Sub-topic detection and tracking based on dependency connection weights for vector space model

Aiming at the phenomenon that there are abrupt reports,similar topics and abundant levels of subtopics in the news,a novel method based on relationship analysis using dependent sentence pattern was proposed for sub-topic detection and tracking (sTDT),which constructed feature dimensions to generate...

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Main Authors: Xue-guang ZHOU, Fei GAO, Yan SUN
Format: Article
Language:zho
Published: Editorial Department of Journal on Communications 2013-08-01
Series:Tongxin xuebao
Subjects:
Online Access:http://www.joconline.com.cn/zh/article/doi/10.3969/j.issn.1000-436x.2013.08.001/
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author Xue-guang ZHOU
Fei GAO
Yan SUN
author_facet Xue-guang ZHOU
Fei GAO
Yan SUN
author_sort Xue-guang ZHOU
collection DOAJ
description Aiming at the phenomenon that there are abrupt reports,similar topics and abundant levels of subtopics in the news,a novel method based on relationship analysis using dependent sentence pattern was proposed for sub-topic detection and tracking (sTDT),which constructed feature dimensions to generate the global vectors according to the increment of TF-IDF,and then created the partial adjoin map based on the connection weights within the time window and decreased the dimensions through dependent sentence pattern.Finally,a novel method for sTDT computing was built with adjoins dictionary weights and time threshold attenuation.Experiments show that the proposed method transferrs the text from linear to plane structure,and extracts the subtopics effectively,of which the minimum DET cost is reduced by at least 2.2 percent than that of classical methods.
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institution Kabale University
issn 1000-436X
language zho
publishDate 2013-08-01
publisher Editorial Department of Journal on Communications
record_format Article
series Tongxin xuebao
spelling doaj-art-eabc981d86d44171b457ca963ad1ddf82025-01-14T06:40:58ZzhoEditorial Department of Journal on CommunicationsTongxin xuebao1000-436X2013-08-01341959673950Sub-topic detection and tracking based on dependency connection weights for vector space modelXue-guang ZHOUFei GAOYan SUNAiming at the phenomenon that there are abrupt reports,similar topics and abundant levels of subtopics in the news,a novel method based on relationship analysis using dependent sentence pattern was proposed for sub-topic detection and tracking (sTDT),which constructed feature dimensions to generate the global vectors according to the increment of TF-IDF,and then created the partial adjoin map based on the connection weights within the time window and decreased the dimensions through dependent sentence pattern.Finally,a novel method for sTDT computing was built with adjoins dictionary weights and time threshold attenuation.Experiments show that the proposed method transferrs the text from linear to plane structure,and extracts the subtopics effectively,of which the minimum DET cost is reduced by at least 2.2 percent than that of classical methods.http://www.joconline.com.cn/zh/article/doi/10.3969/j.issn.1000-436x.2013.08.001/topic detection and trackingdependency connection weightsassociating words groupreport relation detectionvector space model
spellingShingle Xue-guang ZHOU
Fei GAO
Yan SUN
Sub-topic detection and tracking based on dependency connection weights for vector space model
Tongxin xuebao
topic detection and tracking
dependency connection weights
associating words group
report relation detection
vector space model
title Sub-topic detection and tracking based on dependency connection weights for vector space model
title_full Sub-topic detection and tracking based on dependency connection weights for vector space model
title_fullStr Sub-topic detection and tracking based on dependency connection weights for vector space model
title_full_unstemmed Sub-topic detection and tracking based on dependency connection weights for vector space model
title_short Sub-topic detection and tracking based on dependency connection weights for vector space model
title_sort sub topic detection and tracking based on dependency connection weights for vector space model
topic topic detection and tracking
dependency connection weights
associating words group
report relation detection
vector space model
url http://www.joconline.com.cn/zh/article/doi/10.3969/j.issn.1000-436x.2013.08.001/
work_keys_str_mv AT xueguangzhou subtopicdetectionandtrackingbasedondependencyconnectionweightsforvectorspacemodel
AT feigao subtopicdetectionandtrackingbasedondependencyconnectionweightsforvectorspacemodel
AT yansun subtopicdetectionandtrackingbasedondependencyconnectionweightsforvectorspacemodel