Extracting relevant snippets for web navigation

Qing Li, Kasim Candan, Qi Yan

Research output: Chapter in Book/Report/Conference proceedingConference contribution

7 Scopus citations

Abstract

Search engines present fix-length passages from documents ranked by relevance against the query. In this paper, we present and compare novel, language-model based methods for extracting variable length document snippets by real-time processing of documents using the query issued by the user. With this extra level of information, the returned snippets are considerably more informative. Unlike previous work on passage retrieval which relies on searching relevant segments for filtering of preoccupied passages, we focus on query-informed segmentation to extract context-aware relevant snippets with variable length. In particular, we show that, when informed through an appropriate relevance language model, curvature analysis and Hidden Markov model (HMM) based content segmentation techniques can facilitate to extract relevant document snippets.

Original languageEnglish (US)
Title of host publicationAAAI-08/IAAI-08 Proceedings - 23rd AAAI Conference on Artificial Intelligence and the 20th Innovative Applications of Artificial Intelligence Conference
Pages1195-1200
Number of pages6
StatePublished - Dec 24 2008
Event23rd AAAI Conference on Artificial Intelligence and the 20th Innovative Applications of Artificial Intelligence Conference, AAAI-08/IAAI-08 - Chicago, IL, United States
Duration: Jul 13 2008Jul 17 2008

Publication series

NameProceedings of the National Conference on Artificial Intelligence
Volume2

Other

Other23rd AAAI Conference on Artificial Intelligence and the 20th Innovative Applications of Artificial Intelligence Conference, AAAI-08/IAAI-08
CountryUnited States
CityChicago, IL
Period7/13/087/17/08

ASJC Scopus subject areas

  • Software
  • Artificial Intelligence

Fingerprint Dive into the research topics of 'Extracting relevant snippets for web navigation'. Together they form a unique fingerprint.

  • Cite this

    Li, Q., Candan, K., & Yan, Q. (2008). Extracting relevant snippets for web navigation. In AAAI-08/IAAI-08 Proceedings - 23rd AAAI Conference on Artificial Intelligence and the 20th Innovative Applications of Artificial Intelligence Conference (pp. 1195-1200). (Proceedings of the National Conference on Artificial Intelligence; Vol. 2).