Evaluating knowledge-based approaches to the multilingual extension of a temporal expression normalizer

The extension to new languages is a well known bottleneck for rule-based systems. Considerable human effort, which typically consists in re-writing from scratch huge amounts of rules, is in fact required to transfer the knowledge available to the system from one language to a new one. Provided sufficient annotated data, machine learning algorithms allow to minimize the costs of such knowledge transfer but, up to date, proved to be ineffective for some specific tasks. Among these, the recognition and normalization of temporal expressions still remains out of their reach. Focusing on this task, and still adhering to the rule-based framework, this paper presents a bunch of experiments on the automatic porting to Italian of a system originally developed for Spanish. Different automatic rule translation strategies are evaluated and discussed, providing a comprehensive overview of the challenge.

Una nueva técnica de construcción de grafos semánticos para la desambiguación bilingüe del sentido de las palabras

En este trabajo presentamos unos resultados preliminares obtenidos mediante la aplicación de una nueva técnica de construcción de grafos semánticos a la tarea de desambiguación del sentido de las palabras en un entorno multilingüe. Gracias al uso de esta técnica no supervisada, inducimos los sentidos asociados a las traducciones de la palabra ambigua considerada en la lengua destino. Utilizamos las traducciones de las palabras del contexto de la palabra ambigua en la lengua origen para seleccionar el sentido más probable de la traducción. El sistema ha sido evaluado sobre la colección de datos de una tarea de desambiguación multilingüe que se propuso en la competición SemEval-2010, consiguiendo superar los resultados de todos los sistemas no supervisados que participaron en aquella tarea.

Inconsistencies in Guidelines for Visual Health Surveillance of VDT Workers

Objectives: In Europe, 25% of workers use video display terminals (VDTs). Occupational health surveillance has been considered a key element in the protection of these workers. Nevertheless, it is unclear if guidelines available for this purpose, based on EU standards and available evidence, meet currently accepted quality criteria. The aim of this study was to appraise three sets of European VDT guidelines (UK, France, Spain) in which regulatory and evidence-based approaches for visual health have been formulated and recommendations for practice made. Methods: Three independent appraisers used an adapted AGREE instrument with seven domains to appraise the guidelines. A modified nominal group technique approach was used in two consecutive phases: first, individual evaluation of the three guidelines simultaneously, and second, a face-to-face meeting of appraisers to discuss scoring. Analysis of ratings obtained in each domain and variability among appraisers was undertaken (correlation and kappa coefficients). Results: All guidelines had low domain scores. The domain evaluated most highly was Scope and purpose, while Applicability was scored minimally. The UK guidelines had the highest overall score, and the Spanish ones had the lowest. The analysis of reliability and differences between scores in each domain showed a high level of agreement. Conclusions: These results suggest current guidelines used in these countries need an update. The formulation of evidence-base European guidelines on VDT could help to reduce the significant variation of national guidelines, which may have an impact on practical application.

Tackling redundancy in text summarization through different levels of language analysis

One of the main challenges to be addressed in text summarization concerns the detection of redundant information. This paper presents a detailed analysis of three methods for achieving such goal. The proposed methods rely on different levels of language analysis: lexical, syntactic and semantic. Moreover, they are also analyzed for detecting relevance in texts. The results show that semantic-based methods are able to detect up to 90% of redundancy, compared to only the 19% of lexical-based ones. This is also reflected in the quality of the generated summaries, obtaining better summaries when employing syntactic- or semantic-based approaches to remove redundancy.