A supervised learning approach to search of definitions
文献类型：期刊论文
| 作者 | Xu, J; Cao, YB; Li, H ; Zhao, M; Huang, YL
|
| 刊名 | JOURNAL OF COMPUTER SCIENCE AND TECHNOLOGY
 |
| 出版日期 | 2006-05-01 |
| 卷号 | 21期号:3页码:439-449 |
| 关键词 | definition search text mining web mining web search |
| 英文摘要 | This paper addresses the issue of search of definitions. Specifically, for a given term, we are to find out its definition candidates and rank the candidates according to their likelihood of being good definitions. This is in contrast to the traditional methods of either generating a single combined definition or outputting all retrieved definitions. Definition ranking is essential for tasks. A specification for judging the goodness of a definition is given. In the specification, a definition is categorized into one of the three levels: good definition, indifferent definition, or bad definition. Methods of performing definition ranking are also proposed in this paper, which formalize the problem as either classification or ordinal regression. We employ SVM (Support Vector Machines) as the classification model and Ranking SVM as the ordinal regression model respectively, and thus they rank definition candidates according to their likelihood of being good definitions. Features for constructing the SVM and Ranking SVM models are defined, which represent the characteristics of terms, definition candidate, and their relationship. Experimental results indicate that the use of SVM and Ranking SVM can significantly outperform the baseline methods such as heuristic rules, the conventional information retrieval-Okapi, or SVM regression. This is true when both the answers are paragraphs and they are sentences. Experimental results also show that SVM or Ranking SVM models trained in one domain can be adapted to another domain, indicating that generic models for definition ranking can be constructed. |
| WOS标题词 | Science & Technology ; Technology |
| 类目[WOS] | Computer Science, Hardware & Architecture ; Computer Science, Software Engineering |
| 研究领域[WOS] | Computer Science |
| 关键词[WOS] | WEB |
| 收录类别 | SCI |
| 语种 | 英语 |
| WOS记录号 | WOS:000238079200019 |
| 公开日期 | 2015-12-24 |
| 源URL | [http://ir.ia.ac.cn/handle/173211/9218]  |
| 专题 | 自动化研究所_09年以前成果 |
| 作者单位 | 1.Nankai Univ, Coll Software, Tianjin 300071, Peoples R China 2.Microsoft Res Asia, Beijing 100080, Peoples R China 3.Chinese Acad Sci, Inst Automat, Beijing 100080, Peoples R China |
| 推荐引用方式 GB/T 7714 | Xu, J,Cao, YB,Li, H,et al. A supervised learning approach to search of definitions[J]. JOURNAL OF COMPUTER SCIENCE AND TECHNOLOGY,2006,21(3):439-449. |
| APA | Xu, J,Cao, YB,Li, H,Zhao, M,&Huang, YL.(2006).A supervised learning approach to search of definitions.JOURNAL OF COMPUTER SCIENCE AND TECHNOLOGY,21(3),439-449. |
| MLA | Xu, J,et al."A supervised learning approach to search of definitions".JOURNAL OF COMPUTER SCIENCE AND TECHNOLOGY 21.3(2006):439-449. |
入库方式： OAI收割
来源：自动化研究所
浏览0
下载0
收藏0
其他版本
除非特别说明，本系统中所有内容都受版权保护，并保留所有权利。