Abstract
We present an end-to-end system for open-domain non-factoid question-answering. To accomplish this we leverage the information on the ever-growing World Wide Web, and the capabilities of commercial search engines to find the relevant information. Our QA system is composed of three components: (i) query formulation module (QFM) (ii) candidate answer generation module (CAGM) and (iii) answer selection module (ASM). A thorough empirical evaluation using two datasets demonstrates that the proposed approach is highly competitive.
This is a preview of subscription content, log in via an institution.
Buying options
Tax calculation will be finalised at checkout
Purchases are for personal use only
Learn about institutional subscriptionsNotes
- 1.
- 2.
- 3.
- 4.
- 5.
- 6.
- 7.
- 8.
- 9.
- 10.
References
Agichtein, E., et al.: Finding high-quality content in social media. In: Proceedings of WSDM (2008)
Agichtein, E., et al.: Overview of the TREC 2015 LiveQA track. In: Proceedings of TREC (2015)
Bian, J., et al.: Finding the right facts in the crowd: factoid question answering over social media. In: Proceedings of WWW (2008)
Bobrow, D.G.: A question-answering system for high school algebra word problems. In: Proceedings of FJCC (1964)
Burges, C.: From ranknet to lambdarank to lambdamart: an overview. Learning 11, 23–581 (2010)
Chen, D., Manning, C.: A fast and accurate dependency parser using neural networks. In: Proceedings of EMNLP (2014)
Chen, Q., Li, M., Zhou, M.: Improving query spelling correction using web search results. In: Proceedings of EMNLP-CoNLL (2007)
Green, C.: Theorem proving by resolution as a basis for question-answering systems. In: Machine Intelligence (1969)
Higashinaka, R., Isozaki, H.: Corpus-based question answering for why-questions. In: Proceedings of IJCNLP (2008)
Mikolov, T., et al.: Efficient estimation of word representations in vector space. In: Proceedings of ICLR (2013)
Oh, J.H., et al.: Why question answering using sentiment analysis and word classes. In: Proceedings of EMNLP-CoNLL (2012)
Soricut, R., Brill, E.: Automatic question answering using the web: beyond the factoid. Inf. Retrieval 9, 191–206 (2006)
Surdeanu, M., Ciaramita, M., Zaragoza, H.: Learning to rank answers to non-factoid questions from web collections. Comput. Linguist. 37, 351–383 (2011)
Suryanto, M.A., et al.: Quality-aware collaborative question answering: methods and evaluation. In: Proceedings of WSDM (2009)
Varanasi, S., Neumann, G.: Question/answer matching for Yahoo! Answers using a corpus-based extracted Ngram-based mapping. In: Proceedings of TREC (2015)
Waltz, D.L.: An English language question answering system for a large relational database. Commun. ACM 21, 526–539 (1978)
Wang, D., Nyberg, E.: CMU OAQA at TREC 2015 LiveQA: discovering the right answer with clues. In: Proceedings of TREC (2015)
Wang, D., Nyberg, E.: CMU OAQA at TREC 2016 LiveQA: an attentional neural encoder-decoder approach for answer ranking. In: Proceedings of TREC (2016)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2017 Springer International Publishing AG
About this paper
Cite this paper
Khvalchik, M., Pithyaachariyakul, C., Kulkarni, A. (2017). Answering the Hard Questions. In: Gracia, J., Bond, F., McCrae, J., Buitelaar, P., Chiarcos, C., Hellmann, S. (eds) Language, Data, and Knowledge. LDK 2017. Lecture Notes in Computer Science(), vol 10318. Springer, Cham. https://doi.org/10.1007/978-3-319-59888-8_22
Download citation
DOI: https://doi.org/10.1007/978-3-319-59888-8_22
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-59887-1
Online ISBN: 978-3-319-59888-8
eBook Packages: Computer ScienceComputer Science (R0)