Head-driven statistical models for natural language parsing
Head-driven statistical models for natural language parsing
TnT: a statistical part-of-speech tagger
ANLC '00 Proceedings of the sixth conference on Applied natural language processing
Introduction to the CoNLL-2003 shared task: language-independent named entity recognition
CONLL '03 Proceedings of the seventh conference on Natural language learning at HLT-NAACL 2003 - Volume 4
Experiments with geographic knowledge for information extraction
HLT-NAACL-GEOREF '03 Proceedings of the HLT-NAACL 2003 workshop on Analysis of geographic references - Volume 1
GeoCLEF: the CLEF 2005 cross-language geographic information retrieval track overview
CLEF'05 Proceedings of the 6th international conference on Cross-Language Evalution Forum: accessing Multilingual Information Repositories
CLEF'04 Proceedings of the 5th conference on Cross-Language Evaluation Forum: multilingual Information Access for Text, Speech and Images
A standard language for service delivery: Enabling understanding among stakeholders
Computer Standards & Interfaces
CLEF'06 Proceedings of the 7th international conference on Cross-Language Evaluation Forum: evaluation of multilingual and multi-modal information retrieval
TALP at GeoCLEF 2006: experiments Using JIRS and Lucene with the ADL feature type thesaurus
CLEF'06 Proceedings of the 7th international conference on Cross-Language Evaluation Forum: evaluation of multilingual and multi-modal information retrieval
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This paper describes GeoTALP-IR system, a Geographical Information Retrieval (GIR) system. The system is described and evaluated in the context of our participation in the CLEF 2005 GeoCLEF Monolingual English task. The GIR system is based on Lucene and uses a modified version of the Passage Retrieval module of the TALP Question Answering (QA) system presented at CLEF 2004 and TREC 2004 QA evaluation tasks. We designed a Keyword Selection algorithm based on a Linguistic and Geographical Analysis of the topics. A Geographical Thesaurus (GT) has been built using a set of publicly available Geographical Gazetteers and a Geographical Ontology. Our experiments show that the use of a Geographical Thesaurus for Geographical Indexing and Retrieval has improved the performance of our GIR system.