A proposal of automatic selection of coarse-grained semantic classes for WSD

Presentamos un método muy simple para seleccionar conceptos base (Base Level Concepts) usando algunas propiedades estructurales básicas de WordNet. Demostramos empíricamente que el conjunto de Base Level Concepts obtenido agrupa sentidos de palabras en un nivel de abstracción adecuado para la desambiguación del sentido de las palabras basada en clases. De hecho, un sencillo clasificador basado en el sentido más frecuente usando las clases generadas, es capaz de alcanzar un acierto próximo a 75% para la tarea de etiquetado semántico.We present a very simple method for selecting Base Level Concepts using some 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%.This paper has been supported by the European Union under the project QALL-ME (FP6 IST-033860) and the Spanish Government under the project Text-Mess (TIN2006-15265-C06-01) and KNOW (TIN2006-15049-C03-01

A semantic framework for textual data enrichment

In this work we present a semantic framework suitable of being used as support tool for recommender systems. Our purpose is to use the semantic information provided by a set of integrated resources to enrich texts by conducting different NLP tasks: WSD, domain classification, semantic similarities and sentiment analysis. After obtaining the textual semantic enrichment we would be able to recommend similar content or even to rate texts according to different dimensions. First of all, we describe the main characteristics of the semantic integrated resources with an exhaustive evaluation. Next, we demonstrate the usefulness of our resource in different NLP tasks and campaigns. Moreover, we present a combination of different NLP approaches that provide enough knowledge for being used as support tool for recommender systems. Finally, we illustrate a case of study with information related to movies and TV series to demonstrate that our framework works properly.This research work has been partially funded by the University of Alicante, Generalitat Valenciana, Spanish Government and the European Commission through the Projects, TIN2015-65136-C2-2- R, TIN2015-65100-R, SAM (FP7-611312), and PROMETEOII/2014/001