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An Aging Theory for Event Life-Cycle Modeling
An event can be described by a sequence of chronological documents from several information sources that together describe a story or happening. The goal of event detection and tracking is to automatically identify events and their associated documents during their life cycles. Conventional document...
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| Published in: | IEEE transactions on systems, man and cybernetics. Part A, Systems and humans man and cybernetics. Part A, Systems and humans, 2007-03, Vol.37 (2), p.237-248 |
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| Language: | English |
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| cites | cdi_FETCH-LOGICAL-c433t-30fcbc0bae0acc1479d5aa9855cd67da7f1de14f0a6354a6ad71a197df221d8d3 |
| container_end_page | 248 |
| container_issue | 2 |
| container_start_page | 237 |
| container_title | IEEE transactions on systems, man and cybernetics. Part A, Systems and humans |
| container_volume | 37 |
| creator | Chen, Chien Chin Chen, Yao-Tsung Chen, Meng Chang |
| description | An event can be described by a sequence of chronological documents from several information sources that together describe a story or happening. The goal of event detection and tracking is to automatically identify events and their associated documents during their life cycles. Conventional document clustering and classification techniques cannot effectively detect and track sequential events, as they ignore the temporal relationships among documents related to an event. The life cycle of an event is analogous to living beings. With abundant nourishment (i.e., related documents for the event), the life cycle is prolonged; conversely, an event or living fades away when nourishment is exhausted. Improper tracking algorithms often unnecessarily prolong or shorten the life cycle of detected events. In this paper, we propose an aging theory to model the life cycle of sequential events, which incorporates a traditional single-pass clustering algorithm to detect and track events. Our experiment results show that the proposed method achieves a better overall performance for both long-running and short-term events than previous approaches. Moreover, we find that the aging parameters of the aging schemes are profile dependent and that using proper profile-specific aging parameters improves the detection and tracking performance further |
| doi_str_mv | 10.1109/TSMCA.2006.886370 |
| format | article |
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Our experiment results show that the proposed method achieves a better overall performance for both long-running and short-term events than previous approaches. 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Part A, Systems and humans</title><addtitle>TSMCA</addtitle><description>An event can be described by a sequence of chronological documents from several information sources that together describe a story or happening. The goal of event detection and tracking is to automatically identify events and their associated documents during their life cycles. Conventional document clustering and classification techniques cannot effectively detect and track sequential events, as they ignore the temporal relationships among documents related to an event. The life cycle of an event is analogous to living beings. With abundant nourishment (i.e., related documents for the event), the life cycle is prolonged; conversely, an event or living fades away when nourishment is exhausted. Improper tracking algorithms often unnecessarily prolong or shorten the life cycle of detected events. 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Moreover, we find that the aging parameters of the aging schemes are profile dependent and that using proper profile-specific aging parameters improves the detection and tracking performance further</description><subject>Aging</subject><subject>Algorithms</subject><subject>Classification</subject><subject>Clustering</subject><subject>Clustering algorithms</subject><subject>Cybernetics</subject><subject>Event detection</subject><subject>Information science</subject><subject>Internet</subject><subject>knowledge life cycle</subject><subject>Life cycle engineering</subject><subject>Mathematical models</subject><subject>Publishing</subject><subject>Search engines</subject><subject>Studies</subject><subject>Temporal logic</subject><subject>Text categorization</subject><subject>Tracking</subject><subject>Web mining</subject><issn>1083-4427</issn><issn>2168-2216</issn><issn>1558-2426</issn><issn>2168-2232</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2007</creationdate><recordtype>article</recordtype><recordid>eNpdkEtLw0AUhQdRsFZ_gLgJblyl3pt5L0PwBS0urOthOo-akiY10wr996ZWXLi6Z_Gdw-Uj5Bphggj6fv42q8pJASAmSgkq4YSMkHOVF6wQp0MGRXPGCnlOLlJaASBjmo1IXrZZuazbZTb_CF2_z2LXZw9fod1m0zqGvNq7JmSzzodmgC7JWbRNCle_d0zeHx_m1XM-fX16qcpp7hil25xCdAsHCxvAOodMas-t1Ypz54X0Vkb0AVkEKyhnVlgv0aKWPhYFeuXpmNwddzd997kLaWvWdXKhaWwbul0ySoHgmnE5kLf_yFW369vhOaMEQ1S80AOER8j1XUp9iGbT12vb7w2COegzP_rMQZ856hs6N8dOHUL44xkCSKHpNwg1aew</recordid><startdate>20070301</startdate><enddate>20070301</enddate><creator>Chen, Chien Chin</creator><creator>Chen, Yao-Tsung</creator><creator>Chen, Meng Chang</creator><general>IEEE</general><general>The Institute of Electrical and Electronics Engineers, Inc. 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Part A, Systems and humans</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Chen, Chien Chin</au><au>Chen, Yao-Tsung</au><au>Chen, Meng Chang</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>An Aging Theory for Event Life-Cycle Modeling</atitle><jtitle>IEEE transactions on systems, man and cybernetics. Part A, Systems and humans</jtitle><stitle>TSMCA</stitle><date>2007-03-01</date><risdate>2007</risdate><volume>37</volume><issue>2</issue><spage>237</spage><epage>248</epage><pages>237-248</pages><issn>1083-4427</issn><issn>2168-2216</issn><eissn>1558-2426</eissn><eissn>2168-2232</eissn><coden>ITSHFX</coden><abstract>An event can be described by a sequence of chronological documents from several information sources that together describe a story or happening. The goal of event detection and tracking is to automatically identify events and their associated documents during their life cycles. Conventional document clustering and classification techniques cannot effectively detect and track sequential events, as they ignore the temporal relationships among documents related to an event. The life cycle of an event is analogous to living beings. With abundant nourishment (i.e., related documents for the event), the life cycle is prolonged; conversely, an event or living fades away when nourishment is exhausted. Improper tracking algorithms often unnecessarily prolong or shorten the life cycle of detected events. In this paper, we propose an aging theory to model the life cycle of sequential events, which incorporates a traditional single-pass clustering algorithm to detect and track events. 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| fulltext | fulltext |
| identifier | ISSN: 1083-4427 |
| ispartof | IEEE transactions on systems, man and cybernetics. Part A, Systems and humans, 2007-03, Vol.37 (2), p.237-248 |
| issn | 1083-4427 2168-2216 1558-2426 2168-2232 |
| language | eng |
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| source | IEEE Electronic Library (IEL) Journals |
| subjects | Aging Algorithms Classification Clustering Clustering algorithms Cybernetics Event detection Information science Internet knowledge life cycle Life cycle engineering Mathematical models Publishing Search engines Studies Temporal logic Text categorization Tracking Web mining |
| title | An Aging Theory for Event Life-Cycle Modeling |
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