Enhancing information retrieval through concept-based language modeling and semantic smoothing.
Traditionally, many information retrieval models assume that terms occur in documents independently. Although these models have already shown good performance, the word independency assumption seems to be unrealistic from a natural language point of view, which considers that terms are related to ea...
| Publicado en: | Journal of the Association for Information Science & Technology Vol. 67; no. 12; pp. 2909 - 2927 |
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| Autores principales: | , , |
| Formato: | equations & formulas research tables/charts Journal Article |
| Publicado: |
Wiley-Blackwell
Dec2016
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| Acceso en línea: | Ver este registro en EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ccm&AN=119478025&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 119478025 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 23301635 H6JN jtl: Journal of the Association for Information Science & Technology issn: 23301635 maglogo: N pubinfo: dt: Dec2016 vid: 67 iid: 12 pid: 480 pub: Wiley-Blackwell place: Malden, Massachusetts artinfo: ui: 119478025 119478025 119478025 10.1002/asi.23553 119478025 ppf: 2909 ppct: 18 formats: tig: atl: Enhancing information retrieval through concept-based language modeling and semantic smoothing. aug: au: Said Lhadj, Lynda Boughanem, Mohand Amrouche, Karima affil: Ecole nationale Supérieure d'Informatique ESI, P.O. Box 68 M, 16309, Algiers Algeria sug: subj: Information Retrieval Semantics Models, Statistical Human News Experimental Studies ab: Traditionally, many information retrieval models assume that terms occur in documents independently. Although these models have already shown good performance, the word independency assumption seems to be unrealistic from a natural language point of view, which considers that terms are related to each other. Therefore, such an assumption leads to two well-known problems in information retrieval ( IR), namely, polysemy, or term mismatch, and synonymy. In language models, these issues have been addressed by considering dependencies such as bigrams, phrasal-concepts, or word relationships, but such models are estimated using simple n-grams or concept counting. In this paper, we address polysemy and synonymy mismatch with a concept-based language modeling approach that combines ontological concepts from external resources with frequently found collocations from the document collection. In addition, the concept-based model is enriched with subconcepts and semantic relationships through a semantic smoothing technique so as to perform semantic matching. Experiments carried out on TREC collections show that our model achieves significant improvements over a single word-based model and the Markov Random Field model (using a Markov classifier). pubtype: Academic Journal doctype: equations & formulas research tables/charts Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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