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Predicting Lexical Answer Types in Open Domain QA

Predicting Lexical Answer Types in Open Domain QA

Alfio Massimiliano Gliozzo, Aditya Kalyanpur
Copyright: © 2012 |Volume: 8 |Issue: 3 |Pages: 15
ISSN: 1552-6283|EISSN: 1552-6291|EISBN13: 9781466614871|DOI: 10.4018/jswis.2012070104
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MLA

Gliozzo, Alfio Massimiliano, and Aditya Kalyanpur. "Predicting Lexical Answer Types in Open Domain QA." IJSWIS vol.8, no.3 2012: pp.74-88. http://doi.org/10.4018/jswis.2012070104

APA

Gliozzo, A. M. & Kalyanpur, A. (2012). Predicting Lexical Answer Types in Open Domain QA. International Journal on Semantic Web and Information Systems (IJSWIS), 8(3), 74-88. http://doi.org/10.4018/jswis.2012070104

Chicago

Gliozzo, Alfio Massimiliano, and Aditya Kalyanpur. "Predicting Lexical Answer Types in Open Domain QA," International Journal on Semantic Web and Information Systems (IJSWIS) 8, no.3: 74-88. http://doi.org/10.4018/jswis.2012070104

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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.

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