Predicting Lexical Answer Types in Open Domain QA

Alfio Massimiliano Gliozzo and Aditya Kalyanpur
Additional contact information
Alfio Massimiliano Gliozzo: IBM T.J. Watson Research Center, USA
Aditya Kalyanpur: IBM T.J. Watson Research Center, USA

International Journal on Semantic Web and Information Systems (IJSWIS), 2012, vol. 8, issue 3, 74-88

Abstract: Automatic open-domain Question Answering has been a long standing research challenge in the AI community. IBM Research undertook this challenge with the design of the DeepQA architecture and the implementation of Watson. This paper addresses a specific subtask of Deep QA, consisting of predicting the Lexical Answer Type (LAT) of a question. Our approach is completely unsupervised and is based on PRISMATIC, a large-scale lexical knowledge base automatically extracted from a Web corpus. Experiments on the Jeopardy! data shows that it is possible to correctly predict the LAT in a substantial number of questions. This approach can be used for general purpose knowledge acquisition tasks such as frame induction from text.

Date: 2012
References: Add references at CitEc
Citations: View citations in EconPapers (1)

Downloads: (external link)
http://services.igi-global.com/resolvedoi/resolve. ... 018/jswis.2012070104 (application/pdf)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:igg:jswis0:v:8:y:2012:i:3:p:74-88

Access Statistics for this article

International Journal on Semantic Web and Information Systems (IJSWIS) is currently edited by Brij Gupta

More articles in International Journal on Semantic Web and Information Systems (IJSWIS) from IGI Global
Bibliographic data for series maintained by Journal Editor ().