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abstract

Learning Web Queries for Retrieval of Relevant Information about an Entity in a Wikipedia Category

Published: 11 April 2016 Publication History

Abstract

In this paper, we present a novel method to obtain a set of most appropriate queries for retrieval of relevant information about an entity from the Web. Using the body text of existing articles in a Wikipedia category, we generate a set of queries capable of fetching the most relevant content for any entity belonging to that category. We find the common topics discussed in the articles of a category using Latent Semantic Analysis (LSA) and use them to formulate the queries. Using Long Short-Term Memory (LSTM) neural network, we reduce the number of queries by removing the less sensible ones and then select the best ones out of them. The experimental results show that the proposed method outperforms the baselines. Existing approaches are performing better in generation of the relevant section title queries by extraction from the headings of the Wikipedia articles as compared to the generation of queries by extraction from the body text of the articles. Whereas, the experimental results show that the proposed approach can perform equally well and even better in extraction of the relevant queries from the body text of the Wikipedia articles.

References

[1]
Sauper, C., and Barzilay, R. 2009. Automatically Generating Wikipedia Articles: A Structure-aware Approach. In Proc. of the Joint Conf. of the 47th Annual Meeting of the ACL and the 4th Intl Joint Conf. on NLP of the AFNLP: Volume 1 - Volume 1, pages 208--216
[2]
Tanaka, S., Okazaki, N., and Ishizuka, M. 2010. Learning Web Query Patterns for Imitating Wikipedia Articles. In Proc. of 23rd Intl Conf. On Computational Linguistics (COLING 2010) -- Poster Volume, pp. 1229--1237.
[3]
Sutskever, I., Martens, J., and Hinton, G. Generating Text with Recurrent Neural Networks. ICML 2011
[4]
Mikolov, T., Sutskever, I., Chen, K., Corrado, G., and Dean, J. 2013. Distributed Representations of Words and Phrases and their Compositionality. In Proceedings of NIPS, 2013.

Cited By

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  • (2018)A Wikipedia powered state-based approach to automatic search query enhancementInformation Processing and Management: an International Journal10.1016/j.ipm.2017.10.00154:4(726-739)Online publication date: 1-Jul-2018
  • (2017)A Comparison of Automatic Search Query Enhancement Algorithms That Utilise Wikipedia as a Source of A Priori KnowledgeProceedings of the 9th Annual Meeting of the Forum for Information Retrieval Evaluation10.1145/3158354.3158356(6-13)Online publication date: 8-Dec-2017

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WWW '16 Companion: Proceedings of the 25th International Conference Companion on World Wide Web
April 2016
1094 pages
ISBN:9781450341448

Sponsors

  • IW3C2: International World Wide Web Conference Committee

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International World Wide Web Conferences Steering Committee

Republic and Canton of Geneva, Switzerland

Publication History

Published: 11 April 2016

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Author Tags

  1. information retrieval
  2. long short-term memory neural networks (lstm)
  3. query generation
  4. wikipedia article generation

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WWW '16
Sponsor:
  • IW3C2
WWW '16: 25th International World Wide Web Conference
April 11 - 15, 2016
Québec, Montréal, Canada

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WWW '16 Companion Paper Acceptance Rate 115 of 727 submissions, 16%;
Overall Acceptance Rate 1,899 of 8,196 submissions, 23%

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Cited By

View all
  • (2018)A Wikipedia powered state-based approach to automatic search query enhancementInformation Processing and Management: an International Journal10.1016/j.ipm.2017.10.00154:4(726-739)Online publication date: 1-Jul-2018
  • (2017)A Comparison of Automatic Search Query Enhancement Algorithms That Utilise Wikipedia as a Source of A Priori KnowledgeProceedings of the 9th Annual Meeting of the Forum for Information Retrieval Evaluation10.1145/3158354.3158356(6-13)Online publication date: 8-Dec-2017

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