Enriching the integration of semantic resources based on WordNet

In this paper we present the enrichment of the Integration of Semantic Resources based in WordNet (ISR-WN Enriched). This new proposal improves the previous one where several semantic resources such as SUMO, WordNet Domains and WordNet Affects were related, adding other semantic resources such as Semantic Classes and SentiWordNet. Firstly, the paper describes the architecture of this proposal explaining the particularities of each integrated resource. After that, we analyze some problems related to the mappings of different versions and how we solve them. Moreover, we show the advantages that this kind of tool can provide to different applications of Natural Language Processing. Related to that question, we can demonstrate that the integration of semantic resources allows acquiring a multidimensional vision in the analysis of natural language.

An approach based on Logic Forms and WordNet relationships to Textual Entailment performance

This paper outlines the approach adopted by the PLSI research group at University of Alicante in the PASCAL-2006 second Recognising Textual Entailment challenge. Our system is composed of several components. On the one hand, the first component performs the derivation of the logic forms of the text/hypothesis pairs and, on the other hand, the second component provides us with a similarity score given by the semantic relations between the derived logic forms. In order to obtain this score we apply several measures of similitude and relatedness based on the structure and content of WordNet.

Using WordNet relations and semantic classes in information retrieval tasks

In this paper we explore the use of semantic classes in an existing information retrieval system in order to improve its results. Thus, we use two different ontologies of semantic classes (WordNet domain and Basic Level Concepts) in order to re-rank the retrieved documents and obtain better recall and precision. Finally, we implement a new method for weighting the expanded terms taking into account the weights of the original query terms and their relations in WordNet with respect to the new ones (which have demonstrated to improve the results). The evaluation of these approaches was carried out in the CLEF Robust-WSD Task, obtaining an improvement of 1.8% in GMAP for the semantic classes approach and 10% in MAP employing the WordNet term weighting approach.

ISR-WN: Integración de recursos semánticos basados en WordNet

La presente herramienta informática constituye un software que es capaz concebir una red semántica con los siguientes recursos: WordNet versión 1.6 y 2.0, WordNet Affects versión 1.0 y 1.1, WordNet Domain versión 2.0, SUMO, Semantic Classes y Senti WordNet versión 3.0, todos integrados y relacionados en una única base de conocimiento. Utilizando estos recursos, ISR-WN cuenta con funcionalidades añadidas que permiten la exploración de dicha red de un modo simple aplicando funciones tanto como de recorrido como de búsquedas textuales. Mediante la interrogación de dicha red semántica es posible obtener información para enriquecer textos, como puede ser obtener las definiciones de aquellas palabras que son de uso común en determinados Dominios en general, dominios emocionales, y otras conceptualizaciones, además de conocer de un determinado sentido de una palabra su valoración proporcionada por el recurso SentiWordnet de positividad, negatividad y objetividad sentimental. Toda esta información puede ser utilizada en tareas de procesamiento del lenguaje natural como: • Desambiguación del Sentido de las Palabras, • Detección de la Polaridad Sentimental • Análisis Semántico y Léxico para la obtención de conceptos relevantes en una frase según el tipo de recurso implicado. Esta herramienta tiene como base el idioma inglés y se encuentra disponible como una aplicación de Windows la cual dispone de un archivo de instalación el cual despliega en el ordenador de residencia las librerías necesarias para su correcta utilización. Además de la interfaz de usuario ofrecida, esta herramienta puede ser utilizada como API (Application Programming Interface) por otras aplicaciones.

PHORA: a system to solve the anaphora in Spanish

In this paper we present a whole Natural Language Processing (NLP) system for Spanish. The core of this system is the parser, which uses the grammatical formalism Lexical-Functional Grammars (LFG). Another important component of this system is the anaphora resolution module. To solve the anaphora, this module contains a method based on linguistic information (lexical, morphological, syntactic and semantic), structural information (anaphoric accessibility space in which the anaphor obtains the antecedent) and statistical information. This method is based on constraints and preferences and solves pronouns and definite descriptions. Moreover, this system fits dialogue and non-dialogue discourse features. The anaphora resolution module uses several resources, such as a lexical database (Spanish WordNet) to provide semantic information and a POS tagger providing the part of speech for each word and its root to make this resolution process easier.

A knowledge-based textual entailment approach applied to the QA answer validation at CLEF 2006

The Answer Validation Exercise (AVE) is a pilot track within the Cross-Language Evaluation Forum (CLEF) 2006. The AVE competition provides an evaluation frame- work for answer validations in Question Answering (QA). In our participation in AVE, we propose a system that has been initially used for other task as Recognising Textual Entailment (RTE). The aim of our participation is to evaluate the improvement our system brings to QA. Moreover, due to the fact that these two task (AVE and RTE) have the same main idea, which is to find semantic implications between two fragments of text, our system has been able to be directly applied to the AVE competition. Our system is based on the representation of the texts by means of logic forms and the computation of semantic comparison between them. This comparison is carried out using two different approaches. The first one managed by a deeper study of the Word- Net relations, and the second uses the measure defined by Lin in order to compute the semantic similarity between the logic form predicates. Moreover, we have also designed a voting strategy between our system and the MLEnt system, also presented by the University of Alicante, with the aim of obtaining a joint execution of the two systems developed at the University of Alicante. Although the results obtained have not been very high, we consider that they are quite promising and this supports the fact that there is still a lot of work on researching in any kind of textual entailment.

Interoperabilidad semántica de ontologías basada en técnicas de procesamiento del lenguaje natural

Se presenta un estudio y propuesta de interoperabilidad semántica entre ontologías del dominio de la salud basada en técnicas de procesamiento del lenguaje natural. El objetivo fundamental ha sido el desarrollo de un algoritmo de interconexión semántica entre los términos de dos ontologías solapadas y heterogéneas, denominadas «fuente» (Clasificación internacional de enfermedades, 9ª revisión, modificación clínica: CIE-9-MC) y «diana» (esquema jerárquico de la asignatura Enfermería Materno-Infantil: EMI). Esta propuesta permite emparejar semánticamente ontologías, a partir de la reutilización de otro recurso ontológico (WordNet español), sin destruir o modificar la semántica de identidad de cada una de las ontologías involucradas. El modelo presentado puede permitir al usuario acceder a la información que necesita en otra clasificación jerárquica, sin precisar de un entrenamiento referido a la conceptualización de cada sistema, pues utilizaría la ontología «diana» con la que está familiarizado para su aplicación a la recuperación de información.

Improving subjectivity detection using unsupervised subjectivity word sense disambiguation

En este trabajo se presenta un método para la detección de subjetividad a nivel de oraciones basado en la desambiguación subjetiva del sentido de las palabras. Para ello se extiende un método de desambiguación semántica basado en agrupamiento de sentidos para determinar cuándo las palabras dentro de la oración están siendo utilizadas de forma subjetiva u objetiva. En nuestra propuesta se utilizan recursos semánticos anotados con valores de polaridad y emociones para determinar cuándo un sentido de una palabra puede ser considerado subjetivo u objetivo. Se presenta un estudio experimental sobre la detección de subjetividad en oraciones, en el cual se consideran las colecciones del corpus MPQA y Movie Review Dataset, así como los recursos semánticos SentiWordNet, Micro-WNOp y WordNet-Affect. Los resultados obtenidos muestran que nuestra propuesta contribuye de manera significativa en la detección de subjetividad.

Sentiment classification for early detection of health alerts in the chemical textile domain

In the chemical textile domain experts have to analyse chemical components and substances that might be harmful for their usage in clothing and textiles. Part of this analysis is performed searching opinions and reports people have expressed concerning these products in the Social Web. However, this type of information on the Internet is not as frequent for this domain as for others, so its detection and classification is difficult and time-consuming. Consequently, problems associated to the use of chemical substances in textiles may not be detected early enough, and could lead to health problems, such as allergies or burns. In this paper, we propose a framework able to detect, retrieve, and classify subjective sentences related to the chemical textile domain, that could be integrated into a wider health surveillance system. We also describe the creation of several datasets with opinions from this domain, the experiments performed using machine learning techniques and different lexical resources such as WordNet, and the evaluation focusing on the sentiment classification, and complaint detection (i.e., negativity). Despite the challenges involved in this domain, our approach obtains promising results with an F-score of 65% for polarity classification and 82% for complaint detection.