A Ranking Approach Based on Example Texts for Geographic Information Retrieval

Villatoro-Tello, Esaú; Montes-y-Gómez, Manuel; Villaseñor-Pineda, Luis

doi:10.1007/978-3-642-04447-2_115

Cited by 6 publications

(14 citation statements)

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“…In all cases, five different retrieval systems were used to retrieve the result lists. In particular, for the GeoCLEF data set, we used some IR systems developed in [20], which differ one from another in the use of different relevance feedback and ranking refinement techniques. For the ImageCLEF data set, the result lists were retrieved using different combinations of visual and textual features [7].…”

Section: Data Sets and Results Listsmentioning

confidence: 99%

Selecting the N-Top Retrieval Result Lists for an Effective Data Fusion

Juárez-González

Montes-y-Gómez

Villaseñor-Pineda

et al. 2010

Computational Linguistics and Intelligent Text Processing

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Abstract.Although the application of data fusion in information retrieval has yielded good results in the majority of the cases, it has been noticed that its achievement is dependent on the quality of the input result lists. In order to tackle this problem, in this paper we explore the combination of only the n-top result lists as an alternative to the fusion of all available data. In particular, we describe a heuristic measure based on redundancy and ranking information to evaluate the quality of each result list, and, consequently, to select the presumably n-best lists per query. Preliminary results in four IR test collections, containing a total of 266 queries, and employing three different DF methods are encouraging. They indicate that the proposed approach could significantly outperform the results achieved by fusion all available lists, showing improvements in mean average precision of 10.7%, 3.7% and 18.8% when it was used along with Maximum RSV, CombMNZ and Fuzzy Borda methods.

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Section: Data Sets and Results Listsmentioning

confidence: 99%

Selecting the N-Top Retrieval Result Lists for an Effective Data Fusion

Juárez-González

Montes-y-Gómez

Villaseñor-Pineda

et al. 2010

Computational Linguistics and Intelligent Text Processing

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“…For our purposes, we will employ the complete documents (called example texts), since by doing this, we have showed (Villatoro-Tello et al 2009a, 2009b that it is possible to make a more accurate approximation of the users' search intention (i.e. For our purposes, we will employ the complete documents (called example texts), since by doing this, we have showed (Villatoro-Tello et al 2009a, 2009b that it is possible to make a more accurate approximation of the users' search intention (i.e.…”

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Document ranking refinement using a Markov random field model

et al. 2012

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Link to this article: http://journals.cambridge.org/abstract_S1351324912000010How to cite this article: ESAÚ VILLATORO, ANTONIO JUÁREZ, MANUEL MONTES, LUIS VILLASEÑOR and L. ENRIQUE SUCAR (2012). Document ranking renement using a Markov random eld model. AbstractThis paper introduces a novel ranking refinement approach based on relevance feedback for the task of document retrieval. We focus on the problem of ranking refinement since recent evaluation results from Information Retrieval (IR) systems indicate that current methods are effective retrieving most of the relevant documents for different sets of queries, but they have severe difficulties to generate a pertinent ranking of them. Motivated by these results, we propose a novel method to re-rank the list of documents returned by an IR system. The proposed method is based on a Markov Random Field (MRF) model that classifies the retrieved documents as relevant or irrelevant. The proposed MRF combines: (i) information provided by the base IR system, (ii) similarities among documents in the retrieved list, and (iii) relevance feedback information. Thus, the problem of ranking refinement is reduced to that of minimising an energy function that represents a trade-off between document relevance and inter-document similarity. Experiments were conducted using resources from four different tasks of the Cross Language Evaluation Forum (CLEF) forum as well as from one task of the Text Retrieval Conference (TREC) forum. The obtained results show the feasibility of the method for re-ranking documents in IR and also depict an improvement in mean average precision compared to a state of the art retrieval machine. * This work was done under partial support of CONACyT project grants: 61335, CB-2008-106013 and CB-2009-134186. 1 Depending on the context, items may refer to text documents, images, audio or video sequences.

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“…Recent evaluation results indicate that there is not a notable advantage of resource-based strategies over methods that do not depend on any geographical resource [11]. Motivated by these results, our method does not depend on the availability of geographical resources, but we contemplate the use of different lists of ranked retrieved documents (VSM, BoC and HRR) looking for improvement of the base ranker efficiency by the combination.…”

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“…Several approaches were focused on solving the ranking problem during these years. Common employed strategies are: a) query expansion through feedback relevance [6], [9], [10]; b) re-ranking retrieved elements through adapted similarity measures [7]; and c) re-ranking through information fusion techniques [9], [10], [11].…”

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“…There is also work focusing on the re-ranking problem; it considers the existence of several lists of retrieved documents from one or more IR engines. For instance, one IR engine can be configured to manage a thematic index (i.e., non geographical terms), while another IR engine is configured to manage only geographical indexes [8], [9], [10], [11], [18]. Therefore, the ranking problem is seen as an information fusion problem; where simple strategies only apply logical operators to the lists (e.g., AND) in order to generate one final re-ranked list [10], while others apply techniques based on information redundancy (e.g., CombMNZ, Round-Robin or Fuzzy Borda ) [8], [10], [11], [18].…”

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Concept Based Representations for Ranking in Geographic Information Retrieval

Carrillo

Villatoro-Tello

Eliasmith

et al. 2010

Advances in Natural Language Processing

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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.

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A Ranking Approach Based on Example Texts for Geographic Information Retrieval

Cited by 6 publications

References 3 publications

Selecting the N-Top Retrieval Result Lists for an Effective Data Fusion

Selecting the N-Top Retrieval Result Lists for an Effective Data Fusion

Document ranking refinement using a Markov random field model

Concept Based Representations for Ranking in Geographic Information Retrieval

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