| 融合C4.5与SVM算法的汉语句义类型识别方法 |
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| 引用本文： | 罗森林,王倩,刘莉莉,韩磊.融合C4.5与SVM算法的汉语句义类型识别方法[J].北京理工大学学报,2012,32(10):1036-1041. |
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| 作者姓名： | 罗森林 王倩 刘莉莉 韩磊 |
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| 作者单位： | 北京理工大学信息与电子学院,北京,100081;北京理工大学信息与电子学院,北京,100081;北京理工大学信息与电子学院,北京,100081;北京理工大学信息与电子学院,北京,100081 |
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| 基金项目： | 国家"二四二"计划项目(2005C48);北京理工大学基础研究基金资助项目(20060142014);北京理工大学研究生创新资助项目(GC200802);北京理工大学科技创新计划重大项目培育专项资助项目(2011CX01015) |
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| 摘 要： | 选择50个词法和句法特征,进行了大量特征筛选实验,并基于筛选后的特征组合提出了一种融合C4.5和SVM的句义类型识别方法.该方法充分利用C4.5对多重句义的高精度识别和SVM对简单句义、复杂句义的高精度识别的特点,将C4.5与SVM分别识别的结果进行融合处理.给出最终的句义类型识别结果.识别结果表明,在BFS-CTC汉语标注语料库中,选取了4 500个句子,经十折交叉验证,句义类型的识别准确率达到92.1%.
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| 关 键 词： | 自然语言处理 语义分析 句义结构 句义类型识别 |
| 收稿时间： | 2011/11/14 0:00:00 |
Chinese Sentential Semantic Type Recognition Based on C4.5 Decision Tree and SVM Algorithm |
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| LUO Sen-lin,WANG Qian,LIU Li-li and HAN Lei.Chinese Sentential Semantic Type Recognition Based on C4.5 Decision Tree and SVM Algorithm[J].Journal of Beijing Institute of Technology(Natural Science Edition),2012,32(10):1036-1041. |
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| Authors: | LUO Sen-lin WANG Qian LIU Li-li and HAN Lei |
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| Institution: | School of Information & Electronics, Beijing Institute of Technology, Beijing 10081, China;School of Information & Electronics, Beijing Institute of Technology, Beijing 10081, China;School of Information & Electronics, Beijing Institute of Technology, Beijing 10081, China;School of Information & Electronics, Beijing Institute of Technology, Beijing 10081, China |
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| Abstract: | 50 lexical and syntax features were chosen to implement selecting experiments of two-feature combinations. Based on those feature combinations, a Chinese sentential semantic recognition method combining C4.5 (decision tree) and SVM was proposed. The method makes full use of the features of high precision of multiple by C4.5 as well as high precision of single and complex by SVM. The final recognition results are determined by synthetic blend of recognition results from C4.5 and SVM, respectively. The experimental data contains 4 500 sentences chosen from Beijing Forest Studio-Chinese Tag Corpus (BFS-CTC). Through ten-fold cross verification, it is concluded that the accuracy rate of proposed method for recognizing sentential semantic type is 92.1%. |
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| Keywords: | natural language processing semantic parsing sentential semantic structure sentential semantic type recognition(SSTR) |
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