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 language | English (US) |
|---|---|
| Title of host publication | Proceedings of the National Conference on Artificial Intelligence |
| Pages | 1195-1200 |
| Number of pages | 6 |
| Volume | 2 |
| State | Published - 2008 |
| Event | 23rd 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 2008 → Jul 17 2008 |
Other
| Other | 23rd AAAI Conference on Artificial Intelligence and the 20th Innovative Applications of Artificial Intelligence Conference, AAAI-08/IAAI-08 |
|---|---|
| Country | United States |
| City | Chicago, IL |
| Period | 7/13/08 → 7/17/08 |
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ASJC Scopus subject areas
- Software
- Artificial Intelligence
Cite this
Extracting relevant snippets for web navigation. / Li, Qing; Candan, Kasim; Yan, Qi.
Proceedings of the National Conference on Artificial Intelligence. Vol. 2 2008. p. 1195-1200.Research output: Chapter in Book/Report/Conference proceeding › Conference contribution
}
TY - GEN
T1 - Extracting relevant snippets for web navigation
AU - Li, Qing
AU - Candan, Kasim
AU - Yan, Qi
PY - 2008
Y1 - 2008
N2 - 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.
AB - 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.
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UR - http://www.scopus.com/inward/citedby.url?scp=57749189559&partnerID=8YFLogxK
M3 - Conference contribution
AN - SCOPUS:57749189559
SN - 9781577353683
VL - 2
SP - 1195
EP - 1200
BT - Proceedings of the National Conference on Artificial Intelligence
ER -