Conference proceeding
AskDragon: a redundancy-based factoid question answering system with lightweight local context analysis
Proceedings of the 9th ACM/IEEE-CS joint conference on digital libraries, pp 483-484
15 Jun 2009
Abstract
We introduce our QA system AskDragon which employs a novel lightweight local context analysis technique to handling two broad classes of factoid questions, entity and numeric questions. The local context analysis module dramatically improves the efficiency of QA systems without sacrificing high accuracy performance.
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Details
- Title
- AskDragon
- Creators
- Xiaohua Zhou - Drexel UniversityPalakorn Achananuparp - Drexel UniversityE Park - University of Missouri–Kansas CityXiaohua Hu - Drexel UniversityXiaodan Zhang - Drexel University
- Publication Details
- Proceedings of the 9th ACM/IEEE-CS joint conference on digital libraries, pp 483-484
- Series
- JCDL '09
- Publisher
- Association for Computing Machinery (ACM)
- Resource Type
- Conference proceeding
- Language
- English
- Academic Unit
- Information Science
- Scopus ID
- 2-s2.0-70450230366
- Other Identifier
- 991019173585504721