Chinese document re-ranking based on automatically acquired term resource.
In this paper, we address the problem of document re-ranking in information retrieval, which is usually conducted after initial retrieval to improve rankings of relevant documents. To deal with this problem, we propose a method which automatically constructs a term resource specific to the document...
| Publicado en: | Language Resources & Evaluation Vol. 43; no. 4; pp. 385 - 407 |
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| Autores principales: | , , |
| Formato: | Artículo |
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Springer Nature
Dec2009
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| Materias: | |
| Acceso en línea: | Ver este registro en EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=hlh&AN=45284387&site=ehost-live header: @attributes: shortDbName: hlh uiTerm: 45284387 longDbName: Humanities International Complete uiTag: AN controlInfo: bkinfo: jinfo: jid: 1574020X 179V jtl: Language Resources & Evaluation issn: 1574020X maglogo: N pubinfo: dt: Dec2009 vid: 43 iid: 4 pid: 237 pub: Springer Nature artinfo: ui: 45284387 10.1007/s10579-009-9106-z ppf: 385 ppct: 22 formats: fmt: @attributes: type: P size: 780KB tig: atl: Chinese document re-ranking based on automatically acquired term resource. aug: au: Donghong Ji Shiju Zhao Guozheng Xiao affil: Department of Computer Science, Center for Study of Language Information, Wuhan University, 430072 Wuhan, China. Department of Chinese Language and Literature, Wuhan University, 430072 Wuhan, China. Center for Study of Language Information, Wuhan University, 430072 Wuhan, China. su: Information resources management Search engines Information retrieval Artificial intelligence Electronic data processing sug: subj: Information resources management Search engines Information retrieval Artificial intelligence Electronic data processing keyword: Document re-ranking Maximal marginal relevance Term extraction Term weighting ab: In this paper, we address the problem of document re-ranking in information retrieval, which is usually conducted after initial retrieval to improve rankings of relevant documents. To deal with this problem, we propose a method which automatically constructs a term resource specific to the document collection and then applies the resource to document re-ranking. The term resource includes a list of terms extracted from the documents as well as their weighting and correlations computed after initial retrieval. The term weighting based on local and global distribution ensures the re-ranking not sensitive to different choices of pseudo relevance, while the term correlation helps avoid any bias to certain specific concept embedded in queries. Experiments with NTCIR3 data show that the approach can not only improve performance of initial retrieval, but also make significant contribution to standard query expansion. pubtype: Academic Journal doctype: Article src: R language: English refInfo: copyright: @attributes: flag: Y custom: Language Resources & Evaluation is a copyright of Springer, 2009. All Rights Reserved. item: Language Resources & Evaluation holder: Springer Nature dt: @attributes: year: 2009 holdings: @attributes: islocal: N |
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