More Web Proxy on the site http://driver.im/

research-article

Free access

Why question answering using sentiment analysis and word classes

Authors:

Kentaro Torisawa,

Chikara Hashimoto,

Stijn De Saeger,

Jun'ichi Kazama,

Yiou WangAuthors Info & Claims

EMNLP-CoNLL '12: Proceedings of the 2012 Joint Conference on Empirical Methods in Natural Language Processing and Computational Natural Language Learning

Pages 368 - 378

Published: 12 July 2012 Publication History

Abstract

In this paper we explore the utility of sentiment analysis and semantic word classes for improving why-question answering on a large-scale web corpus. Our work is motivated by the observation that a why-question and its answer often follow the pattern that if something undesirable happens, the reason is also often something undesirable, and if something desirable happens, the reason is also often something desirable. To the best of our knowledge, this is the first work that introduces sentiment analysis to non-factoid question answering. We combine this simple idea with semantic word classes for ranking answers to why-questions and show that on a set of 850 why-questions our method gains 15.2% improvement in precision at the top-1 answer over a baseline state-of-the-art QA system that achieved the best performance in a shared task of Japanese non-factoid QA in NTCIR-6.

References

[1]

Francis Bond, Hitoshi Isahara, Sanae Fujita, Kiyotaka Uchimoto, Takayuki Kuribayashi, and Kyoko Kanzaki. 2009. Enhancing the japanese wordnet. In Proceedings of the 7th Workshop on Asian Language Resources, pages 1--8.

Digital Library

[2]

Hoa Tran Dang. 2008. Overview of the TAC 2008 opinion question answering and summarization tasks. In Proc. TAC 2008.

[3]

Stijn De Saeger, Kentaro Torisawa, Jun'ichi Kazama, Kow Kuroda, and Masaki Murata. 2009. Large scale relation acquisition using class dependent patterns. In Proc. of ICDM 2009, pages 764--769.

Digital Library

[4]

David A. Ferrucci, Eric W. Brown, Jennifer Chu-Carroll, James Fan, David Gondek, Aditya Kalyanpur, Adam Lally, J. William Murdock, Eric Nyberg, John M. Prager, Nico Schlaefer, and Christopher A. Welty. 2010. Building Watson: An overview of the DeepQA project. AI Magazine, 31(3): 59--79.

Digital Library

[5]

Junichi Fukumoto, Tsuneaki Kato, Fumito Masui, and Tsunenori Mori. 2007. An overview of the 4th question answering challenge (QAC-4) at NTCIR workshop 6. In Proc. of NTCIR-6.

[6]

Zellig Harris. 1954. Distributional structure. Word, 10(23): 146--162.

[7]

Chikara Hashimoto, Kentaro Torisawa, Stijn De Saeger, Jong-Hoon Oh, and Jun'ichi Kazama. 2012. Excitatory or inhibitory: A new semantic orientation extracts contradiction and causality from the web. In Proceedings of EMNLP-CoNLL 2012.

Digital Library

[8]

Ryuichiro Higashinaka and Hideki Isozaki. 2008. Corpus-based question answering for why-questions. In Proc. of IJCNLP, pages 418--425.

[9]

Thomas Hofmann. 1999. Probabilistic latent semantic indexing. In Proc. of the 22nd annual international ACM SIGIR conference on Research and development in information retrieval, SIGIR '99, pages 50--57.

Digital Library

[10]

Jun'ichi Kazama and Kentaro Torisawa. 2008. Inducing gazetteers for named entity recognition by large-scale clustering of dependency relations. In Proc. of ACL-08: HLT, pages 407--415.

[11]

Fangtao Li, Yang Tang, Minlie Huang, and Xiaoyan Zhu. 2009. Answering opinion questions with random walks on graphs. In Proc. of the Joint Conference of the 47th Annual Meeting of the ACL and the 4th International Joint Conference on Natural Language Processing of the AFNLP: Volume 2 - Volume 2, pages 737--745.

Digital Library

[12]

Masaki Murata, Sachiyo Tsukawaki, Toshiyuki Kanamaru, Qing Ma, and Hitoshi Isahara. 2007. A system for answering non-factoid Japanese questions by using passage retrieval weighted based on type of answer. In Proc. of NTCIR-6.

[13]

Tetsuji Nakagawa, Kentaro Inui, and Sadao Kurohashi. 2010. Dependency tree-based sentiment classification using CRFs with hidden variables. In Human Language Technologies: The 2010 Annual Conference of the North American Chapter of the Association for Computational Linguistics, pages 786--794, Los Angeles, California, June. Association for Computational Linguistics.

Digital Library

[14]

Bo Pang, Lillian Lee, and Shivakumar Vaithyanathan. 2002. Thumbs up? Sentiment classification using machine learning techniques. In Proc. of the 2002 Conference on Empirical Methods in Natural Language Processing (EMNLP), pages 79--86.

Digital Library

[15]

Ted Pedersen, Siddharth Patwardhan, and Jason Michelizzi. 2004. WordNet::Similarity: measuring the relatedness of concepts. In Demonstration Papers at HLT-NAACL 2004, HLT-NAACL--Demonstrations '04, pages 38--41.

Digital Library

[16]

Anselmo Peñas, Eduard H. Hovy, Pamela Forner, Álvaro Rodrigo, Richard F. E. Sutcliffe, Corina Forascu, and Caroline Sporleder. 2011. Overview of QA4MRE at CLEF 2011: Question answering for machine reading evaluation. In CLEF.

[17]

Veselin Stoyanov, Claire Cardie, and Janyce Wiebe. 2005. Multi-perspective question answering using the opqa corpus. In Proceedings of the conference on Human Language Technology and Empirical Methods in Natural Language Processing, HLT '05, pages 923--930.

Digital Library

[18]

Mihai Surdeanu, Massimiliano Ciaramita, and Hugo Zaragoza. 2011. Learning to rank answers to non-factoid questions from web collections. Computational Linguistics, 37(2): 351--383.

Digital Library

[19]

Peter D. Turney. 2002. Thumbs up or thumbs down?: semantic orientation applied to unsupervised classification of reviews. In Proc. of the 40th Annual Meeting on Association for Computational Linguistics, ACL '02, pages 417--424.

Digital Library

[20]

Vladimir N. Vapnik. 1995. The nature of statistical learning theory. Springer-Verlag New York, Inc., New York, NY, USA.

Digital Library

[21]

Suzan Verberne, Lou Boves, Nelleke Oostdijk, and Peter-Arno Coppen. 2007. Evaluating discourse-based answer extraction for why-question answering. In SIGIR, pages 735--736.

Digital Library

[22]

Suzan Verberne, Lou Boves, Nelleke Oostdijk, and Peter-Arno Coppen. 2010. What is not in the bag of words for why-QA? Computational Linguistics, 36: 229--245.

Digital Library

[23]

Ellen M. Voorhees. 2004. Overview of the TREC 2004 question answering track. In TREC.

Cited By

Guy IMakarenkov VHazon NRokach LShapira BChang YZhai CLiu YMaarek Y(2018)Identifying Informational vs. Conversational Questions on Community Question Answering ArchivesProceedings of the Eleventh ACM International Conference on Web Search and Data Mining10.1145/3159652.3159733(216-224)Online publication date: 2-Feb-2018
https://dl.acm.org/doi/10.1145/3159652.3159733
Oh JTorisawa KHashimoto CIida RTanaka MKloetzer J(2016)A semi-supervised learning approach to why-question answeringProceedings of the Thirtieth AAAI Conference on Artificial Intelligence10.5555/3016100.3016325(3022-3029)Online publication date: 12-Feb-2016
https://dl.acm.org/doi/10.5555/3016100.3016325
Zadeh AZhang ZCohen PBohus DHoraud RMeng H(2015)Micro-opinion Sentiment Intensity Analysis and Summarization in Online VideosProceedings of the 2015 ACM on International Conference on Multimodal Interaction10.1145/2818346.2823317(587-591)Online publication date: 9-Nov-2015
https://dl.acm.org/doi/10.1145/2818346.2823317
Show More Cited By

Recommendations

Summarizing Answers in Non-Factoid Community Question-Answering
WSDM '17: Proceedings of the Tenth ACM International Conference on Web Search and Data Mining

We aim at summarizing answers in community question-answering (CQA). While most previous work focuses on factoid question-answering, we focus on the non-factoid question-answering. Unlike factoid CQA, non-factoid question-answering usually requires ...
A Yes/No Answer Generator Based on Sentiment-Word Scores in Biomedical Question Answering

Background and Objective: Yes/no question answering QA in open-domain is a longstanding challenge widely studied over the last decades. However, it still requires further efforts in the biomedical domain. Yes/no QA aims at answering yes/no questions, ...
Quality-aware collaborative question answering: methods and evaluation
WSDM '09: Proceedings of the Second ACM International Conference on Web Search and Data Mining

Community Question Answering (QA) portals contain questions and answers contributed by hundreds of millions of users. These databases of questions and answers are of great value if they can be used directly to answer questions from any user. In this ...

Comments

Please enable JavaScript to view thecomments powered by Disqus.

Information & Contributors

Information

Published In

cover image DL Hosted proceedings

EMNLP-CoNLL '12: Proceedings of the 2012 Joint Conference on Empirical Methods in Natural Language Processing and Computational Natural Language Learning

July 2012

1573 pages

General Chair:
Jun'ichi Tsujii
Microsoft Research Asia
,
Program Chairs:
James Henderson
Xerox Research Centre Europe
,
Marius Pasca
Google

Publisher

Association for Computational Linguistics

United States

Publication History

Published: 12 July 2012

Qualifiers

Research-article

Acceptance Rates

Overall Acceptance Rate 73 of 234 submissions, 31%

Contributors

Other Metrics

View Article Metrics

Bibliometrics & Citations

Bibliometrics

Article Metrics

5
Total Citations
View Citations
385
Total Downloads

Downloads (Last 12 months)37
Downloads (Last 6 weeks)7

Reflects downloads up to 12 Dec 2024

Other Metrics

View Author Metrics

Citations

Cited By

Guy IMakarenkov VHazon NRokach LShapira BChang YZhai CLiu YMaarek Y(2018)Identifying Informational vs. Conversational Questions on Community Question Answering ArchivesProceedings of the Eleventh ACM International Conference on Web Search and Data Mining10.1145/3159652.3159733(216-224)Online publication date: 2-Feb-2018
https://dl.acm.org/doi/10.1145/3159652.3159733
Oh JTorisawa KHashimoto CIida RTanaka MKloetzer J(2016)A semi-supervised learning approach to why-question answeringProceedings of the Thirtieth AAAI Conference on Artificial Intelligence10.5555/3016100.3016325(3022-3029)Online publication date: 12-Feb-2016
https://dl.acm.org/doi/10.5555/3016100.3016325
Zadeh AZhang ZCohen PBohus DHoraud RMeng H(2015)Micro-opinion Sentiment Intensity Analysis and Summarization in Online VideosProceedings of the 2015 ACM on International Conference on Multimodal Interaction10.1145/2818346.2823317(587-591)Online publication date: 9-Nov-2015
https://dl.acm.org/doi/10.1145/2818346.2823317
Chaturvedi SCastelli VFlorian RNallapati RRaghavan HChung CBroder AShim KSuel T(2014)Joint question clustering and relevance prediction for open domain non-factoid question answeringProceedings of the 23rd international conference on World wide web10.1145/2566486.2567999(503-514)Online publication date: 7-Apr-2014
https://dl.acm.org/doi/10.1145/2566486.2567999
Hashimoto CTorisawa KDe Saeger SOh JKazama JTsujii JHenderson JPasca M(2012)Excitatory or inhibitoryProceedings of the 2012 Joint Conference on Empirical Methods in Natural Language Processing and Computational Natural Language Learning10.5555/2390948.2391018(619-630)Online publication date: 12-Jul-2012
https://dl.acm.org/doi/10.5555/2390948.2391018

View Options

View options

PDF

View or Download as a PDF file.

eReader

View online with eReader.

Login options

Check if you have access through your login credentials or your institution to get full access on this article.

Full Access

Get this Publication

Media

Figures

Other

Tables

View Table of Contents