Extract-biased pseudo-relevance feedback
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| Format: | Artículo científico |
| Sprache: | en |
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Asociación Española para la Inteligencia Artificial
2007
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| _version_ | 1876486443074846720 |
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| author | Wilson dos S. Batista Junior |
| author_facet | Wilson dos S. Batista Junior |
| contents | Extract-biased pseudo-relevance feedback Wilson dos S. Batista Junior Lucia Helena Machado Rino Ingeniería Pseudo Blind Feedback Relevance Feedback Automatic Summarization for Information Retrieval Successfully retrieving a web document is a twofold problem: having an adequate query that can usefully andproperly help filtering relevant documents from huge collections, and presenting the user those that may indeedfulfill his/her needs. In this paper, we focus on the first issue the problem of having a misleading user query. Theaim of the work is to refine a query by using extracts instead of full documents. Extracts, in our context, are actuallysummaries of documents of a hitlist produced by an extractive automatic summarizer. Automatic summarization ofsingle and multi-documents is explored through GistSumm, our Gist Summarizer, which is based on the gist of adocument, hence its name. Results on pseudo-relevance feedback for the Portuguese CHAVE collection show thatgist-based extracts may improve information retrieval 2007 artículo científico 1137-3601 https://www.redalyc.org/articulo.oa?id=92503607 en http://www.redalyc.org/revista.oa?id=925 Inteligencia Artificial. Revista Iberoamericana de Inteligencia Artificial application/pdf Asociación Española para la Inteligencia Artificial Inteligencia Artificial. Revista Iberoamericana de Inteligencia Artificial (España) Num.36 Vol.11 |
| format | Artículo científico |
| id | redalyc_92503607 |
| institution | Redalyc |
| language | en |
| publishDate | 2007 |
| publisher | Asociación Española para la Inteligencia Artificial |
| spellingShingle | Extract-biased pseudo-relevance feedback Wilson dos S. Batista Junior Ingeniería Pseudo Blind Feedback Relevance Feedback Automatic Summarization for Information Retrieval Extract-biased pseudo-relevance feedback Wilson dos S. Batista Junior Lucia Helena Machado Rino Ingeniería Pseudo Blind Feedback Relevance Feedback Automatic Summarization for Information Retrieval Successfully retrieving a web document is a twofold problem: having an adequate query that can usefully andproperly help filtering relevant documents from huge collections, and presenting the user those that may indeedfulfill his/her needs. In this paper, we focus on the first issue the problem of having a misleading user query. Theaim of the work is to refine a query by using extracts instead of full documents. Extracts, in our context, are actuallysummaries of documents of a hitlist produced by an extractive automatic summarizer. Automatic summarization ofsingle and multi-documents is explored through GistSumm, our Gist Summarizer, which is based on the gist of adocument, hence its name. Results on pseudo-relevance feedback for the Portuguese CHAVE collection show thatgist-based extracts may improve information retrieval 2007 artículo científico 1137-3601 https://www.redalyc.org/articulo.oa?id=92503607 en http://www.redalyc.org/revista.oa?id=925 Inteligencia Artificial. Revista Iberoamericana de Inteligencia Artificial application/pdf Asociación Española para la Inteligencia Artificial Inteligencia Artificial. Revista Iberoamericana de Inteligencia Artificial (España) Num.36 Vol.11 |
| title | Extract-biased pseudo-relevance feedback |
| topic | Ingeniería Pseudo Blind Feedback Relevance Feedback Automatic Summarization for Information Retrieval |
| url | https://www.redalyc.org/articulo.oa?id=92503607 |