The original paper is in English. Non-English content has been machine-translated and may contain typographical errors or mistranslations. ex. Some numerals are expressed as "XNUMX".
Copyrights notice
The original paper is in English. Non-English content has been machine-translated and may contain typographical errors or mistranslations. Copyrights notice
Kertas kerja ini memperkenalkan enjin carian masa depan dan masa lalu, ChronoSeeker, yang boleh membantu pengguna untuk membangunkan strategi jangka panjang untuk organisasi mereka. Untuk menyediakan carian atas permintaan, kami menangani dua isu teknikal: (1) mengatur carian acara yang cekap dan (2) menapis bunyi daripada hasil carian. Sistem kami menggunakan pengembangan pertanyaan dengan ungkapan biasa yang berkaitan dengan maklumat acara seperti ungkapan tahun, pengubah masa dan istilah konteks untuk carian acara yang cekap. Kami menggunakan teknik pembelajaran mesin untuk menapis bunyi untuk mengklasifikasikan calon kepada maklumat atau maklumat bukan peristiwa, menggunakan ciri heuristik dan corak leksikal yang diperoleh daripada pendekatan perlombongan teks. Percubaan kami mendedahkan bahawa penapisan mencapai 85% ukuran F dan pengembangan pertanyaan itu boleh mengumpulkan berpuluh-puluh lebih peristiwa daripada yang tanpa pengembangan.
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Salinan
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
Salinan
@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},}
Salinan
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 -