IEICE TRANSACTIONS on Information
Query Expansion and Text Mining for ChronoSeeker -- Search Engine for Future/Past Events --
Summary :
This paper introduces a future and past search engine, ChronoSeeker, which can help users to develop long-term strategies for their organizations. To provide on-demand searches, we tackled two technical issues: (1) organizing efficient event searches and (2) filtering out noises from search results. Our system employed query expansion with typical expressions related to event information such as year expressions, temporal modifiers, and context terms for efficient event searches. We utilized a machine-learning technique of filtering noise to classify candidates into information or non-event information, using heuristic features and lexical patterns derived from a text-mining approach. Our experiment revealed that filtering achieved an 85% F-measure, and that query expansion could collect dozens more events than those without expansion.
- Publication
- IEICE TRANSACTIONS on Information Vol.E94-D No.3 pp.552-563
- Publication Date
- 2011/03/01
- Publicized
- Online ISSN
- 1745-1361
- DOI
- 10.1587/transinf.E94.D.552
- Type of Manuscript
- Special Section PAPER (Special Section on Data Engineering)
- Category
Authors
Keyword
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Hideki KAWAI, Adam JATOWT, Katsumi TANAKA, Kazuo KUNIEDA, Keiji YAMADA, "Query Expansion and Text Mining for ChronoSeeker -- Search Engine for Future/Past Events --" in IEICE TRANSACTIONS on Information,
vol. E94-D, no. 3, pp. 552-563, March 2011, doi: 10.1587/transinf.E94.D.552.
Abstract: This paper introduces a future and past search engine, ChronoSeeker, which can help users to develop long-term strategies for their organizations. To provide on-demand searches, we tackled two technical issues: (1) organizing efficient event searches and (2) filtering out noises from search results. Our system employed query expansion with typical expressions related to event information such as year expressions, temporal modifiers, and context terms for efficient event searches. We utilized a machine-learning technique of filtering noise to classify candidates into information or non-event information, using heuristic features and lexical patterns derived from a text-mining approach. Our experiment revealed that filtering achieved an 85% F-measure, and that query expansion could collect dozens more events than those without expansion.
URL: https://global.ieice.org/en_transactions/information/10.1587/transinf.E94.D.552/_p
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@ARTICLE{e94-d_3_552,
author={Hideki KAWAI, Adam JATOWT, Katsumi TANAKA, Kazuo KUNIEDA, Keiji YAMADA, },
journal={IEICE TRANSACTIONS on Information},
title={Query Expansion and Text Mining for ChronoSeeker -- Search Engine for Future/Past Events --},
year={2011},
volume={E94-D},
number={3},
pages={552-563},
abstract={This paper introduces a future and past search engine, ChronoSeeker, which can help users to develop long-term strategies for their organizations. To provide on-demand searches, we tackled two technical issues: (1) organizing efficient event searches and (2) filtering out noises from search results. Our system employed query expansion with typical expressions related to event information such as year expressions, temporal modifiers, and context terms for efficient event searches. We utilized a machine-learning technique of filtering noise to classify candidates into information or non-event information, using heuristic features and lexical patterns derived from a text-mining approach. Our experiment revealed that filtering achieved an 85% F-measure, and that query expansion could collect dozens more events than those without expansion.},
keywords={},
doi={10.1587/transinf.E94.D.552},
ISSN={1745-1361},
month={March},}
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TY - JOUR
TI - Query Expansion and Text Mining for ChronoSeeker -- Search Engine for Future/Past Events --
T2 - IEICE TRANSACTIONS on Information
SP - 552
EP - 563
AU - Hideki KAWAI
AU - Adam JATOWT
AU - Katsumi TANAKA
AU - Kazuo KUNIEDA
AU - Keiji YAMADA
PY - 2011
DO - 10.1587/transinf.E94.D.552
JO - IEICE TRANSACTIONS on Information
SN - 1745-1361
VL - E94-D
IS - 3
JA - IEICE TRANSACTIONS on Information
Y1 - March 2011
AB - This paper introduces a future and past search engine, ChronoSeeker, which can help users to develop long-term strategies for their organizations. To provide on-demand searches, we tackled two technical issues: (1) organizing efficient event searches and (2) filtering out noises from search results. Our system employed query expansion with typical expressions related to event information such as year expressions, temporal modifiers, and context terms for efficient event searches. We utilized a machine-learning technique of filtering noise to classify candidates into information or non-event information, using heuristic features and lexical patterns derived from a text-mining approach. Our experiment revealed that filtering achieved an 85% F-measure, and that query expansion could collect dozens more events than those without expansion.
ER -
English
