By Nicola Ferro, Fabio Crestani, Marie-Francine Moens, Josiane Mothe, Fabrizio Silvestri, Giorgio Maria Di Nunzio, Claudia Hauff, Gianmaria Silvello
This ebook constitutes the refereed complaints of the thirty eighth eu convention on IR study, ECIR 2016, held in Padua, Italy, in March 2016.
The forty two complete papers and 28 poster papers provided 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 instructional papers moreover. Being the most effective eu discussion board for the presentation of recent learn ends up in the sphere of knowledge Retrieval, ECIR encompasses a wide variety of subject matters reminiscent of: social context and information, desktop studying, query answering, score, overview technique, probalistic modeling, assessment concerns, multimedia and collaborative filtering, and lots of more.
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Extra resources for Advances in Information Retrieval: 38th European Conference on IR Research, ECIR 2016, Padua, Italy, March 20-23, 2016. Proceedings
16,17] (using ranking) as well as the dataset. However, representing sentence-tweet by RTE and ranking to generate summaries are two key diﬀerences in comparison to , which used RankBoost, another supervised method. In addition, our method calculates inter-wing/dual-wing similarity with a set of RTE features instead of IDF-modiﬁed-cosine similarity in comparison to . 2 Feature Extraction To detect the entailment, a straightforward method is to deﬁne a set of features for representing sentence-tweet relation.
5 – a value we obtained empirically. The negative instances are created by pairing randomly selected comments from two diﬀerent articles from The Guardian. They are used to present the linear regression algorithm with the instances of comment pairs that are not on the same topic or are only weakly topically related. The topical similarity measure for each such pair was set to 0. We have in total 14,700 positive pairs and the same number of negative instances. 2 Testing Data For testing, clusters generated by human annotators are used as a gold standard data set.
4). m score(tj ) = δ ∗ n rteScore(tj , tk ) + (1 − δ) ∗ k=1 rteScore(tj , si ) (4) i=1 where δ is the damping factor; n and m are the number of sentences and tweets. Ranking: Important sentences and representative tweets were found by selecting the highest score of vertices in the DWEG. The selection is denoted in Eq. (5). (5) Sr ← ranking(Sn ); Tr ← ranking(Tm ) where ranking() returns a list of instances in a decreased weight order; top-K instances would be selected as the summaries from the Sr and Tr .