This paper describes our contribution to the English Entrance Exams task of CLEF 2015, which requires participating systems to automatically solve multiple choice reading comprehension tasks. We use a combination of text segmentation and different similarity measures with the aim of exploiting two observed aspects of tests: 1) the often linear relationship between reading text and test question...

This is an overview of the NTCIR-9 INTENT task, which comprises the Subtopic Mining and the Document Ranking subtasks. The INTENT task attracted participating teams from seven different countries/regions – 16 teams for Subtopic Mining and 8 teams for Document Ranking. The Subtopic Mining subtask received 42 Chinese runs and 14 Japanese runs; the Document Ranking subtask received 24 Chinese runs...

In this paper, we address the problem of ranking clinical documents using centrality based approach. We model the documents to be ranked as nodes in a graph and place edges between documents based on their similarity. Given a query, we compute similarity of the query with respect to every document in the graph. Based on these similarity values, documents are ranked for a given query. Initially,...

Due to the opportunities provided by the Internet, people are taking advantage of e-learning courses and during the last few years enormous research efforts have been dedicated to the development of e-learning systems. So far, many e-learning systems are proposed and used practically. However, in these systems the e-learning completion rate is low. One of the reasons is the low study desire and...

The bottleneck of event recommender systems is the availability of actual, up-to-date information on events. Usually, there is no single data feed, thus information on events must be crawled from numerous sources. Ranking these sources helps the system to decide which sources to crawl and how often. In this paper, a model for event source evaluation and ranking is proposed based on well-known c...

UNLABELLED We present a large-scale implementation of the Rankprop protein homology ranking algorithm in the form of an openly accessible web server. We use the NRDB40 PSI-BLAST all-versus-all protein similarity network of 1.1 million proteins to construct the graph for the Rankprop algorithm, whereas previously, results were only reported for a database of 108 000 proteins. We also describe tw...

In this paper we present the system for Answer Selection and Ranking in Community Question Answering, which we build as part of our participation in SemEval2017 Task 3. We develop a Support Vector Machine (SVM) based system that makes use of textual, domain-specific, wordembedding and topic-modeling features. In addition, we propose a novel method for dialogue chain identification in comment th...

Recent research and applications for evaluation and quality estimation of Machine Translation require statistical measures for comparing machine-predicted ranking against gold sets annotated by humans. Additional to the existing practice of measuring segment-level correlation with Kendall tau, we propose using ranking metrics from the research field of Information Retrieval such as Mean Recipro...

This working paper reviews some different ideas in link-based analysis for search. First, results about static ranking of web pages based on the so called randomsurfer model are reviewed and presented in a unified framework. Second, a topic-based hubs and authorities model using a discrete component method (a variant of ICA and PCA) is developed, and illustrated on the 500,000 page English lang...

Wikipedia is a useful source of knowledge that has many applications in language processing and knowledge representation. The Wikipedia category graph can be compared with the class hierarchy in an ontology; it has some characteristics in common as well as some differences. In this paper, we present our approach for answering entity ranking queries from the Wikipedia. In particular, we explore ...