Machine Learning
Conditional Random Fields: Probabilistic Models for Segmenting and Labeling Sequence Data
ICML '01 Proceedings of the Eighteenth International Conference on Machine Learning
An Information-Theoretic Definition of Similarity
ICML '98 Proceedings of the Fifteenth International Conference on Machine Learning
Automatic acquisition of hyponyms from large text corpora
COLING '92 Proceedings of the 14th conference on Computational linguistics - Volume 2
HLT '91 Proceedings of the workshop on Speech and Natural Language
Measuring semantic similarity in the taxonomy of WordNet
ACSC '05 Proceedings of the Twenty-eighth Australasian conference on Computer Science - Volume 38
Grounding spatial named entities for information extraction and question answering
HLT-NAACL-GEOREF '03 Proceedings of the HLT-NAACL 2003 workshop on Analysis of geographic references - Volume 1
A confidence-based framework for disambiguating geographic terms
HLT-NAACL-GEOREF '03 Proceedings of the HLT-NAACL 2003 workshop on Analysis of geographic references - Volume 1
Biomedical named entity recognition using conditional random fields and rich feature sets
JNLPBA '04 Proceedings of the International Joint Workshop on Natural Language Processing in Biomedicine and its Applications
Using information content to evaluate semantic similarity in a taxonomy
IJCAI'95 Proceedings of the 14th international joint conference on Artificial intelligence - Volume 1
The University of Lisbon at CLEF 2006 ad-hoc task
CLEF'06 Proceedings of the 7th international conference on Cross-Language Evaluation Forum: evaluation of multilingual and multi-modal information retrieval
Cross-lingual geo-parsing for non-structured data
Proceedings of the 7th Workshop on Geographic Information Retrieval
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Geographic Information Retrieval (GIR) systems rely on the identification and disambiguation of place names in documents to determine the region about which they are relevant. The place names are mapped into geographic concepts and used to assign an encompassing concept (a scope) to each document. However, sometimes a single scope is too restrictive and insufficient for capturing the geographic semantics of a document. We propose as an alternative to abstract the geographic semantics of a document as a geographic signature, which is a list of maximally disambiguated geographic references found in a document. A signature can be used in multiple GIR applications, such as in building a geographic index for a document collection. We perform the disambiguation of the possible geographic meanings using semantic similarity measures.