skip to main content
10.1145/1148170.1148211acmconferencesArticle/Chapter ViewAbstractPublication PagesirConference Proceedingsconference-collections
Article

Answering complex questions with random walk models

Published: 06 August 2006 Publication History

Abstract

We present a novel framework for answering complex questions that relies on question decomposition. Complex questions are decomposed by a procedure that operates on a Markov chain, by following a random walk on a bipartite graph of relations established between concepts related to the topic of a complex question and subquestions derived from topic-relevant passages that manifest these relations. Decomposed questions discovered during this random walk are then submitted to a state-of-the-art Question Answering (Q/A) system in order to retrieve a set of passages that can later be merged into a comprehensive answer by a Multi-Document Summarization (MDS) system. In our evaluations, we show that access to the decompositions generated using this method can significantly enhance the relevance and comprehensiveness of summary-length answers to complex questions.

References

[1]
E. Brill. Transformation-Based Error-Driven Learning and Natural Language Processing: A Case Study in Part of Speech Tagging. Computational Linguistics, 21(4), 1995.
[2]
M. Collins. Head-Driven Statistical Models for Natural Language Parsing. PhD thesis, University of Pennsylvania, 1999.
[3]
S. Harabagiu. Incremental Topic Representations. In Proceedings of the 20th COLING Conference, Geneva, Switzerland, 2004.
[4]
S. Harabagiu, A. Hickl, J. Lehmann, and D. Moldovan. Experiments with Interactive Question-Answering. In Proceedings of the 43rd Annual Meeting of the Association for Computational Linguistics (ACL'05), 2005.
[5]
S. Harabagiu, D. Moldovan, C. Clark, M. Bowden, A. Hickl, and P. Wang. Employing Two Question Answering Systems in TREC 2005. In Proceedings of the Fourteenth Text REtrieval Conference, 2005.
[6]
S. Harabagiu, D. Moldovan, C. Clark, M. Bowden, J. Williams, and J. Bensley. Answer Mining by Combining Extraction Techniques with Abductive Reasoning. In Proceedings of the Twelfth Text REtrieval Conference, 2003.
[7]
F. Lacatusu, A. Hickl, P. Aarseth, and L. Taylor. Lite-GISTexter at DUC 2005. In Proceedings of the Document Understanding Workshop (DUC-2005), 2005.
[8]
F. Lacatusu, A. Hickl, and S. Harabagiu. Impact of Question Decomposition on the Quality of Answer Summaries. In Proceedings of the fifth international conference on Language Resources and Evaluation, (LREC 2006), 2006.
[9]
J. Lafferty and C. Zhai. Document language models, query models, and risk minimization for information retrieval. In 2001 ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR), 2001.
[10]
C.-Y. Lin and E. Hovy. The automated acquisition of topic signatures for text summarization. In Proceedings of the 18th COLING Conference, Saarbrücken, Germany, 2000.
[11]
S. Lytinen and N. Tomuro. The Use of Question Types to Match Questions in FAQFinder. In Papers from the 2002 AAAI Spring Symposium on Mining Answers from Texts and Knowledge Bases, pages 46--53, 2002.
[12]
G. A. Miller. WordNet: a lexical database for English. Communications of the Association for Computing Machinery, 38(11):39--41, 1995.
[13]
S. Narayanan and S. Harabagiu. Question Answering based on Semantic Structures. In Proceedings of COLING-2004, 2004.
[14]
A. Nenkova and R. Passonneau. Evaluating Content Selection in Summarization: the Pyramid Method. In HLT-NAACL 2004, Boston, MA, 2004.
[15]
J. Otterbacher, G. Erkan, and D. Radev. Using random walks for question-focused sentence retrieval. In Proceedings of Human Language Technology Conference and Empirical Methods in Natural Language Processing (HLT/EMNLP 2005), Vancouver, Canada, 2005.
[16]
M. Palmer, D. Gildea, and P. Kingsbury. The Proposition Bank: An Annotated Corpus of Semantic Roles. Computational Linguistics, 31(1):71--106, 2005.
[17]
M. Pasca and S. Harabagiu. High Performance Question/Answering. In Proceedings of the 24th Annual International ACM SIGIR Conference, 2001.
[18]
M. Thelen and E. Riloff. A Bootstrapping Method for Learning Semantic Lexicons using Extraction Pattern Contexts. In Proceedings of the 2002 Conference on Empirical Methods in Natural Language Processing (EMNLP 2002), 2002.

Cited By

View all
  • (2019)Results and Lessons of the Question Answering Track at CLEFInformation Retrieval Evaluation in a Changing World10.1007/978-3-030-22948-1_18(441-460)Online publication date: 14-Aug-2019
  • (2019)Learning Discourse-Level Structures for Question AnsweringDeveloping Enterprise Chatbots10.1007/978-3-030-04299-8_7(177-219)Online publication date: 5-Apr-2019
  • (2017)A new and efficient method based on syntactic dependency relations features for ad hoc clinical question classificationInternational Journal of Bioinformatics Research and Applications10.1504/IJBRA.2017.08315013:2(161-177)Online publication date: 1-Jan-2017
  • Show More Cited By

Recommendations

Comments

Information & Contributors

Information

Published In

cover image ACM Conferences
SIGIR '06: Proceedings of the 29th annual international ACM SIGIR conference on Research and development in information retrieval
August 2006
768 pages
ISBN:1595933697
DOI:10.1145/1148170
Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

Sponsors

Publisher

Association for Computing Machinery

New York, NY, United States

Publication History

Published: 06 August 2006

Permissions

Request permissions for this article.

Check for updates

Author Tags

  1. question answering
  2. summarization

Qualifiers

  • Article

Conference

SIGIR06
Sponsor:
SIGIR06: The 29th Annual International SIGIR Conference
August 6 - 11, 2006
Washington, Seattle, USA

Acceptance Rates

Overall Acceptance Rate 792 of 3,983 submissions, 20%

Contributors

Other Metrics

Bibliometrics & Citations

Bibliometrics

Article Metrics

  • Downloads (Last 12 months)9
  • Downloads (Last 6 weeks)1
Reflects downloads up to 17 Feb 2025

Other Metrics

Citations

Cited By

View all
  • (2019)Results and Lessons of the Question Answering Track at CLEFInformation Retrieval Evaluation in a Changing World10.1007/978-3-030-22948-1_18(441-460)Online publication date: 14-Aug-2019
  • (2019)Learning Discourse-Level Structures for Question AnsweringDeveloping Enterprise Chatbots10.1007/978-3-030-04299-8_7(177-219)Online publication date: 5-Apr-2019
  • (2017)A new and efficient method based on syntactic dependency relations features for ad hoc clinical question classificationInternational Journal of Bioinformatics Research and Applications10.1504/IJBRA.2017.08315013:2(161-177)Online publication date: 1-Jan-2017
  • (2017)Towards question identification from online healthcare consultation forum post in bahasa2017 International Conference on Asian Language Processing (IALP)10.1109/IALP.2017.8300627(399-402)Online publication date: Dec-2017
  • (2017)Matching parse thickets for open domain question answeringData & Knowledge Engineering10.1016/j.datak.2016.11.002107:C(24-50)Online publication date: 1-Jan-2017
  • (2016)Addressing Complex and Subjective Product-Related Queries with Customer ReviewsProceedings of the 25th International Conference on World Wide Web10.1145/2872427.2883044(625-635)Online publication date: 11-Apr-2016
  • (2015)Towards topic-to-question generationComputational Linguistics10.1162/COLI_a_0020641:1(1-20)Online publication date: 1-Mar-2015
  • (2015)A reinforcement learning formulation to the complex question answering problemInformation Processing and Management: an International Journal10.1016/j.ipm.2015.01.00251:3(252-272)Online publication date: 1-May-2015
  • (2012)Improving the performance of the reinforcement learning model for answering complex questionsProceedings of the 21st ACM international conference on Information and knowledge management10.1145/2396761.2398676(2499-2502)Online publication date: 29-Oct-2012
  • (2012)SUBTOPIC‐BASED MULTIMODALITY RANKING FOR TOPIC‐FOCUSED MULTIDOCUMENT SUMMARIZATIONComputational Intelligence10.1111/j.1467-8640.2012.00435.x29:4(627-648)Online publication date: 26-Jun-2012
  • Show More Cited By

View Options

Login options

View options

PDF

View or Download as a PDF file.

PDF

eReader

View online with eReader.

eReader

Figures

Tables

Media

Share

Share

Share this Publication link

Share on social media