215 research outputs found

    Evaluation of information retrieval systems using structural equation modeling

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    The interpretation of the experimental data collected by testing systems across input datasets and model parameters is of strategic importance for system design and implementation. In particular, finding relationships between variables and detecting the latent variables affecting retrieval performance can provide designers, engineers and experimenters with useful if not necessary information about how a system is performing. This paper discusses the use of Structural Equation Modeling (SEM) in providing an in-depth explanation of evaluation results and an explanation of failures and successes of a system; in particular, we focus on the case of evaluation of Information Retrieval systems

    Using Learning to Rank Approach to Promoting Diversity for Biomedical Information Retrieval with Wikipedia

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    In most of the traditional information retrieval (IR) models, the independent relevance assumption is taken, which assumes the relevance of a document is independent of other documents. However, the pitfall of this is the high redundancy and low diversity of retrieval result. This has been seen in many scenarios, especially in biomedical IR, where the information need of one query may refer to different aspects. Promoting diversity in IR takes the relationship between documents into account. Unlike previous studies, we tackle this problem in the learning to rank perspective. The main challenges are how to find salient features for biomedical data and how to integrate dynamic features into the ranking model. To address these challenges, Wikipedia is used to detect topics of documents for generating diversity biased features. A combined model is proposed and studied to learn a diversified ranking result. Experiment results show the proposed method outperforms baseline models

    Automatic text summarization using pathfinder network scaling

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    Contém uma errataTese de Mestrado. Inteligência Artificial e Sistemas Inteligentes. Faculdade de Engenharia. Universidade do Porto, Faculdade de Economia. Universidade do Porto. 200

    ExpFinder: An Ensemble Expert Finding Model Integrating NN-gram Vector Space Model and μ\muCO-HITS

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    Finding an expert plays a crucial role in driving successful collaborations and speeding up high-quality research development and innovations. However, the rapid growth of scientific publications and digital expertise data makes identifying the right experts a challenging problem. Existing approaches for finding experts given a topic can be categorised into information retrieval techniques based on vector space models, document language models, and graph-based models. In this paper, we propose ExpFinder\textit{ExpFinder}, a new ensemble model for expert finding, that integrates a novel NN-gram vector space model, denoted as nnVSM, and a graph-based model, denoted as \textit{\muCO-HITS}, that is a proposed variation of the CO-HITS algorithm. The key of nnVSM is to exploit recent inverse document frequency weighting method for NN-gram words and ExpFinder\textit{ExpFinder} incorporates nnVSM into \textit{\muCO-HITS} to achieve expert finding. We comprehensively evaluate ExpFinder\textit{ExpFinder} on four different datasets from the academic domains in comparison with six different expert finding models. The evaluation results show that ExpFinder\textit{ExpFinder} is a highly effective model for expert finding, substantially outperforming all the compared models in 19% to 160.2%.Comment: 15 pages, 18 figures, "for source code on Github, see https://github.com/Yongbinkang/ExpFinder", "Submitted to IEEE Transactions on Knowledge and Data Engineering
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