A vector space model for automatic indexing
Communications of the ACM
Holographic Reduced Representation: Distributed Representation for Cognitive Structures
Holographic Reduced Representation: Distributed Representation for Cognitive Structures
Noun-phrase analysis in unrestricted text for information retrieval
ACL '96 Proceedings of the 34th annual meeting on Association for Computational Linguistics
Distributional term representations: an experimental comparison
Proceedings of the thirteenth ACM international conference on Information and knowledge management
Automatic bilingual lexicon acquisition using random indexing of parallel corpora
Natural Language Engineering
Using bag-of-concepts to improve the performance of support vector machines in text categorization
COLING '04 Proceedings of the 20th international conference on Computational Linguistics
Characteristics of geographic information needs
Proceedings of the 4th ACM workshop on Geographical information retrieval
Integrating structure and meaning: a new method for encoding structure for text classification
ECIR'08 Proceedings of the IR research, 30th European conference on Advances in information retrieval
Cheshire at GeoCLEF 2008: text and fusion approaches for GIR
CLEF'08 Proceedings of the 9th Cross-language evaluation forum conference on Evaluating systems for multilingual and multimodal information access
Ontology-based query construction for GeoCLEF
CLEF'08 Proceedings of the 9th Cross-language evaluation forum conference on Evaluating systems for multilingual and multimodal information access
The University of Lisbon at GeoCLEF 2006
CLEF'06 Proceedings of the 7th international conference on Cross-Language Evaluation Forum: evaluation of multilingual and multi-modal information retrieval
Relevance and ranking in geographic information retrieval
FDIA'11 Proceedings of the Fourth BCS-IRSG conference on Future Directions in Information Access
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Geographic Information Retrieval (GIR) is a specialized Information Retrieval (IR) branch that deals with information related to geographical locations. Traditional IR engines are perfectly able to retrieve the majority of the relevant documents for most geographical queries, but they have severe difficulties generating a pertinent ranking of the retrieved results, which leads to poor performance. A key reason for this ranking problem has been a lack of information. Therefore, previous GIR research has tried to fill this gap using robust geographical resources (i.e. a geographical ontology), while other research with the same aim has used relevant feedback techniques instead. This paper explores the use of Bag of Concepts (BoC; a representation where documents are considered as the union of the meanings of its terms) and Holographic Reduced Representation (HRR; a novel representation for textual structure) as re-ranking mechanisms for GIR. Our results reveal an improvement in mean average precision (MAP) when compared to the traditional vector space model, even if Pseudo Relevance Feedback is employed.