Learning to rank answers on large online QA collections

Learning to rank answers on large online QA collections (2008)

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by Mihai Surdeanu , Massimiliano Ciaramita , Hugo Zaragoza

Venue:	In Proceedings of the 46th Annual Meeting for the Association for Computational Linguistics: Human Language Technologies (ACL-08: HLT
Citations:	20 - 3 self

BibTeX

@INPROCEEDINGS{Surdeanu08learningto,
    author = {Mihai Surdeanu and Massimiliano Ciaramita and Hugo Zaragoza},
    title = {Learning to rank answers on large online QA collections},
    booktitle = {In Proceedings of the 46th Annual Meeting for the Association for Computational Linguistics: Human Language Technologies (ACL-08: HLT},
    year = {2008},
    pages = {719--727}
}

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Abstract

This work describes an answer ranking engine for non-factoid questions built using a large online community-generated question-answer collection (Yahoo! Answers). We show how such collections may be used to effectively set up large supervised learning experiments. Furthermore we investigate a wide range of feature types, some exploiting NLP processors, and demonstrate that using them in combination leads to considerable improvements in accuracy. 1

Citations

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