Instance-Based Learning Algorithms
Machine Learning
Understanding metonymies in discourse
Artificial Intelligence
A statistical approach to the processing of metonymy
COLING '00 Proceedings of the 18th conference on Computational linguistics - Volume 2
Metonymy resolution as a classification task
EMNLP '02 Proceedings of the ACL-02 conference on Empirical methods in natural language processing - Volume 10
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
Example-based metonymy recognition for proper nouns
EACL '06 Proceedings of the Eleventh Conference of the European Chapter of the Association for Computational Linguistics: Student Research Workshop
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
Using semantic networks for geographic information retrieval
CLEF'05 Proceedings of the 6th international conference on Cross-Language Evalution Forum: accessing Multilingual Information Repositories
Proceedings of the 6th Workshop on Geographic Information Retrieval
Integrating methods from IR and QA for geographic information retrieval
CLEF'08 Proceedings of the 9th Cross-language evaluation forum conference on Evaluating systems for multilingual and multimodal information access
Multifaceted toponym recognition for streaming news
Proceedings of the 34th international ACM SIGIR conference on Research and development in Information Retrieval
Local and global context for supervised and unsupervised metonymy resolution
EMNLP-CoNLL '12 Proceedings of the 2012 Joint Conference on Empirical Methods in Natural Language Processing and Computational Natural Language Learning
An algorithm for local geoparsing of microtext
Geoinformatica
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Metonymically used location names (toponyms) refer to other, related entities and thus possess a meaning different from their literal, geographic sense. Metonymic uses are to be treated differently to improve the performance of geographic information retrieval (GIR). Statistics on toponym senses show that 75.06% of all location names are used in their literal sense, 17.05% are used metonymically, and 7.89% have a mixed sense. This article presents a method for disambiguating location names in texts between literal and metonymic senses, based on shallow features. The evaluation of this method is two-fold. First, we use a memory-based learner (TiMBL) to train a classifier and determine standard evaluation measures such as F-score and accuracy. The classifier achieved an F-score of 0.842 and an accuracy of 0.846 for identifying toponym senses in a subset of the CoNLL (Conference on Natural Language Learning) data. Second, we perform retrieval experiments based on the GeoCLEF data (newspaper article corpus and queries) from 2005 and 2006. We compare searching location names in a database index containing both their literal and metonymic senses with searching in an index containing their literal senses only. Evaluation results indicate that removing metonymic senses from the index yields a higher mean average precision (MAP) for GIR. In total, we observed a significant gain in MAP: an increase from 0.0704 to 0.0715 MAP for the GeoCLEF 2005 data, and an increase from 0.1944 to 0.2100 MAP for the GeoCLEF 2006 data.