Advances in Information Retrieval: 38th European Conference by Nicola Ferro, Fabio Crestani, Marie-Francine Moens, Josiane

By Nicola Ferro, Fabio Crestani, Marie-Francine Moens, Josiane Mothe, Fabrizio Silvestri, Giorgio Maria Di Nunzio, Claudia Hauff, Gianmaria Silvello

This booklet constitutes the refereed complaints of the thirty eighth eu convention on IR examine, ECIR 2016, held in Padua, Italy, in March 2016.

The forty two complete papers and 28 poster papers awarded including three keynote talks and six demonstration papers, have been conscientiously reviewed and chosen from 284 submissions. the amount includes the end result of four workshops in addition to four educational papers moreover. Being the top-rated eu discussion board for the presentation of recent examine leads to the sector of data Retrieval, ECIR incorporates a wide selection of themes reminiscent of: social context and information, desktop studying, query answering, score, assessment method, probalistic modeling, review concerns, multimedia and collaborative filtering, and plenty of more.

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Additional resources for Advances in Information Retrieval: 38th European Conference on IR Research, ECIR 2016, Padua, Italy, March 20-23, 2016. Proceedings (Lecture Notes in Computer Science)

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Sumit Bhatia, Debapriyo Majumdar, and Nitish Aggarwal First International Workshop on Recent Trends in News Information Retrieval (NewsIR’16) . . . . . . . . . . . . . . . . . . . . Miguel Martinez-Alvarez, Udo Kruschwitz, Gabriella Kazai, Frank Hopfgartner, David Corney, Ricardo Campos, and Dyaa Albakour 865 869 874 878 Tutorials Collaborative Information Retrieval: Concepts, Models and Evaluation . . . Lynda Tamine and Laure Soulier Group Recommender Systems: State of the Art, Emerging Aspects and Techniques, and Research Challenges .

Nguyen model. 255 vs. 209 on tweet side of ROUGE-1. 111 vs. 074 vs. 069). This shows that out approach is also appropriate for tweet summarization and supports our hypothesis stated in Sect. 1. We discuss some important different points with [17] (using same method ranking and the dataset) due to experimental settings and re-running the experiments. Firstly, [17] uses IDF-modified-cosine similarity, thus the noise of tweets may badly affect to the summarization (same conclusion with [2]). 293, see [17]).

Fahmida Hamid, David Haraburda, and Paul Tarau 351 Multi-document Summarization Based on Atomic Semantic Events and Their Temporal Relationships . . . . . . . . . . . . . . . . Yllias Chali and Mohsin Uddin 366 Tweet Stream Summarization for Online Reputation Management . . . . . Jorge Carrillo-de-Albornoz, Enrique Amigó, Laura Plaza, and Julio Gonzalo 378 Reproducibility Who Wrote the Web? Revisiting Influential Author Identification Research Applicable to Information Retrieval .

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