IEICE TRANSACTIONS on Information
Effectiveness of Passage-Based Document Retrieval for Short Queries
Summary :
Document retrieval is a fundamental but important task for intelligent access to a huge amount of information stored in documents. Although the history of its research is long, it is still a hard task especially in the case that lengthy documents are retrieved with very short queries (a few keywords). For the retrieval of long documents, methods called passage-based document retrieval have proven to be effective. In this paper, we experimentally show that a passage-based method based on window passages is also effective for dealing with short queries on condition that documents are not too short. We employ a method called "density distributions" as a method based on window passages, and compare it with three conventional methods: the simple vector space model, pseudo relevance feedback and latent semantic indexing. We also compare it with a passage-based method based on discourse passages.
- Publication
- IEICE TRANSACTIONS on Information Vol.E86-D No.9 pp.1753-1761
- Publication Date
- 2003/09/01
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- Online ISSN
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- Type of Manuscript
- Special Section PAPER (Special Issue on Text Processing for Information Access)
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Koichi KISE, Markus JUNKER, Andreas DENGEL, Keinosuke MATSUMOTO, "Effectiveness of Passage-Based Document Retrieval for Short Queries" in IEICE TRANSACTIONS on Information,
vol. E86-D, no. 9, pp. 1753-1761, September 2003, doi: .
Abstract: Document retrieval is a fundamental but important task for intelligent access to a huge amount of information stored in documents. Although the history of its research is long, it is still a hard task especially in the case that lengthy documents are retrieved with very short queries (a few keywords). For the retrieval of long documents, methods called passage-based document retrieval have proven to be effective. In this paper, we experimentally show that a passage-based method based on window passages is also effective for dealing with short queries on condition that documents are not too short. We employ a method called "density distributions" as a method based on window passages, and compare it with three conventional methods: the simple vector space model, pseudo relevance feedback and latent semantic indexing. We also compare it with a passage-based method based on discourse passages.
URL: https://global.ieice.org/en_transactions/information/10.1587/e86-d_9_1753/_p
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@ARTICLE{e86-d_9_1753,
author={Koichi KISE, Markus JUNKER, Andreas DENGEL, Keinosuke MATSUMOTO, },
journal={IEICE TRANSACTIONS on Information},
title={Effectiveness of Passage-Based Document Retrieval for Short Queries},
year={2003},
volume={E86-D},
number={9},
pages={1753-1761},
abstract={Document retrieval is a fundamental but important task for intelligent access to a huge amount of information stored in documents. Although the history of its research is long, it is still a hard task especially in the case that lengthy documents are retrieved with very short queries (a few keywords). For the retrieval of long documents, methods called passage-based document retrieval have proven to be effective. In this paper, we experimentally show that a passage-based method based on window passages is also effective for dealing with short queries on condition that documents are not too short. We employ a method called "density distributions" as a method based on window passages, and compare it with three conventional methods: the simple vector space model, pseudo relevance feedback and latent semantic indexing. We also compare it with a passage-based method based on discourse passages.},
keywords={},
doi={},
ISSN={},
month={September},}
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TY - JOUR
TI - Effectiveness of Passage-Based Document Retrieval for Short Queries
T2 - IEICE TRANSACTIONS on Information
SP - 1753
EP - 1761
AU - Koichi KISE
AU - Markus JUNKER
AU - Andreas DENGEL
AU - Keinosuke MATSUMOTO
PY - 2003
DO -
JO - IEICE TRANSACTIONS on Information
SN -
VL - E86-D
IS - 9
JA - IEICE TRANSACTIONS on Information
Y1 - September 2003
AB - Document retrieval is a fundamental but important task for intelligent access to a huge amount of information stored in documents. Although the history of its research is long, it is still a hard task especially in the case that lengthy documents are retrieved with very short queries (a few keywords). For the retrieval of long documents, methods called passage-based document retrieval have proven to be effective. In this paper, we experimentally show that a passage-based method based on window passages is also effective for dealing with short queries on condition that documents are not too short. We employ a method called "density distributions" as a method based on window passages, and compare it with three conventional methods: the simple vector space model, pseudo relevance feedback and latent semantic indexing. We also compare it with a passage-based method based on discourse passages.
ER -
English
