5408\ufa01\u3002(3)POS\uff1a\u5c07\uf978\u500b\u82f1\u8a9e\u55ae\u5b57\u5176\u4e2d\u4e00\u500b\u4ee5 POS \u8868\u9054\u5143 \u7d20\u67e5\u8a62\uff0c\u5982\u4ee5 specialize in \u70ba\uf9b5\uff0c\u5c07\u5206\u70ba V in \u548c specialize P \uf978\u7a2e\u53ef\u80fd\u7684\u67e5\u8a62\u689d\u4ef6\uff0c\u540c \u6a23\u7684\uff0c\u53d6\uf978\u7a2e\uf967\u540c\u67e5\u8a62\u689d\u4ef6\u6240\u5f97\u6aa2\uf96a\u7d50\u679c\u7684\u5e73\u5747\u7b26\u5408\ufa01\u3002(4)wildcard\uff1a\u5c07\uf978\u500b\u82f1\u8a9e\u55ae\u5b57 \u5176\u4e2d\u4e00\u500b\u4ee5 wildcard \u8868\u9054\u5143\u7d20\u67e5\u8a62\uff0c\u5982\u4ee5 specialize in\uff0c\u5c07\u5206\u70ba # in \u548c specialize # \u7684\uf978 \u7a2e\u53ef\u80fd\u7684\u67e5\u8a62\u689d\u4ef6\uff0c\u518d\u53d6\uf978\u7a2e\uf967\u540c\u67e5\u8a62\u689d\u4ef6\u6240\u5f97\u6aa2\uf96a\u7d50\u679c\u7684\u5e73\u5747\u7b26\u5408\ufa01\u3002 \u5be6\u9a57\u8a2d\u5b9a SAW \u7cfb\u7d71\u6bcf\u6b21\u63d0\u4f9b 10 \u500b\uf9b5\uf906\uff0c\u4ea6\u5373\u9808\u63a8\u85a6\u51fa\u524d 10 \u500b\uf9b5\uf906\u4f9b\u4f7f\u7528\u8005\uf96b\u7167\u3002\u5716 \u4e94\u986f\u793a 10 \u500b\u7531\uf978\u500b\u82f1\u8a9e\u55ae\u5b57\u7d44\u6210\u7684\u7247\u8a9e\u7684\u8cc7\u8a0a\u9700\u6c42\uff0c\u5c0d\u65bc SAW \u7cfb\u7d71\u6240\u63d0\u4f9b\u7684\uf96b\u7167\u8cc7 \u8a0a\u4e4b\u7b26\u5408\ufa01\u8a55\u4f30\u3002\u4e00\u5982\u9810\u671f\u7684\uff0cexact \u67e5\u8a62\u689d\u4ef6\u7684\u7d50\u679c\u7b26\u5408\ufa01\u6700\u9ad8\uff0c\u70ba 100%\u3002\u53e6\u5916\uff0cprefix \u67e5\u8a62\u689d\u4ef6\u65bc\u6b64 10 \u500b\u7247\u8a9e\u4e2d\u7b26\u5408\ufa01\u9ad8\u65bc POS \u689d\u4ef6\uff1b\u800c POS \u67e5\u8a62\u689d\u4ef6\u7b26\u5408\ufa01\u9ad8\u65bc wildcard \u67e5\u8a62\u689d\u4ef6\uff0c\u5176\u4e2d\u6709\u4e00\u500b\uf9b5\u5916\u70ba native to \u7247\u8a9e\u3002\u9019\u4e9b\u7d50\u679c\u7d04\uf976\uf96f\u660e prefix \u8868\u9054\u5143\u7d20\u70ba\u9650\u5236 \u8f03\u5f37\u7684\u67e5\u8a62\u689d\u4ef6\uff0c\u800c POS \u6b21\u4e4b\uff0cwildcard \u6700\u4f4e\u3002 as\uff1beither * or\uff1bso * that \uff0c\u4ee5\u8a55\u4f30 subsequence \u8868\u9054\u5143\u7d20\u7684\u67e5\u8a62\u65b9\u5f0f\u4e4b\uf96b\u7167\u6210 \u6548\uff0c\u4e26\u523b\u610f\u5c0d\u65bc 12 \u500b\uf906\u578b\u7d50\u69cb\u4e2d\u63d0\u4f9b\u90e8\u5206\u932f\u8aa4\u7684\u67e5\u8a62\u689d\u4ef6\u3002\u8209\uf9b5\u800c\u8a00\uff0c\u5c0d\u65bc enable \u2026 to \u7684\u7247\u8a9e\uff0c\u523b\u610f\u4ee5 enable * and \u548c enable * but \u7684\u90e8\u5206\u932f\u8aa4\u67e5\u8a62\u689d\u4ef6\u6a21\u64ec\u5b78\u7fd2\u8005\u8a8d\u77e5\u4e0a\u7684 \u932f\u8aa4\uff0c\u4e26\u4ee5\u5e73\u5747\u7b26\u5408\ufa01\uf92d\u63cf\u8ff0\u7cfb\u7d71\u7684\uf96b\u7167\u5354\u52a9\u6210\u6548\u3002\u6b64 12 \u500b\u5b50\uf906\u4e2d\uff0c\u5747\u6709\u4e00\u500b\u91cd\u8981\u7684 \u95dc\u9375\u4ecb\u4fc2\u8a5e\uff0c\u4f9d\u5e8f\u70ba to, from, to, but, than, from, from, with, into, as, or, so\u3002\u672c\u5be6\u9a57\u5c07\u90e8\u5206 \u932f\u8aa4\u7684\u8a8d\u77e5\u5747\u4ee5 and \u548c but \uf92d\u53d6\u4ee3\u4e0a\u8ff0\u7684\u95dc\u9375\u5b57\uff0c\u552f\u4e00\uf9b5\u5916\u7684\u662f not only * but also \u4e2d\u7684 but\uff0c\u56e0 but \u662f\u6b63\u78ba\u7528\u6cd5\uff0c\u5be6\u9a57\u5c07 but \u53d6\u4ee3\u70ba or \u7576\u90e8\u5206\u932f\u8aa4\u8a8d\u77e5\u7684\u67e5\u8a62\u689d\u4ef6\u3002 SAW \u7cfb\u7d71\u4e2d\u5f97\u5230\u4e00\u5b9a\u7684\uf96b\u7167\u5354\u52a9\uff0c\u8b49\u5be6 SAW \u7cfb\u7d71\u5c0d\u65bc\u5beb\u4f5c\u6709\u5176\u4e00 \u5b9a\u7684\u5354\u52a9\u6210\u6548\u3002\u7136\u56e0\u672c\u554f\u5377\u65bd\u6e2c\u7684\u5c0d\u8c61\u666e\u904d\u5b57\u5f59\u7a0b\ufa01\uf967\u8d85\u904e 2000 \u5b57\uff0c\u800c BNC \u7684\uf9b5\uf906\u5c0d \u65bc\u53d7\u6e2c\u8005\u666e\u904d\u504f\u96e3\uff0c\u5bb9\uf9e0\u9020\u6210\u53d7\u6e2c\u8005\u7684\u63a8\u85a6\ufa01\u548c\u6eff\u610f\ufa01\u4e0b\uf904\uff0c\u800c\u6b64\u90e8\u5206\u53ef\u85c9\u7531\u63a1\u53d6\u8f03\u9069 \u5b9c\u7684\u8a9e\uf9be\u5eab\u8b93\u7d50\u679c\uf901\u5b8c\u5584\u3002\u6211\u5011\u53e6\u5916\u4e5f\u4ee5\u5b78\u751f\u5be6\u969b\u7684\u5beb\u4f5c\u932f\u8aa4\u9032\ufa08\u6e2c\u8a66\uff0c\u5982 I would rather going shopping than staying home. \u6b64\u5b78\u751f\u6240\u72af\u7684\u932f\u8aa4\u70ba\u5c0d\u65bc would rather \u2026 than \u7684\uf906\u578b\uf967\u592a\u719f\u6089\uff0c\u5176\u4e2d would rather \u5f8c\uf974\u63a5\u52d5\u8a5e\uff0c\u61c9\u4ee5\u539f\u5f62\u8868\u9054\uff0c\u4ea6\u5373\u6b63\u78ba\u7684\uf906\u578b\u61c9\u70ba I would rather go shopping than stay home. \u6211\u5011\u4f7f\u7528 SAW \u7cfb\u7d71\u4ee5 would rather V than V \u548c would rather * than * \u6a21 \u64ec\u5b78\u7fd2\u8005\u53ef\u80fd\u7684\u67e5\u8a62\u689d\u4ef6\uff0c\u800c\u7531\u7d50\u679c\u767c\u73fe\uff0c\u4f7f\u7528"
            }
        }
    }
}