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This paper presents the automatic extension of TERSEO to other languages, a knowledge-based system for the recognition and normalization of temporal expressions, originally developed for Spanish. TERSEO was extended to English and Italian through the automatic translation of the temporal expressions, and it was presented in previous works (see Saquete et al. (2004a)), but a new methodology has been designed with the purpose of obtaining better results in this issue. This new methodology is based on the use of parallel corpora for extending the TERSEO temporal model to other languages. In this case, two different methods have been tested: (1) automatic translation of TERSEO patterns to other languages and (2) automatic corpora annotation in the target side of parallel corpora. The main idea is focused on annotating the Spanish side of a parallel corpora, projecting the analysis to the second language, and then obtaining new TERSEO patterns (1) and new annotated corpus (2). The set of new patterns will be used to improve the current TERSEO language independent modules. Whereas the new annotated corpus will be used to train a ML system. This system will annotate new temporal expressions in the new language. This work has been suported by the Generalitat Valenciana throught the research grant BFPI06/182 and the project GV06/028 (Tratamiento bilingüe Valenciano-Castellano de preguntas temporales complejas en los sistemas de búsqueda de respuestas), the Spanish Ministery of Science and Technology (project TIN2006-15265-C06-01: TEXT-MESS - Knowledge discovery and Representation in Human Language Technology) and the European Union (project FP6-IST-2005-33860: QALL-ME - Question answering learning technologies in a multilingual and multimodal environment).

Grado en Ingeniería Informática

We present a very simple method for selecting Base Level Concepts using basic structural properties of WordNet. We also empirically demonstrate that these automatically derived set of Base Level Concepts group senses into an adequate level of abstraction in order to perform class-based Word Sense Disambiguation. In fact a very naive Most Frequent classifier using the classes selected is able to perform a semantic tagging with accuracy figures over 75%. Union Europea bajo proyecto QALL-ME (FP6 IST-033860) y el Gobierno Español bajo el proyecto Text-Mess (TIN2006-15265-C06-01) y KNOW (TIN2006-15049-C03-01)

Hospitals attached to the Spanish Ministry of Health are currently using the International Classification of Diseases 9 Clinical Modification (ICD9-CM) to classify health discharge records. Nowadays, this work is manually done by experts. This paper tackles the automatic classification of real Discharge Records in Spanish following the ICD9-CM standard. The challenge is that the Discharge Records are written in spontaneous language. We explore several machine learning techniques to deal with the classification problem. Random Forest resulted in the most competitive one, achieving an F-measure of 0.876. La red de hospitales que configuran el sistema español de sanidad utiliza la Clasificación Internacional de Enfermedades Modificación Clínica (ICD9-CM) para codificar partes de alta hospitalaria. Hoy en día, este trabajo lo realizan a mano los expertos. Este artículo aborda la problemática de clasificar automáticamente partes reales de alta hospitalaria escritos en español teniendo en cuenta el estándar ICD9-CM. El desafío radica en que los partes hospitalarios están escritos con lenguaje espontáneo. Hemos experimentado con varios sistemas de aprendizaje automático para solventar este problema de clasificación. El algoritmo Random Forest es el más competitivo de los probados, obtiene un F-measure de 0.876. This work was partially supported by the European Commission (SEP-210087649), the Spanish Ministry of Science and Innovation (TIN2012-38584-C06-02) and the Industry of the Basque Government (IT344-10).

The KNOW project aims to add meaning, knowledge and reasoning to current natural language processing technologies. El proyecto KNOW pretende añadir significado, conocimiento y razonamiento a las tecnologías actuales de procesamiento del lenguaje natural.