BibSonomy publications for /user/diego_ma/inf_retrievalTue Jan 12 08:06:20 CET 2010Hobart{ADCS} 2008 Proceedings53--60Facilitating biomedical systematic reviews using ranked text retrieval and classification2008EBM inf_retrieval Searching and selecting articles to be included in systematic reviews is a real challenge for healthcare agencies responsible for publishing these reviews. The current practice of manually reviewing all papers returned by complex hand-crafted boolean queries is human labour-intensive and difficult to maintain. We demonstrate a two-stage searching system that takes advantage of ranked queries and support-vector machine text classification to assist in the retrieval of relevant articles, and to restrict results to higher-quality documents. Our proposed approach shows significant work saved in the systematic review process over a baseline of a keyword-based retrieval system.Tue Jan 13 03:30:41 CET 2009New York{SIGIR'03}: Proceedings of the 26th Annual International ACM SIGIR Conference on Research and Development in Information Retrieval41-47Quantitavie Evaluation of Passage Retrieval Algorithms for Question Answering2003QA inf_retrieval tobibsonomy Tue Sep 02 04:08:07 CEST 2008Proc. First International Workshop on Semantic Web and DatabasesEfficient {RDF} Storage and Retrieval in {Jena2}2003semantic_web RDF inf_retrieval RDF and related Semantic Web technologies have been the recent focus of much research activity. This work has led to new specifications for RDF and OWL. However, efficient implementations of these standards are needed to realize the vision of a world-wide semantic Web. In particular, implementations that scale to large, enterprise-class data sets are required. Jena2 is the second generation of Jena, a leading semantic web programmers' toolkit. This paper describes the persistence subsystem of Jena2 which is intended to support large datasets. This paper describes its features, the changes from Jena1, relevant details of the implementation and performance tuning issues. Query optimization for RDF is identified as a promising area for future research.Tue Sep 02 03:52:42 CEST 2008On-line{RDF} Storage and Retrieval Systems2008semantic_web inf_retrieval RDF web Ontologies are often used to improve data access. For this purpose, existing data has to be linked to an ontology and appropriate access mechanisms have to be provided. In this chapter, we review RDF storage and retrieval technologies as a common approach for accessing ontology-based data. We discuss different storage models, typical functionalities of RDF middleware such as data model support and reasoning capabilities and RDF query languages with a special focus on SPARQL as an emerging standard. We also discuss some trends such as support for expressive ontology and rule languages.Tue Jul 01 10:45:03 CEST 2008Proc. COLING Workshop on Information Retrieval for Question Answering8 pagesIndexing on Semantic Roles for Question Answering2008question_answering molla_publication AnswerFinder inf_retrieval Semantic Role Labeling (SRL) has been used successfully in several stages of automated Question Answering (QA) systems but its inherent slow procedures make it difficult to use at the indexing stage of the document retrieval component. In this paper we confirm the intuition that SRL at indexing stage improves the performance of QA and propose a simplified technique named the Question Prediction Language Model (QPLM), which provides similar information with a much lower cost. The methods were tested on four different QA systems and the results suggest that QPLM can be used as a good compromise between speed and accuracy.Tue Mar 04 07:47:41 CET 2008Proceedings ALTW83-90Pseudo Relevance Feedback Using Named Entities for Question Answering42006AnswerFinder inf_retrieval molla_publication Relevance feedback has already proven its usefulness in probabilistic information retrieval (IR). In this research we explore whether a pseudo relevance feedback technique on IR can improve the Question Answering task (QA). The basis of our exploration is the use of relevant named entities from the top retrieved documents as clues of relevance. We discuss two interesting findings from these experiments: the reasons the results were not improved, and the fact that today's metrics of IR evalu ation on QA do not reflect the results obtained by a QA system.Thu Jan 31 08:44:43 CET 2008X-TREC:2007{TREC} 2006 Genomics Track Overview2007biomedical question_answering inf_retrieval The TREC Genomics Track implemented a new task in 2006 that focused on passage retrieval for question answering using full-text documents from the biomedical literature. A test collection of 162,259 full-text documents and 28 topics expressed as questions was assembled. Systems were required to return passages that contained answers to the questions. Expert judges determined the relevance of passages and grouped them into aspects identified by one or more Medical Subject Headings (MeSH) terms. Document relevance was defined by the presence of one or more relevant aspects. The performance of submitted runs was scored using mean average precision (MAP) at the passage, aspect, and document level. In general, passage MAP was low, while aspect and document MAP were somewhat higher.Wed Jan 30 07:27:54 CET 2008Proceedings {TREC} 2006The Fifteenth {Text} {REtrieval} {Conference} ({TREC} 2006) ProceedingsSP 500-2722007inf_retrieval question_answering Tue Jan 29 07:22:02 CET 2008Proceedings ALTW92-99Question Prediction Language Model52007AnswerFinder inf_retrieval molla_publication This paper proposes the use of a language representation that specifies the relationship between terms of a sentence using question words. The proposed representation is tailored to help the search for documents containing an answer for a natural language question. This study presents the construction of this language model, the framework where it is used, and its evaluation.Fri Dec 14 02:49:40 CET 2007Hillsdale (New Jersey), Hove and LondonText-based Intelligent Systems: current research and practice in information extraction and retrieval1992inf_extraction inf_retrieval Fri Dec 14 02:49:29 CET 2007Proc. {TREC-6}500-240NIST Special PublicationThe Sixth {Text} {REtrieval} {Conference} ({TREC}-6)1997inf_retrieval Fri Dec 14 02:49:26 CET 2007Proc. {TREC-7}500-242NIST Special PublicationThe Seventh {Text} {REtrieval} {Conference} ({TREC-7})1998inf_retrieval Fri Dec 14 02:49:22 CET 2007Proc. {TREC-8}500-246NIST Special PublicationThe Eighth {Text} {REtrieval} {Conference} ({TREC-8})1999inf_retrieval Fri Dec 14 02:49:21 CET 2007Dordrecht, The NetherlandsNatural Language Information Retrieval1999NLP inf_retrieval Fri Dec 14 02:49:17 CET 2007Proc. {TREC-9}500-249NIST Special PublicationThe Ninth {Text} {REtrieval} {Conference} ({TREC-9})2000inf_retrieval Fri Dec 14 02:49:15 CET 2007Proc. ACL workshop in Recent Advances in NLP and IRProceedings of the Workshop on Recent Advances in Natural Language Processing and Information Retrieval2000NLP inf_retrieval Fri Dec 14 02:49:05 CET 2007Gaithersburg, MarylandProc. {TREC 2001}500-250NIST Special PublicationThe Tenth {Text} {REtrieval} {Conference} ({TREC 2001})2001inf_retrieval Fri Dec 14 02:49:02 CET 2007Proc. {TREC 2002}500-251NIST Special PublicationThe Eleventh {Text} {REtrieval} {Conference} ({TREC 2002})2002inf_retrieval Fri Dec 14 02:48:59 CET 2007Proc. {TREC 2003}500-255NIST Special PublicationThe Twelfth {Text} {REtrieval} {Conference} ({TREC 2003})2004inf_retrieval Fri Dec 14 02:48:56 CET 2007Proc. {TREC 2004}500-261NIST Special PublicationThe Thirteenth {Text} {REtrieval} {Conference} ({TREC 2004})2005inf_retrieval