An evaluation resource for geographic information retrieval

In this paper we present an evaluation resource for geographic information retrieval developed within the Cross Language Evaluation Forum (CLEF). The GeoCLEF track is dedicated to the evaluation of geographic information retrieval systems. The resource encompasses more than 600,000 documents, 75 topics so far, and more than 100,000 relevance judgments for these topics. Geographic information retrieval requires an evaluation resource which represents realistic information needs and which is geographically challenging. Some experimental results and analysis are reported

GeoCLEF 2007: the CLEF 2007 cross-language geographic information retrieval track overview

GeoCLEF ran as a regular track for the second time within the Cross Language Evaluation Forum (CLEF) 2007. The purpose of GeoCLEF is to test and evaluate cross-language geographic information retrieval (GIR): retrieval for topics with a geographic specification. GeoCLEF 2007 consisted of two sub tasks. A search task ran for the third time and a query classification task was organized for the first. For the GeoCLEF 2007 search task, twenty-five search topics were defined by the organizing groups for searching English, German, Portuguese and Spanish document collections. All topics were translated into English, Indonesian, Portuguese, Spanish and German. Several topics in 2007 were geographically challenging. Thirteen groups submitted 108 runs. The groups used a variety of approaches. For the classification task, a query log from a search engine was provided and the groups needed to identify the queries with a geographic scope and the geographic components within the local queries

DCU@TRECMed 2012: Using ad-hoc baselines for domain-specific retrieval

This paper describes the first participation of DCU in the TREC Medical Records Track (TRECMed). We performed some initial experiments on the 2011 TRECMed data based on the BM25 retrieval model. Surprisingly, we found that the standard BM25 model with default parameters, performs comparable to the best automatic runs submitted to TRECMed 2011 and would have resulted in rank four out of 29 participating groups. We expected that some form of domain adaptation would increase performance. However, results on the 2011 data proved otherwise: concept-based query expansion decreased performance, and filtering and reranking by term proximity also decreased performance slightly. We submitted four runs based on the BM25 retrieval model to TRECMed 2012 using standard BM25, standard query expansion, result filtering, and concept-based query expansion. Official results for 2012 confirm that domain-specific knowledge does not increase performance compared to the BM25 baseline as applied by us

Challenges to evaluation of multilingual geographic information retrieval in GeoCLEF

This is the third year of the evaluation of geographic information retrieval (GeoCLEF) within the Cross-Language Evaluation Forum (CLEF). GeoCLEF 2006 presented topics and documents in four languages (English, German, Portuguese and Spanish). After two years of evaluation we are beginning to understand the challenges to both Geographic Information Retrieval from text and of evaluation of the results of geographic information retrieval. This poster enumerates some of these challenges to evaluation and comments on the limitations encountered in the first two evaluations

A Reinforcement Learning-driven Translation Model for Search-Oriented Conversational Systems

Search-oriented conversational systems rely on information needs expressed in natural language (NL). We focus here on the understanding of NL expressions for building keyword-based queries. We propose a reinforcement-learning-driven translation model framework able to 1) learn the translation from NL expressions to queries in a supervised way, and, 2) to overcome the lack of large-scale dataset by framing the translation model as a word selection approach and injecting relevance feedback in the learning process. Experiments are carried out on two TREC datasets and outline the effectiveness of our approach.Comment: This is the author's pre-print version of the work. It is posted here for your personal use, not for redistribution. Please cite the definitive version which will be published in Proceedings of the 2018 EMNLP Workshop SCAI: The 2nd International Workshop on Search-Oriented Conversational AI - ISBN: 978-1-948087-75-

On the probabilistic logical modelling of quantum and geometrically-inspired IR

Information Retrieval approaches can mostly be classed into probabilistic, geometric or logic-based. Recently, a new unifying framework for IR has emerged that integrates a probabilistic description within a geometric framework, namely vectors in Hilbert spaces. The geometric model leads naturally to a predicate logic over linear subspaces, also known as quantum logic. In this paper we show the relation between this model and classic concepts such as the Generalised Vector Space Model, highlighting similarities and differences. We also show how some fundamental components of quantum-based IR can be modelled in a descriptive way using a well-established tool, i.e. Probabilistic Datalog