We address the issue of providing topic driven access to full text documents. The methodology we propose is a combination of topic segmentation and information retrieval techniques...
Caterina Caracciolo, Willem Robert van Hage, Maart...
Abstract. We present a linguistically-motivated sub-sentential alignment system that extends the intersected IBM Model 4 word alignments. The alignment system is chunk-driven and r...
In this paper, we propose a Bayesian learning approach to promoting diversity for information retrieval in biomedicine and a re-ranking model to improve retrieval performance in t...
Sheffield's participation in the inaugural Arabic cross language track is described here. Our goal was to examine how well one could achieve retrieval of Arabic text with the...
In this paper we tackle the problem of document image retrieval by combining a similarity measure between documents and the probability that a given document belongs to a certain ...
Albert Gordo, Jaume Gibert, Ernest Valveny, Mar&cc...