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| 多特征文本蕴涵识别研究 | |
| 引用本文： | 赵红燕,刘 鹏,李 茹,王智强.多特征文本蕴涵识别研究[J].中文信息学报,2014,28(2):109-115. |
| 作者姓名： | 赵红燕 刘 鹏 李 茹 王智强 |
| 作者单位： | 1. 太原科技大学 计算机科学与技术学院,山西 太原 030024; 2. 中国电子科技集团公司 第二研究所,山西 太原 030024; 3. 山西大学 计算机与信息技术学院,山西 太原 030006; 4. 山西大学 计算智能与中文信息处理教育部重点实验室,山西 太原 030006 |
| 基金项目： | 国家语委“十二五”科研规划项目(YB125-19);国家自然科学基金(61373082,60970053);山西省回国留学人员科研资助项目(2013-015);国家“863”高技术研究发展计划基金(2006AA0lZ142) |
| 摘 要： | 文本蕴涵识别是解决自然语言中存在的同义异形问题的有效途径。虽然国内外学者已经提出了很多文本蕴涵识别模型,但影响文本蕴涵识别的因素错综复杂,识别准确率普遍不高。该文把文本蕴涵识别看作二元分类问题,抽取词汇特征、句法依存关系特征及FrameNet语义知识库特征的多种特征构造特征矩阵,训练SVM分类器,实现文本蕴涵识别。该方法在国际文本蕴涵识别技术评测RTE3的测试集上进行测试,蕴涵正例识别准确率达到了78.1%,高于RTE3评测2-ways的最高结果。 |
| 关 键 词： | 文本蕴含识别 句法依存关系 FrameNet |
Recognizing Textual Entailment Based on the Multi-features |
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| ZHAO Hongyan,LIU Peng,LI Ru,WANG Zhiqiang.Recognizing Textual Entailment Based on the Multi-features[J].Journal of Chinese Information Processing,2014,28(2):109-115. | |
| Authors: | ZHAO Hongyan LIU Peng LI Ru WANG Zhiqiang |
| Affiliation: | 1. School of Computer Science &Technology,TaiYuan University of Science and Technology,Taiyuan, Shanxi 030024, China; 2. The Second Research Institute of China Electronics Technology Group Corporation,Taiyuan, Shanxi 030024, China; 3. School of Computer & Information Technology,Shanxi University, Taiyuan, Shanxi 030006,China; 4. Key Laboratory of Ministry of Education for Computation Intelligence & Chinese Information Processing,Taiyuan,Shanxi 030006,China |
| Abstract: | Recognizing text entailment is an effective solution to the natural language stating the same meaning in various ways. Although many text entailment recognition models have been proposed,the recognition accuracy rate is not satisfactory due to the complex factors in the text entailment.Treating the text entailment as a binary classification problem, this paper extracts multiple features of lexical, syntactic dependencies and FrameNet semantic knowledge to train a SVM classifiers for the text entailment recognition. Evaluated by the international RTE3 test set,this method achieves 78.1% precisionin in positive entailments,which is higher than the best result of RTE3. |
| Keywords: | recognize textual entailment syntactic dependent relationship FrameNet |
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