Miguel Ángel García Cumbreras

BRUJA: question classification for Spanish. Using machine translation and an English classifier

Question Classification is an important task in Question Answering Systems. This paper presents a Spanish Question Classifier based on machine learning, automatic online translators and different language features. Our system works with English collections and bilingual questions (English/Spanish). We have tested two Spanish-English online translators to identify the lost of precision. We have made experiments using lexical, syntactic and semantic features to test which ones made a better performance. The obtained results show that our system makes good classifications, over a 80% in terms of accuracy using the original English questions and over a 65% using Spanish questions and machine translation systems. Our conclusion about the features is that a lexical, syntactic and semantic features combination obtains the best result.

The Merging Problem in Distributed Information Retrieval and the 2-Step RSV Merging Algorithm

This paper presents the application of the 2-step RSV (retrieval status value) merging algorithm to DIR environments. The reported experiment shows that the proposed algorithm is scalable and stable. In addition, it obtains a precision measure higher than the well known CORI algorithm.

Knowledge Integration using Textual Information for Improving ImageCLEF Collections

In this chapter we explain our participation at ImageCLEF from 2005 to 2009. During these years we have mainly developed systems for the ad hoc and the medical retrieval tasks. Although the different proposed tasks include both visual and textual information, the diverse approaches applied by the participants also include the use of only one type of information. The SINAI group specializes in the management of textual collections. For this reason, our main goal has been to improve the general system by taking advantage of the textual information.

The democratization of deep learning in TASS 2017

This research work is partially supported by REDES project (TIN2015-65136-C2-1-R) and SMART project (TIN2017-89517-P) from the Spanish Government, and a grant from the Fondo Europeo de Desarrollo Regional (FEDER). Eugenio Martinez Camara was supported by the Juan de la Cierva Formacion Programme (FJCI-2016-28353) from the Spanish Government.

Pictogrammar: an AAC device based on a semantic grammar.

As many as two-thirds of individuals with an Autism Spectrum Disorder (ASD) also have language impairments, which can range from mild limitations to complete non-verbal behavior. For such cases, there are several Augmentative and Alternative Communication (AAC) devices available. These are computer-designed tools in order to help people with ASD to palliate or overcome such limitations, at least partially. Some of the most popular AAC devices are based on pictograms, so that a pictogram is the graphical representation of a simple concept and sentences are composed by concatenating a number of such pictograms. Usually, these tools have to manage a vocabulary made up of hundreds of pictograms/concepts, with no or very poor knowledge of the language at semantic and pragmatic level. In this paper we present Pictogrammar, an AAC system which takes advantage of SUpO and PictOntology. SUpO (Simple Upper Ontology) is a formal semantic ontology which is made up of detailed knowledge of facts of everyday life such as simple words, with special interest in linguistic issues, allowing automated grammatical supervision. PictOntology is an ontology developed to manage sets of pictograms, linked to SUpO. Both ontologies make possible the development of tools which are able to take advantage of a formal semantics.