Titlebook: Information Access Evaluation. Multilinguality, Multimodality, and Visualization; 4th International Co Pamela Forner,Henning Müller,Benno S

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Improving Ranking Evaluation Employing Visual Analyticssually and analytically. This paper provides an analytical model for examining rankings produced by IR systems, based on the discounted cumulative gain family of metrics, and visualization for performing failure and “what-if” analyses.

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A Proposal for New Evaluation Metrics and Result Visualization Technique for Sentiment Analysis Taskon Analysis tasks. We provide a theoretical justification for their use and discuss how they complement other accuracy-based metrics. We apply the proposed techniques to the analysis of TASS-SEPLN and RepLab 2012 results and show how the metric is effective for system comparison purposes, for system

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A Dependency-Inspired Semantic Evaluation of Machine Translation Systems terms in MT output with the human translated reference rather than measuring the similarity in meaning. In this paper, we developed an MT evaluation metric to assess the output of MT systems, semantically. Inspiring by the dependency grammar, we consider to what extent the headword and its dependen

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A Turing Test to Evaluate a Complex Summarization Task on sentence compression requires to asses informativeness and modify inner sentence structures. This is much more intrinsically related with real rephrasing than plain sentence extraction and ranking paradigm so new evaluation methods are needed. We propose a novel imitation game to evaluate Automa

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A Formative Evaluation of a Comprehensive Search System for Medical Professionalsn of a comprehensive search system for medical professionals. The study was designed to guide system development. The system features included search in text and 2D images, machine translated summaries of search results, as well as query disambiguation and suggestion features, and a comprehensive se

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ALQASIM: Arabic Language Question Answer Selection in Machines framework of the main task of QA4MRE @ CLEF 2013. ALQASIM uses a novel technique by analyzing the reading test documents instead of the questions, which leads to a promising performance of 0.31 accuracy and 0.36 C@1, without using the test-set background collections.

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