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Predicting Lexical Answer Types in Open Domain QA

Author

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  • Alfio Massimiliano Gliozzo

    (IBM T.J. Watson Research Center, USA)

  • Aditya Kalyanpur

    (IBM T.J. Watson Research Center, USA)

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.

Suggested Citation

  • Alfio Massimiliano Gliozzo & Aditya Kalyanpur, 2012. "Predicting Lexical Answer Types in Open Domain QA," International Journal on Semantic Web and Information Systems (IJSWIS), IGI Global, vol. 8(3), pages 74-88, July.
    • Handle: RePEc:igg:jswis0:v:8:y:2012:i:3:p:74-88
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    Cited by:

    1. Essam H. Houssein & Nahed Ibrahem & Alaa M. Zaki & Awny Sayed, 2022. "Semantic Protocol and Resource Description Framework Query Language: A Comprehensive Review," Mathematics, MDPI, vol. 10(17), pages 1-30, September.

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