| { | |
| "paper_id": "O15-1008", | |
| "header": { | |
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| "date_generated": "2023-01-19T08:10:06.174577Z" | |
| }, | |
| "title": "A Study of Readability Prediction on Elementary and Secondary Chinese Textbooks and Excellent Extracurricular Reading Materials", | |
| "authors": [ | |
| { | |
| "first": "Yi-Nian", | |
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| "middle": [], | |
| "last": "Liu", | |
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| { | |
| "first": "Kuan-Yu", | |
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| { | |
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| "middle": [], | |
| "last": "Chen", | |
| "suffix": "", | |
| "affiliation": {}, | |
| "email": "kychen@iis.sinica.edu.tw" | |
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| { | |
| "first": "Ho-Chiang", | |
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| "middle": [], | |
| "last": "Tseng", | |
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| { | |
| "first": "Berlin", | |
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| "email": "berlin@ntnu.edu.tw" | |
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| "abstract": "Readability is basically concerned with readers' comprehension of given textual materials: the higher the readability of a document, the easier the document can be understood. It may be affected by various factors, such as document length, word difficulty, sentence structure and whether the content of a document meets the prior knowledge of a reader or not. However, simple surface linguistic features cannot always account for these factors in an appropriate manner. To cater for this, we explore in this study a variety of extra features, including syntactic analysis, parts of speech, word embedding, semantic role features and well-written features. The experimental datasets are composed of two parts: one is textbooks of the Chinese language for elementary and junior high schools (K1 to K9) in Taiwan, compiled from three publishers in the academic year of 2009; the other is excellent extracurricular reading materials for students of elementary and junior high schools, collected by the Ministry of Culture in Taiwan. Two readability prediction models, viz. stepwise regression and support vector machine, are evaluated and compared, while the combination of these two models is also investigated so as to further enhance the accuracy of readability prediction. Experimental results reveal that our proposed approach can yield consistently better performance than traditional ones merely with simple surface linguistic features in evaluating text difficulty.", | |
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| "text": "Readability is basically concerned with readers' comprehension of given textual materials: the higher the readability of a document, the easier the document can be understood. It may be affected by various factors, such as document length, word difficulty, sentence structure and whether the content of a document meets the prior knowledge of a reader or not. However, simple surface linguistic features cannot always account for these factors in an appropriate manner. To cater for this, we explore in this study a variety of extra features, including syntactic analysis, parts of speech, word embedding, semantic role features and well-written features. The experimental datasets are composed of two parts: one is textbooks of the Chinese language for elementary and junior high schools (K1 to K9) in Taiwan, compiled from three publishers in the academic year of 2009; the other is excellent extracurricular reading materials for students of elementary and junior high schools, collected by the Ministry of Culture in Taiwan. Two readability prediction models, viz. stepwise regression and support vector machine, are evaluated and compared, while the combination of these two models is also investigated so as to further enhance the accuracy of readability prediction. Experimental results reveal that our proposed approach can yield consistently better performance than traditional ones merely with simple surface linguistic features in evaluating text difficulty.", | |
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| "section": "Abstract", | |
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| "bib_entries": { | |
| "BIBREF2": { | |
| "ref_id": "b2", | |
| "title": "Coh-Metrix: Analysis of Text on Cohesion and Language", | |
| "authors": [ | |
| { | |
| "first": "A", | |
| "middle": [ | |
| "C" | |
| ], | |
| "last": "Graesser", | |
| "suffix": "" | |
| }, | |
| { | |
| "first": "D", | |
| "middle": [ | |
| "S" | |
| ], | |
| "last": "Mcnamara", | |
| "suffix": "" | |
| }, | |
| { | |
| "first": "M", | |
| "middle": [ | |
| "M" | |
| ], | |
| "last": "Louwerse", | |
| "suffix": "" | |
| }, | |
| { | |
| "first": "Z", | |
| "middle": [], | |
| "last": "Cai", | |
| "suffix": "" | |
| } | |
| ], | |
| "year": 2004, | |
| "venue": "Behavior Research Methods, Instruments, & Computers", | |
| "volume": "36", | |
| "issue": "2", | |
| "pages": "193--202", | |
| "other_ids": {}, | |
| "num": null, | |
| "urls": [], | |
| "raw_text": "A. C. Graesser, D. S. McNamara, M. M. Louwerse, and Z. Cai, \"Coh-Metrix: Analysis of Text on Cohesion and Language,\" Behavior Research Methods, Instruments, & Computers, vol. 36, no. 2, pp. 193-202, 2004.", | |
| "links": null | |
| }, | |
| "BIBREF3": { | |
| "ref_id": "b3", | |
| "title": "\u65b0\u805e\u5b78\u7814\u7a76, 8 \u5377", | |
| "authors": [ | |
| { | |
| "first": "\"", | |
| "middle": [], | |
| "last": "\u9673\u4e16\u654f", | |
| "suffix": "" | |
| }, | |
| { | |
| "first": "", | |
| "middle": [], | |
| "last": "\u4e2d\u6587\u53ef\u8b80\u6027\u516c\u5f0f\u8a66\u64ec", | |
| "suffix": "" | |
| } | |
| ], | |
| "year": 1971, | |
| "venue": "", | |
| "volume": "", | |
| "issue": "", | |
| "pages": "181--226", | |
| "other_ids": {}, | |
| "num": null, | |
| "urls": [], | |
| "raw_text": "\u9673\u4e16\u654f, \"\u4e2d\u6587\u53ef\u8b80\u6027\u516c\u5f0f\u8a66\u64ec\", \u65b0\u805e\u5b78\u7814\u7a76, 8 \u5377, 181-226, 1971.", | |
| "links": null | |
| }, | |
| "BIBREF4": { | |
| "ref_id": "b4", | |
| "title": "\u65b0\u805e\u5b78\u7814\u7a76, 8 \u5377", | |
| "authors": [ | |
| { | |
| "first": "\"", | |
| "middle": [], | |
| "last": "\u694a\u5b5d\u6fda", | |
| "suffix": "" | |
| }, | |
| { | |
| "first": "", | |
| "middle": [], | |
| "last": "\u4e2d\u6587\u53ef\u8b80\u6027\u516c\u5f0f", | |
| "suffix": "" | |
| } | |
| ], | |
| "year": 1971, | |
| "venue": "", | |
| "volume": "", | |
| "issue": "", | |
| "pages": "77--101", | |
| "other_ids": {}, | |
| "num": null, | |
| "urls": [], | |
| "raw_text": "\u694a\u5b5d\u6fda, \"\u4e2d\u6587\u53ef\u8b80\u6027\u516c\u5f0f\", \u65b0\u805e\u5b78\u7814\u7a76, 8 \u5377, 77-101, 1971.", | |
| "links": null | |
| }, | |
| "BIBREF5": { | |
| "ref_id": "b5", | |
| "title": "Computational Assessment of Text Readability: A Survey of Current and Future Research", | |
| "authors": [ | |
| { | |
| "first": "K", | |
| "middle": [], | |
| "last": "Collins-Thompson", | |
| "suffix": "" | |
| } | |
| ], | |
| "year": 2014, | |
| "venue": "Recent Advances in Automatic Readability Assessment and Text Simplification. Special issue of International Journal of Applied Linguistics", | |
| "volume": "165", | |
| "issue": "2", | |
| "pages": "97--135", | |
| "other_ids": {}, | |
| "num": null, | |
| "urls": [], | |
| "raw_text": "K. Collins-Thompson, \"Computational Assessment of Text Readability: A Survey of Current and Future Research,\" Recent Advances in Automatic Readability Assessment and Text Simplification. Special issue of International Journal of Applied Linguistics, vol. 165, no. 2, 97-135, 2014.", | |
| "links": null | |
| }, | |
| "BIBREF9": { | |
| "ref_id": "b9", | |
| "title": "\u8a08\u7b97\u6a5f\u5de5\u7a0b, 10 \u671f", | |
| "authors": [ | |
| { | |
| "first": "\"", | |
| "middle": [], | |
| "last": "\u7941\u4ea8\u5e74", | |
| "suffix": "" | |
| }, | |
| { | |
| "first": "", | |
| "middle": [], | |
| "last": "\u652f\u6301\u5411\u91cf\u6a5f\u53ca\u5176\u61c9\u7528\u7814\u7a76\u7d9c\u8ff0", | |
| "suffix": "" | |
| } | |
| ], | |
| "year": 2004, | |
| "venue": "", | |
| "volume": "", | |
| "issue": "", | |
| "pages": "6--9", | |
| "other_ids": {}, | |
| "num": null, | |
| "urls": [], | |
| "raw_text": "\u7941\u4ea8\u5e74, \"\u652f\u6301\u5411\u91cf\u6a5f\u53ca\u5176\u61c9\u7528\u7814\u7a76\u7d9c\u8ff0\", \u8a08\u7b97\u6a5f\u5de5\u7a0b, 10 \u671f, 6-9, 2004.", | |
| "links": null | |
| }, | |
| "BIBREF10": { | |
| "ref_id": "b10", | |
| "title": "LIBSVM -A Library for Support Vector Machines", | |
| "authors": [], | |
| "year": null, | |
| "venue": "", | |
| "volume": "", | |
| "issue": "", | |
| "pages": "", | |
| "other_ids": {}, | |
| "num": null, | |
| "urls": [], | |
| "raw_text": "\"LIBSVM -A Library for Support Vector Machines,\" available at: http://www.csie.ntu.edu.tw/~cjlin/libsvm/index.html?js=1#svm-toy-js.", | |
| "links": null | |
| }, | |
| "BIBREF11": { | |
| "ref_id": "b11", | |
| "title": "Coh-Metrix Web Tool", | |
| "authors": [], | |
| "year": null, | |
| "venue": "", | |
| "volume": "", | |
| "issue": "", | |
| "pages": "", | |
| "other_ids": {}, | |
| "num": null, | |
| "urls": [], | |
| "raw_text": "\"Coh-Metrix Web Tool,\" available at: http://tool.cohmetrix.com/.", | |
| "links": null | |
| }, | |
| "BIBREF12": { | |
| "ref_id": "b12", | |
| "title": "About WordNet -WordNet -About WordNet", | |
| "authors": [ | |
| { | |
| "first": "", | |
| "middle": [], | |
| "last": "\" \u4e2d \u6587 \u6587 \u672c \u81ea \u52d5 \u5316 \u5206 \u6790 \u7cfb \u7d71", | |
| "suffix": "" | |
| } | |
| ], | |
| "year": null, | |
| "venue": "", | |
| "volume": "", | |
| "issue": "", | |
| "pages": "", | |
| "other_ids": {}, | |
| "num": null, | |
| "urls": [], | |
| "raw_text": "\" \u4e2d \u6587 \u6587 \u672c \u81ea \u52d5 \u5316 \u5206 \u6790 \u7cfb \u7d71 \" , available at: http://210.240.188.161/Chinese_CohMetrix/index.html. [13] \"About WordNet -WordNet - About WordNet,\" available at: http://wordnet.princeton.edu/.", | |
| "links": null | |
| }, | |
| "BIBREF14": { | |
| "ref_id": "b14", | |
| "title": "A Comparison of Features for Automatic Readability Assessment", | |
| "authors": [ | |
| { | |
| "first": "L", | |
| "middle": [], | |
| "last": "Feng", | |
| "suffix": "" | |
| }, | |
| { | |
| "first": "M", | |
| "middle": [], | |
| "last": "Jansche", | |
| "suffix": "" | |
| }, | |
| { | |
| "first": "M", | |
| "middle": [], | |
| "last": "Huenerfauth", | |
| "suffix": "" | |
| }, | |
| { | |
| "first": "N", | |
| "middle": [], | |
| "last": "Elhadad", | |
| "suffix": "" | |
| } | |
| ], | |
| "year": 2010, | |
| "venue": "23rd International Conference on Computational Linguistics (COLING 2010), Poster Volume", | |
| "volume": "", | |
| "issue": "", | |
| "pages": "276--284", | |
| "other_ids": {}, | |
| "num": null, | |
| "urls": [], | |
| "raw_text": "L. Feng, M. Jansche, M. Huenerfauth, and N. Elhadad, \"A Comparison of Features for Automatic Readability Assessment,\" 23rd International Conference on Computational Linguistics (COLING 2010), Poster Volume, pp. 276-284, 2010.", | |
| "links": null | |
| }, | |
| "BIBREF18": { | |
| "ref_id": "b18", | |
| "title": "word2vec -Tool for computing continuous distributed representations of words. -Google Project Hosting", | |
| "authors": [], | |
| "year": null, | |
| "venue": "", | |
| "volume": "", | |
| "issue": "", | |
| "pages": "", | |
| "other_ids": {}, | |
| "num": null, | |
| "urls": [], | |
| "raw_text": "\"word2vec -Tool for computing continuous distributed representations of words. -Google Project Hosting,\" available at: https://code.google.com/p/word2vec/.", | |
| "links": null | |
| }, | |
| "BIBREF19": { | |
| "ref_id": "b19", | |
| "title": "Efficient Estimation of Word Representations in Vector Space", | |
| "authors": [ | |
| { | |
| "first": "T", | |
| "middle": [], | |
| "last": "Mikolov", | |
| "suffix": "" | |
| }, | |
| { | |
| "first": "K", | |
| "middle": [], | |
| "last": "Chen", | |
| "suffix": "" | |
| }, | |
| { | |
| "first": "G", | |
| "middle": [], | |
| "last": "Corrado", | |
| "suffix": "" | |
| }, | |
| { | |
| "first": "J", | |
| "middle": [], | |
| "last": "Dean", | |
| "suffix": "" | |
| } | |
| ], | |
| "year": 2013, | |
| "venue": "Proceedings of Workshop at ICLR", | |
| "volume": "", | |
| "issue": "", | |
| "pages": "", | |
| "other_ids": {}, | |
| "num": null, | |
| "urls": [], | |
| "raw_text": "T. Mikolov, K. Chen, G. Corrado, and J. Dean. \"Efficient Estimation of Word Representations in Vector Space,\" In Proceedings of Workshop at ICLR, 2013.", | |
| "links": null | |
| }, | |
| "BIBREF20": { | |
| "ref_id": "b20", | |
| "title": "What Makes Writing Great? First Experiments on Article Quality Prediction in the Science Journalism Domain", | |
| "authors": [ | |
| { | |
| "first": "A", | |
| "middle": [], | |
| "last": "Louis", | |
| "suffix": "" | |
| }, | |
| { | |
| "first": "A", | |
| "middle": [], | |
| "last": "Nenkova", | |
| "suffix": "" | |
| } | |
| ], | |
| "year": 2013, | |
| "venue": "Transactions of the Association for Computational Linguistics", | |
| "volume": "1", | |
| "issue": "", | |
| "pages": "341--352", | |
| "other_ids": {}, | |
| "num": null, | |
| "urls": [], | |
| "raw_text": "A. Louis and A. Nenkova, \"What Makes Writing Great? First Experiments on Article Quality Prediction in the Science Journalism Domain,\" Transactions of the Association for Computational Linguistics, 1, pp. 341-352, 2013.", | |
| "links": null | |
| }, | |
| "BIBREF21": { | |
| "ref_id": "b21", | |
| "title": "\u4e2d\u83ef\u5fc3\u7406\u5b78\u520a, 55 \u5377, 4 \u671f", | |
| "authors": [ | |
| { | |
| "first": "\"", | |
| "middle": [], | |
| "last": "\u5353\u6dd1\u73b2\u3001\u9673\u5b78\u5fd7\u3001\u912d\u662d\u660e", | |
| "suffix": "" | |
| }, | |
| { | |
| "first": "", | |
| "middle": [], | |
| "last": "\u53f0\u7063\u5730\u5340\u83ef\u4eba\u60c5\u7dd2\u8207\u76f8\u95dc\u5fc3\u7406\u751f\u7406\u8cc7\u6599\u5eab-\u4e2d \u6587\u60c5\u7dd2\u8a5e\u5e38\u6a21\u7814\u7a76", | |
| "suffix": "" | |
| } | |
| ], | |
| "year": 2013, | |
| "venue": "", | |
| "volume": "", | |
| "issue": "", | |
| "pages": "493--523", | |
| "other_ids": {}, | |
| "num": null, | |
| "urls": [], | |
| "raw_text": "\u5353\u6dd1\u73b2\u3001\u9673\u5b78\u5fd7\u3001\u912d\u662d\u660e, \"\u53f0\u7063\u5730\u5340\u83ef\u4eba\u60c5\u7dd2\u8207\u76f8\u95dc\u5fc3\u7406\u751f\u7406\u8cc7\u6599\u5eab-\u4e2d \u6587\u60c5\u7dd2\u8a5e\u5e38\u6a21\u7814\u7a76\", \u4e2d\u83ef\u5fc3\u7406\u5b78\u520a, 55 \u5377, 4 \u671f, 493-523, 2013.", | |
| "links": null | |
| }, | |
| "BIBREF22": { | |
| "ref_id": "b22", | |
| "title": "\u6587\u5316\u90e8\u4e2d\u5c0f\u5b78\u751f\u512a\u826f\u8ab2\u5916\u8b80\u7269\u63a8\u4ecb\u8a55\u9078\u6d3b\u52d5 -\u7b2c 37 \u6b21", | |
| "authors": [], | |
| "year": null, | |
| "venue": "", | |
| "volume": "", | |
| "issue": "", | |
| "pages": "", | |
| "other_ids": {}, | |
| "num": null, | |
| "urls": [], | |
| "raw_text": "\"\u6587\u5316\u90e8\u4e2d\u5c0f\u5b78\u751f\u512a\u826f\u8ab2\u5916\u8b80\u7269\u63a8\u4ecb\u8a55\u9078\u6d3b\u52d5 -\u7b2c 37 \u6b21\", available at: http://book.moc.gov.tw/book/.", | |
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| "content": "<table><tr><td colspan=\"3\">\u7531\u65bc\u4e2d\u82f1\u6587\u5b57\u5728\u8a9e\u8a00\u7279\u5fb5\u4e0a\u7684\u5dee\u7570\u6975\u5927\uff0c\u904e\u53bb\u897f\u65b9\u7814\u7a76\u8005\u5728\u53ef\u8b80\u6027\u7814\u7a76\u6240\u63a1 \u7528\u7684\u7279\u5fb5\uff0c\u662f\u5426\u9069\u5408\u4e2d\u6587\u53ef\u8b80\u6027\u8a55\u4f30\u6709\u5f85\u5546\u69b7[1]\u3002\u6709\u9451\u65bc\u53ef\u8b80\u6027\u7814\u7a76\u7684\u91cd\u8981\u6027\uff0c \u4ee5\u53ca\u53ef\u80fd\u767c\u5c55\u7684\u591a\u5143\u61c9\u7528\uff0c\u672c\u8ad6\u6587\u63d0\u51fa\u4f7f\u7528\u53e5\u6cd5\u5206\u6790(\u610f\u7fa9\uff0c\u6bd4\u5c0d\u5404\u985e\u7279\u5fb5\u8207\u53ef\u8b80\u6027\u9ad8\u4f4e\u7684\u95dc\u806f\u6027\uff0c\u4e26\u5c07\u7279\u5fb5\u5f7c\u6b64\u7d50\u5408\u4ee5\u63d0\u5347\u53ef\u8b80\u6027\u9810 (Legibility) \u3001\u6613\u95b1\u8b80\u6027(Ease of Reading) \u3001\u6613\u7406\u89e3\u6027(Ease of Understanding) \u7b49\u4efb\u4f55\u4e00\u7a2e\u95dc\u65bc\u6750\u6599\u7684\u7279\u5fb5\u3002\u53ef\u8b80\u6027\u7684\u6982\u5ff5\u4e2d\uff0c\u6613\u7406\u89e3\u6027\u662f\u5728\u95b1\u8b80\u9818\u57df\u4e2d\u6bd4\u8f03\u901a Grade Level = 20 -( \u97f3\u7bc0\u6578 \u55ae\u97f3\u7bc0\u7684\u8a5e\u6578 10 ) (\u4e09) \u3001\u53ef\u8b80\u6027\u6a21\u578b\u5206\u6790\u6bd4\u8f03 \u7528\u7684\u7528\u6cd5[1]\u3002 \u8a9e\u8a00\u5c08\u5bb6\u85c9\u7531\u4e0d\u65b7\u4fee\u6b63\u800c\u5f97\u51fa\u7684\u300c\u53ef\u8b80\u6027\u516c\u5f0f\u300d\u4f86\u8a08\u7b97\u53ef\u8b80\u6027\u7684\u5206\u6578\uff0c\u4e26\u5c07 FORCAST (Caylor et al., 1973) Reading Age = 25 -( \u55ae\u97f3\u7bc0\u7684\u8a5e\u6578 10 \u50b3\u7d71\u53ef\u8b80\u6027\u516c\u5f0f\u591a\u70ba\u7dda\u6027\u8ff4\u6b78\u6a21\u578b\uff0c\u7d0d\u5165\u4e0d\u540c\u7684\u7279\u5fb5\u70ba\u81ea\u8b8a\u9805\uff0c\u4f30\u7b97\u6587\u7ae0\u96e3\u5ea6\uff0c ) years \u2192 150 \u8a5e \u6216\u63d0\u4f9b\u516c\u5f0f\u4f30\u7b97\u6587\u672c\u9069\u5408\u95b1\u8b80\u7684\u5e74\u7d1a\u3002\u8ff4\u6b78\u5206\u6790(Regression Analysis)\u662f\u4e00\u7a2e \u9019\u4e9b\u516c\u5f0f\u5ee3\u6cdb\u61c9\u7528\u65bc\u5c0d\u6587\u672c\u8207\u8b80\u8005\u7fa4\u9ad4\u7684\u95b1\u8b80\u6c34\u6e96\u52a0\u4ee5\u5339\u914d\uff0c\u7136\u800c\u53ef\u8b80\u6027\u516c\u5f0f\u7121 \u6cd5\u6e96\u78ba\u53cd\u6620\u6587\u672c\u96e3\u5ea6\uff0c\u53ea\u662f\u7d66\u51fa\u4e00\u500b\u300c\u4e0d\u932f\u7684\u7c97\u7565\u4f30\u8a08\u300d[6]\u3002 Reading Age = 25 -( \u7d71\u8a08\u5b78\u4e0a\u5206\u6790\u6578\u64da\u7684\u65b9\u6cd5\uff0c\u76ee\u7684\u5728\u65bc\u4e86\u89e3\u5169\u500b\u6216\u591a\u500b\u8b8a\u6578\u9593\u662f\u5426\u76f8\u95dc\uff0c\u4e26\u5efa\u7acb\u6578 \u55ae\u97f3\u7bc0\u7684\u8a5e\u6578 6.67 ) years \u2192 100 \u8a5e \u5b78\u6a21\u578b\u4ee5\u4fbf\u89c0\u5bdf\u7279\u5b9a\u8b8a\u6578\u4f86\u9810\u6e2c\u7814\u7a76\u8005\u611f\u8208\u8da3\u7684\u8b8a\u6578[7]\u3002\u66f4\u660e\u78ba\u5730\uff0c\u8ff4\u6b78\u5206\u6790 \u6e2c\u4e4b\u6b63\u78ba\u6027\u3002\u85c9\u7531\u9019\u4e9b\u7279\u5fb5\uff0c\u672c\u8ad6\u6587\u900f\u904e\u9010\u6b65\u8ff4\u6b78\u8207\u652f\u6301\u5411\u91cf\u6a5f\u7b49\u5169\u7a2e\u65b9\u5f0f\u5efa\u7acb \u53ef\u8b80\u6027\u6a21\u578b\uff0c\u6bd4\u8f03\u5169\u8005\u7528\u65bc\u6e2c\u8a66\u570b\u4e2d\u5c0f\u6559\u79d1\u66f8\u53ca\u512a\u826f\u8ab2\u5916\u8b80\u7269\u4e4b\u6548\u80fd\u512a\u52a3\uff0c\u4e26\u671f \u671b\u627e\u51fa\u53ef\u8b80\u6027\u5206\u985e\u4e4b\u91cd\u8981\u56e0\u7d20\u3002 \u8207\u516c\u5f0f\u3001\u5206\u6790\u53ef\u8b80\u6027\u7684\u6a21\u578b\u3001\u63a2\u8a0e\u53ef\u8b80\u6027\u7684\u767c\u5c55\u8da8\u52e2\u3001\u4ecb\u7d39\u53ef\u8b80\u6027\u7684\u61c9\u7528\u5c64\u9762\u3002 \u7b2c\u4e09\u7bc0\u9664\u89e3\u91cb\u5148\u524d\u7814\u7a76\u7684\u7279\u5fb5\u5916\uff0c\u4ea6\u5206\u5225\u8ad6\u8ff0\u672c\u8ad6\u6587\u6240\u4f7f\u7528\u7684\u5404\u985e\u7279\u5fb5\u3002\u7b2c\u56db\u7bc0 \u7167\uff0c\u7be9\u9078\u51fa\u4e0d\u540c\u7b49\u7d1a\u96e3\u5ea6\u7684\u8a5e\u5f59\u7576\u6210\u6587\u7ae0\u96e3\u5ea6\u6307\u6a19\uff0c\u5c0d\u5f8c\u4f86\u7684\u53ef\u8b80\u6027\u7814\u7a76\u6709\u91cd\u5927 \u8868\u4e00\u3001\u897f\u65b9\u5e38\u898b\u7684\u53ef\u8b80\u6027\u516c\u5f0f\u8207\u63a1\u7528\u6307\u6a19 \u8868\u6dfa\u8a9e\u8a00\u7279\u5fb5\u3002\u7b2c\u4e00\u500b\u53ef\u8b80\u6027\u516c\u5f0f The Lively & Pressey Method \u5229\u7528\u8a5e\u8868\u7576\u6210\u53c3 \u70ba\u5be6\u9a57\u8cc7\u6599\u8207\u5be6\u9a57\u7d50\u679c\u7684\u5448\u73fe\u3002\u7b2c\u4e94\u7bc0\u70ba\u5168\u6587\u7e3d\u7d50\u8207\u672a\u4f86\u7814\u7a76\u5c55\u671b\u3002 \u7a76\u76f8\u5c0d\u800c\u8a00\u5247\u5c48\u6307\u53ef\u6578\uff0c\u65e9\u671f\u50c5\u904b\u7528\u8868\u6dfa\u6307\u6a19\uff0c\u767c\u5c55\u4e00\u7cfb\u5217\u4e2d\u6587\u9069\u8b80\u6027\u516c\u5f0f\uff0c\u8fd1 \u671f\u5247\u6709\u5c07\u5c0f\u5b78\u6559\u79d1\u66f8\u9032\u884c\u53ef\u8b80\u6027\u5206\u985e\u4e4b\u63a2\u8a0e[1]\u3002\u53ef\u8b80\u6027\u7814\u7a76\u6982\u7565\u767c\u5c55\u6b77\u53f2\u53ef\u53c3 \u7167\u5716\u4e00\u3002\u897f\u65b9\u53ef\u8b80\u6027\u7814\u7a76\u4ee5\u767c\u5c55\u6e2c\u91cf\u516c\u5f0f\u70ba\u5927\u5b97\uff0c\u7136\u800c\u4fb7\u9650\u65bc\u6280\u8853\u50c5\u7d0d\u5165\u6587\u672c\u7684 The New Dale-Chall \u53e5\u9577) + 3.6365 (Chall and Dale, 1995) Grade Level = (0.1579 \u00d7 \u96e3\u8a5e \u7e3d\u8a5e\u6578 \u96e3\u8a5e\u6bd4\u7387\u3001\u53e5\u9577 ) + (0.0496 \u00d7 \u5e73\u5747 \u672c\u8ad6\u6587\u7684\u5f8c\u7e8c\u5b89\u6392\u5982\u4e0b\uff1a\u7b2c\u4e8c\u7bc0\u8aaa\u660e\u53ef\u8b80\u6027\u7684\u57fa\u672c\u6982\u5ff5\u3001\u56de\u9867\u53ef\u8b80\u6027\u7684\u6b77\u53f2 (\u4e8c) \u3001\u53ef\u8b80\u6027\u4e4b\u6b77\u53f2\u8207\u516c\u5f0f \u897f\u65b9\u53ef\u8b80\u6027\u7814\u7a76\u884c\u4e4b\u6709\u5e74\uff0c\u65e9\u65bc 1950 \u5e74\u4ee3\u6642\u53ef\u8b80\u6027\u516c\u5f0f\u5df2\u767e\u5bb6\u722d\u9cf4\uff0c\u8fd1\u5e74 \u4f86\u66f4\u5617\u8a66\u63a2\u8a0e\u8207\u6587\u672c\u66f4\u76f8\u95dc\u7684\u51dd\u805a\u6027\u6307\u6a19\uff0c\u53ca\u5404\u6307\u6a19\u9593\u7684\u95dc\u4fc2\uff1b\u4e2d\u6587\u7684\u53ef\u8b80\u6027\u7814 Flesch Grade Level (Kincaid et al., 1975) \u662f\u5229\u7528\u4f9d\u8b8a\u6578 Y \u8207\u81ea\u8b8a\u6578 X \u4e4b\u9593\u7684\u95dc\u4fc2\u6240\u5efa\u7acb\u7684\u6a21\u578b\uff0c\u671f\u671b\u627e\u51fa\u4e00\u689d\u6700\u80fd\u5920\u4ee3 Grade Level = -15.59 + (0.39 \u00d7 \u5e73\u5747\u53e5\u9577) + (11.8 \u53e5\u9577\u3001\u97f3\u7bc0\u6578 \u8868\u6240\u6709\u89c0\u6e2c\u8cc7\u6599\u7684\u51fd\u6578(\u8ff4\u6b78\u4f30\u8a08\u5f0f)[7]\u3002\u800c\u591a\u5143\u8ff4\u6b78\u5373\u70ba\u63a2\u8a0e\u4e00\u500b\u4f9d\u8b8a\u6578\u548c \u00d7 \u5e73\u5747\u97f3\u7bc0\u6578) \u591a\u500b\u81ea\u8b8a\u6578\u9593\u7684\u95dc\u4fc2\uff0c\u5982\uff1aY</td></tr><tr><td colspan=\"3\">\u7684\u5f71\u97ff\u3002\u53e6\u5916\u4e5f\u6709\u4e0d\u5c11\u7684\u53ef\u8b80\u6027\u516c\u5f0f\u5c07\u8a5e\u9577\u8207\u53e5\u9577\u7576\u6210\u96e3\u5ea6\u6307\u6a19\uff0c\u7d0d\u5165\u53ef\u8b80\u6027\u516c \u4e8c\u3001 \u6587\u737b\u63a2\u8a0e \u5f0f\u4e4b\u8a08\u7b97\u3002\u7531\u8868\u4e00\u53ef\u4ee5\u770b\u51fa\u53ef\u8b80\u6027\u516c\u5f0f\u8457\u91cd\u65bc\u5229\u7528\u5982\u8a5e\u5f59\u8207\u53e5\u9577\u7b49\u6dfa\u986f\u7684\u8a9e\u8a00\u7279 \u4e2d\u6587\u53ef\u8b80\u6027\u7814\u7a76\u4ee5\u8ff4\u6b78\u5206\u6790\u6cd5\u767c\u5c55\u53ef\u8b80\u6027\u516c\u5f0f\uff0c\u5c07\u53ef\u8b80\u6027\u6307\u6a19\u9010\u4e00\u522a\u53bb\uff0c\u6700</td></tr><tr><td colspan=\"3\">\u5fb5\u4f5c\u70ba\u6307\u6a19\uff0c\u6709\u5b78\u8005\u56e0\u6b64\u8a8d\u70ba\u4ee5\u9019\u4e9b\u8a9e\u8a00\u7279\u5fb5\u9810\u6e2c\u6587\u672c\u53ef\u8b80\u6027\uff0c\u4e26\u6c92\u6709\u5f37\u800c\u6709\u529b \u5f8c\u53ea\u7559\u4e0b\u5c11\u6578\u5f71\u97ff\u6700\u5927\u7684\u6307\u6a19\u3002\u53e6\u5916\uff0c\u4ea6\u6709\u7814\u7a76\u4f7f\u7528\u652f\u63f4\u5411\u91cf\u6a5f\u5efa\u7f6e\u4e4b\u6a21\u578b\u4f86\u9810 (\u4e00) \u3001\u53ef\u8b80\u6027\u57fa\u672c\u6982\u5ff5\u4ecb\u7d39 \u7684\u8b49\u64da\u3002 \u4f30\u6587\u7ae0\u9069\u5408\u95b1\u8b80\u7684\u5e74\u7d1a(\u5b8b\u66dc\u5ef7\u7b49\u4eba\uff0c2013) \u3002\u7531\u8868\u4e8c\u5247\u53ef\u770b\u51fa\u7814\u7a76\u8005\u591a\u63a1\u7528\u8f03 \u53ef\u8b80\u6027\u662f\u6307\u95b1\u8b80\u6750\u6599\u80fd\u5920\u88ab\u8b80\u8005\u7406\u89e3\u7684\u7a0b\u5ea6(Dale & Chall, 1949; Klare, 1963, \u70ba\u8868\u6dfa\u4e4b\u6307\u6a19\u5efa\u7acb\u516c\u5f0f\u3002\u56e0\u6b64\uff0c\u50b3\u7d71\u4e2d\u6587\u53ef\u8b80\u6027\u7814\u7a76\uff0c\u5728\u6307\u6a19\u7684\u9078\u53d6\u4e0a\u8207\u62fc\u97f3\u6587 2000; McLaughlin, 1969 )\u3002 Klare ( 1984 ) \u8a8d \u70ba \u53ef \u8b80 \u6027 \u7684 \u5b9a \u7fa9 \u70ba \uff1a \u6613 \u8b58 \u5225 \u6027 \u5b57\u7cfb\u7d71\u5e38\u898b\u7684\u6307\u6a19\u4e26\u7121\u986f\u8457\u5dee\u7570\u3002</td></tr><tr><td>\u4e00\u3001 \u7dd2\u8ad6 \u516c\u5f0f\u540d\u7a31</td><td>\u8a08\u7b97\u516c\u5f0f</td><td>\u63a1\u7528\u6307\u6a19</td></tr><tr><td colspan=\"3\">\u53ef\u8b80\u6027(readability)\u662f\u6307\u95b1\u8b80\u6750\u6599\u80fd\u5920\u88ab\u8b80\u8005\u7406\u89e3\u7684\u7a0b\u5ea6[1]\u3002\u53ef\u8b80\u6027\u9ad8\u7684\u6587\u7ae0 Flesch Reading Ease (Flesch, 1948) Reading ease = 206.876 -(1.015 \u00d7 \u5e73\u5747\u53e5\u9577) -\u53e5\u9577\u3001\u97f3\u7bc0\u6578 \u516c\u5f0f\u540d\u7a31 \u8a08\u7b97\u5f0f \u63a1\u7528\u6307\u6a19 (84.6 \u00d7 \u5e73\u5747\u97f3\u7bc0\u6578) \u8f03\u5bb9\u6613\u88ab\u8b80\u8005\u7406\u89e3\u3002\u6587\u7ae0\u7684\u53ef\u8b80\u6027\u8207\u5f88\u591a\u56e0\u7d20\u6709\u95dc\uff0c\u5982\uff1a\u6587\u9577\u3001\u5b57\u8a5e\u96e3\u5ea6\u3001\u53e5\u6cd5 \u7d50\u69cb\u3001\u5167\u5bb9\u662f\u5426\u7b26\u5408\u8b80\u8005\u7684\u5148\u5099\u77e5\u8b58\u7b49\uff0c\u7136\u800c\u8868\u6dfa\u7684\u8a9e\u8a00\u7279\u5fb5\u4e26\u7121\u6cd5\u5b8c\u5168\u53cd\u6620\u9019 \u4e9b\u8907\u96dc\u7684\u6210\u5206\u3002\u82f1\u6587\u6587\u672c\u7684\u53ef\u8b80\u6027\u7814\u7a76\u884c\u4e4b\u6709\u5e74\uff0c\u6216\u4ee5\u8a5e\u5f59\u983b\u7387\u5217\u8868\uff0c\u8a55\u91cf\u6587\u7ae0 New Reading Ease (Flesch, 1951) -1.015 \u00d7 \u6bcf\u53e5\u5e73\u5747\u8a5e\u6578 -31.517 \u55ae\u97f3\u7bc0\u6578\u3001\u8a5e\u6578 Reading ease = 1.599 \u00d7 \u6bcf\u767e\u8a5e\u4e4b\u55ae\u97f3\u7bc0\u8a5e\u6bd4\u7387 Yang(1970)</td></tr><tr><td colspan=\"3\">\u96e3\u5ea6\u3001\u6216\u5c07\u8a5e\u8868\u4f5c\u70ba\u53c3\u7167\uff0c\u5efa\u7f6e\u53ef\u8b80\u6027\u516c\u5f0f\u3001\u6216\u767c\u5c55\u7dda\u4e0a\u591a\u6587\u672c\u7279\u5fb5\u5206\u6790\u5668[2]\uff0c \u8a08\u7b97\u5f71\u97ff\u6587\u7ae0\u96e3\u6613\u5ea6\u7684\u5404\u985e\u578b\u6307\u6a19\uff0c\u4e26\u63d0\u4f9b\u6578\u503c\u5316\u7684\u7d50\u679c\uff1b\u4e2d\u6587\u7684\u53ef\u8b80\u6027\u7814\u7a76\u5247 Gunning FOG (Gunning, 1952) Grade level = 0.4 \u00d7 (\u5e73\u5747\u53e5\u9577 + 100 \u00d7 \u96e3\u8a5e \u7e3d\u8a5e\u6578 ) \u53e5\u9577\u3001\u96e3\u8a5e\u6bd4\u7387</td></tr><tr><td colspan=\"3\">\u5c48\u6307\u53ef\u6578\uff0c\u6216\u9078\u7528\u8868\u6dfa\u7684\u8a9e\u8a00\u7279\u5fb5\u5efa\u7f6e\u53ef\u8b80\u6027\u516c\u5f0f[3, 4]\uff0c\u6216\u5c07\u53ef\u8b80\u6027\u6307\u6a19\u7b49\u7576 \u6210\u9810\u6e2c\u8b8a\u9805\uff0c\u4ee5\u6559\u79d1\u66f8\u7684\u5e74\u7d1a\u503c\u7576\u6210\u6548\u6a19\uff0c\u900f\u904e\u9010\u6b65\u8ff4\u6b78(Stepwise Regression) Spache (Spache, 1953) Grade level = 0.839 + (0.086 \u00d7 \u96e3\u8a5e\u767e\u5206\u6bd4) + \u53e5\u9577\u3001\u96e3\u8a5e\u6bd4\u7387 (0.141 \u00d7 \u5e73\u5747\u53e5\u9577) \u5efa\u7f6e\u516c\u5f0f\u3001\u6216\u7d50\u5408\u7279\u5fb5\u9078\u53d6\u65b9\u6cd5\u8207\u652f\u63f4\u5411\u91cf\u6a5f(Support Vector Machine, SVM) \u5efa\u7acb\u9810\u6e2c\u6a21\u578b\u9810\u6e2c\u6587\u672c\u7b49\u7d1a[1]\u3002\u53ef\u8b80\u6027\u7814\u7a76\u9664\u4e86\u50b3\u7d71\u7684\u8a9e\u8a00\u7279\u5fb5\uff0c\u5fc3\u7406\u5b78\u4e0a\u7684 \u56e0\u7d20\u4ea6\u662f\u503c\u5f97\u8003\u91cf\u4e4b\u56e0\u7d20[5]\u3002\u53ef\u8b80\u6027\u8f03\u9ad8\u7684\u6587\u7ae0\u9664\u4e86\u80fd\u8b93\u8b80\u8005\u8f03\u5bb9\u6613\u7406\u89e3\u5916\uff0c Powers-Summer-Kearl (Power et al., 1958) Grade Level = -2.2029 + 0.0778 \u00d7 \u5e73\u5747\u53e5\u9577 + \u8ab2\u6587\u9577\u5ea6\u3001\u53e5\u9577\u3001\u5e38\u7528 \u53e5\u9577\u3001\u97f3\u7bc0\u6578 \u5b57\u6bd4\u7387\u3001\u6587\u9ad4 0.455 \u00d7 \u97f3\u7bc0\u6578 Reading Age = -2.7971 + 0.0778 \u00d7 \u5e73\u5747\u53e5\u9577 + \u6587\u9ad4 \u4ea6\u61c9\u6709\u8f03\u9ad8\u7684\u8da3\u5473\u6027\uff0c\u589e\u5f37\u95b1\u8b80\u5370\u8c61\uff0c\u52a0\u5feb\u95b1\u8b80\u901f\u5ea6\uff0c\u4ee4\u8b80\u8005\u6709\u610f\u9858\u6301\u7e8c\u95b1\u8b80\uff0c 0.455 \u00d7 \u97f3\u7bc0\u6578 \u834a\u6eaa\u6631(1995) \u5e74\u7d1a = 8.76105604 + 0.00272438 \u00d7 \u8ab2\u6587\u9577\u5ea6 + \u9032\u800c\u9054\u6210\u5982\u8f14\u52a9\u6559\u5b78\u3001\u6587\u672c\u63a8\u85a6\u7b49\u7279\u5b9a\u76ee\u6a19\u3002\u6587\u672c\u53ef\u8b80\u6027\u9810\u6e2c\u53ef\u4f9d\u64da\u8b80\u8005\u63d0\u4f9b\u5408 \u9069\u7684\u6587\u672c\u95b1\u8b80\uff0c\u4ee5\u63d0\u9ad8\u5176\u7406\u89e3\u7a0b\u5ea6\uff0c\u9032\u800c\u57f9\u990a\u5f9e\u5c0f\u95b1\u8b80\u7684\u7fd2\u6163\u3002\u800c\u53ef\u8b80\u6027\u9810\u6e2c\u7684 \u7279\u5fb5\u4ecd\u6709\u8a31\u591a\u63a2\u8a0e\u7a7a\u9593\uff0c\u7d50\u5408\u4e0d\u540c\u6a21\u578b\u4ee5\u63d0\u9ad8\u9810\u6e2c\u6b63\u78ba\u6027\u4ea6\u70ba\u4e00\u7814\u7a76\u9762\u5411\u3002\u73fe\u4eca \u8cc7\u8a0a\u4f86\u6e90\u591a\u5143\uff0c\u975e\u50b3\u7d71\u6587\u5b57\u6587\u4ef6\uff0c\u5982\u5716\u7247\u3001\u97f3\u8a0a\u3001\u5f71\u7247\u7b49\uff0c\u7686\u53ef\u6210\u70ba\u63a5\u6536\u65b0\u77e5\u7684 \u7ba1\u9053\uff0c\u6545\u5176\u53ef\u8b80\u6027\u9810\u6e2c\u4ea6\u662f\u672a\u4f86\u7814\u7a76\u8da8\u52e2\u3002\u7136\u800c\u56e0\u591a\u5a92\u9ad4\u6587\u672c\u6240\u5305\u542b\u7684\u5167\u5bb9\u5f62\u5f0f \u8207\u7d14\u6587\u5b57\u6587\u672c\u4e4b\u7279\u6027\u5dee\u7570\u751a\u5927\uff0c\u5982\u4f55\u7d50\u5408\u65e2\u6709\u6982\u5ff5\u4ee5\u63a2\u8a0e\u65b0\u8208\u9818\u57df\u4e4b\u53ef\u8b80\u6027\uff0c\u6240 \u5716\u4e00\u3001\u53ef\u8b80\u6027\u7814\u7a76\u767c\u5c55\u6b77\u53f2 Fry Graph (Fry, 1968) 0.07866782 \u00d7 \u5e73\u5747\u53e5\u9577 -8.9311010 \u00d7 \u5e38\u7528\u5b57\u6bd4 \u8ab2\u6587\u9577\u5ea6\u3001\u53e5\u9577\u3001\u5e38\u7528 \u8a08\u7b97 3 \u7bc7 100 \u8a5e\u6587\u7ae0\u7684\u5e73\u5747\u53e5\u6578\u8207\u97f3\u7bc0\u6578\uff1b\u5c07\u6578 \u53e5\u6578\u3001\u97f3\u7bc0\u6578 \u7387 + 0.42920182 \u00d7 \u8a69\u6b4c\u9ad4 + 3.23677141 \u00d7 \u6587\u8a00 \u5b57\u6bd4\u7387\u3001\u6587\u9ad4 \u503c\u5728 Fry Graph \u4e2d\u505a\u8a18\u865f\u627e\u51fa\u95b1\u8b80\u5e74\u7d1a SMOG (McLaughlin, 1969) \u6587\u9ad4 SMOG Grade = 1.0430 \u00d7 \u221a\u4e09\u97f3\u7bc0\u4ee5\u4e0a\u7684\u8a5e\u6578 \u00d7 ( 30 2 ) + 3.1291 + 3.1291) \u5b8b\u66dc\u5ef7\u7b49\u4eba \u5e74\u7d1a = 4.53 + 0.01 \u00d7 \u96e3\u8a5e\u6578 -0.86 \u00d7 \u55ae\u53e5\u6578 \u96e3\u8a5e\u6578\u3001\u55ae\u53e5\u6578\u6bd4\u7387\u3001 \u591a\u97f3\u7bc0\u8a5e\u6578\u3001\u53e5\u6578 (2013) \u6bd4\u7387 -1.45 \u00d7 \u5be6\u8a5e\u983b\u5c0d\u6578\u5e73\u5747 + 0.02 \u00d7 \u4eba\u7a31 \u5be6\u8a5e\u983b\u5c0d\u6578\u5e73\u5747\u3001\u4eba\u7a31 \u9762\u81e8\u4e4b\u6311\u6230\u5c07\u66f4\u52a0\u8271\u56f0\u3002 \u4ee3\u540d\u8a5e\u6578 \u4ee3\u540d\u8a5e\u6578</td></tr><tr><td/><td>\u8868\u4e8c\u3001\u4e2d\u6587\u5e38\u898b\u7684\u53ef\u8b80\u6027\u516c\u5f0f\u8207\u63a1\u7528\u6307\u6a19</td><td/></tr></table>", | |
| "num": null | |
| } | |
| } | |
| } | |
| } |