Spatial information retrieval and geographical ontologies an overview of the SPIRIT project
SIGIR '02 Proceedings of the 25th annual international ACM SIGIR conference on Research and development in information retrieval
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
Using semantic networks for geographic information retrieval
CLEF'05 Proceedings of the 6th international conference on Cross-Language Evalution Forum: accessing Multilingual Information Repositories
Challenges for indexing in GIR
SIGSPATIAL Special
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For the GeoCLEF task of the CLEF campaign 2006, we investigate identifying literal (geographic) and metonymic senses of location names (the location name refers to another, related entity) and indexing them differently. In document preprocessing, location name senses are identified with a classifier relying on shallow features only. Different senses are stored in corresponding document fields, i. e. LOC (all senses), LOCLIT (literal senses), and LOCMET (metonymic senses). The classifier was trained on manually annotated data from German CoNLL- 2003 data and from a subset of the GeoCLEF newspaper corpus. The setup of our GIR (geographic information retrieval) system is a variant of our setup for GeoCLEF 2005. Results of the retrieval experiments indicate that excluding metonymic senses of location names (short: metonymic location names) improves mean average precision (MAP). Furthermore, using topic narratives decreases MAP, and query expansion with meronyms improves the performance of GIR in our experiments.