Abstract
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.
The first and second authors were supported by Conacyt scholarships 208265 and 165545 respectively, while the third, fifth and sixth authors were partially supported by SNI, Mexico. This work has been also supported by Conacyt Project Grant 61335.
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Carrillo, M., Villatoro-Tello, E., López-López, A., Eliasmith, C., Villaseñor-Pineda, L., Montes-y-Gómez, M. (2010). Concept Based Representations for Ranking in Geographic Information Retrieval. In: Loftsson, H., Rögnvaldsson, E., Helgadóttir, S. (eds) Advances in Natural Language Processing. NLP 2010. Lecture Notes in Computer Science(), vol 6233. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-14770-8_11
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