Having being proposed for the fourth time, the QA at CLEF track has confirmed a still raising interest from the research community, recording a constant increase both in the numbe...
QRISTAL [9] is a question answering system making intensive use of natural language processing both for indexing documents and extracting answers. It ranked first in the EQueR eva...
In this paper we will describe the Berkeley approaches to the GeoCLEF tasks for CLEF 2006. This year we used two separate systems for different tasks. Although of the systems both...
Abstract. We promote the use of explicit medical knowledge to solve retrieval of information both visual and textual. For text, this knowledge is a set of concepts from a Meta-thes...
This paper summarizes the task design for iCLEF 2006 (the CLEF interactive track). Compared to previous years, we have proposed a radically new task: searching images in a natural...
This paper describes a QA system centered in a full data-driven architecture. It applies machine learning and text mining techniques to identify the most probable answers to factoi...
This paper presents the results of our initial experiments in the monolingual English, Spanish and Portuguese tasks and the Bilingual Spanish English, Spanish Portuguese, Englis...
This paper presents our bilingual question-answering system MUSCLEF. We underline the difficulties encountered when shifting from a mono to a cross-lingual system, then we focus o...
Abstract. The paper features MAVE, a knowledge-based system for answer validation through deep linguistic processing and logical inference. A relaxation loop is used to determine a...