| 基于半监督学习的查询扩展模型 |
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| 引用本文： | 苏俊杰,陈俊. 基于半监督学习的查询扩展模型[J]. 计算机系统应用, 2012, 21(3): 181-184 |
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| 作者姓名： | 苏俊杰 陈俊 |
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| 作者单位： | 装备指挥技术学院,北京,101416 |
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| 摘 要： | 查询扩展是针对信息检索中常见的"词不匹配"问题提出的一种优化方法。通过分析现有查询扩展方法的不足,提出一种基于半监督学习的查询扩展模型,该模型将查询扩展看作一个分类问题,并采用直推式支持向量机对样本进行训练。实验结果表明该方法进一步提高了搜索引擎的查全率和查准率。
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| 关 键 词： | 信息检索 查询扩展 直推式支持向量机 半监督学习 SVM |
| 收稿时间： | 2011-07-01 |
| 修稿时间： | 2011-09-01 |
Query Expansion Model Based on Semi-Supervised Learning |
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| SU Jun-Jie and CHEN Jun. Query Expansion Model Based on Semi-Supervised Learning[J]. Computer Systems& Applications, 2012, 21(3): 181-184 |
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| Authors: | SU Jun-Jie and CHEN Jun |
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| Affiliation: | (Institute of Command and Technology of Equipment, Beijing 101416, China) |
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| Abstract: | Query expansion is a optimization method for ＂word mismatch＂ issues in information retrieval domain. By analyzing the shortcomings of existing methods, query expansion model based on semi-supervised learning is proposed, the model seems query expansion as a classification problem, and using transductvie support vector machine to train the samples. Experiments show that the recall and precision rates of search engine are further improved by this method. |
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| Keywords: | information retrieve query expansion transductive support vector machines semi-supervised learning SVM |
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