Definición de disparador de emoción asociado a la cultura y aplicación a la clasificación de la valencia y la emoción en textos

Este artículo presenta un método de identificación y clasificación de la valencia y las emociones presentes en un texto. Para ello, se introduce un nuevo concepto denominado disparador de emoción. Inicialmente, se construye de forma incremental una base de datos léxica de disparadores de emoción asociados a la cultura con la que se quiere trabajar, basándose en tres teorías diferentes: la Teoría de la Relevancia de Pragmática, la Teoría de la Motivación de Maslow de Psicología y la Teoría de Necesidades de Neef de Economía. La base de datos creada parte de un conjunto inicial de términos y es ampliada con la información de otros recursos léxicos, como WordNet, NomLex y dominios relevantes. El enlace entre idiomas se hace por medio de EuroWordNet y se completa y adapta a diversas culturas con bases de conocimiento específicas para cada lengua. También, se demuestra cómo la base de datos construida puede ser utilizada para buscar en textos la valencia (polaridad) y el significado afectivo. Finalmente, se evalúa el método utilizando los datos de prueba de la tarea nº 14 de Semeval “Texto afectivo” y su traducción al español. Los resultados y las mejoras se presentan junto con una discusión en la que se tratan los puntos fuertes y débiles del método y las directrices para el trabajo futuro.This paper presents a method to automatically spot and classify the valence and emotions present in written text, based on a concept we introduced - of emotion triggers. The first step consists of incrementally building a culture dependent lexical database of emotion triggers, emerging from the theory of relevance from pragmatics, Maslow´s theory of human needs from psychology and Neef´s theory of human needs in economics. We start from a core of terms and expand them using lexical resources such as WordNet, completed by NomLex, sense number disambiguated using the Relevant Domains concept. The mapping among languages is accomplished using EuroWordNet and the completion and projection to different cultures is done through language-specific commonsense knowledge bases. Subsequently, we show the manner in which the constructed database can be used to mine texts for valence (polarity) and affective meaning. An evaluation is performed on the Semeval Task No. 14: Affective Text test data and their corresponding translation to Spanish. The results and improvements are presented together with an argument on the strong and weak points of the method and the directions for future work

ISR-WN: Integration of semantic resources based on 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

T2Know: An Advance Scientific-Tecnical Text Analysis Platform for Trend and Knowledge Extraction Using NLP Techniques

The project T2Know presents the use of natural language processing technologies for the creation of a semantic platform of scientific documents via knowledge graphs. This knowledge graph will link relevant parts of each document with those of other documents in such a way that trend analysis and recommendations can be achieved. The goals addressed within the scope of this project include entity recognizers development, profile definition and documents linkage through the use of transformers technologies. As a result, the relevant parts of the documents to be extracted are related not only to the title and affiliation of the authors, but also to article topics such as references, which are also considered relevant parts of the scientific article.This project is funded by the Valencian Agency for Innovation through the project INNEST/2022/24, partially funded by the Generalitat Valenciana (Conselleria d’Educació, Investigació, Cultura i Esport) through the following projects NL4DISMIS: TLHs for an Equal and Accessible Inclusive Society (CIPROM/2021/021) and T2Know: Platform for advanced analysis of scientific-technical texts to extract trends and knowledge through NLP techniques. (Innest/2022/24). Moreover, it was backed by the work of two COST Actions: CA19134 - “Distributed Knowledge Graphs” and CA19142 - “Leading Platform for European Citizens, Industries, Academia, and Policymakers in Media Accessibility”

Stability and numerical solutions for second-order ordinary differential equations with application in mechanical systems

This study undertakes a comprehensive analysis of second-order Ordinary Differential Equations (ODEs) to examine animal avoidance behaviors, specifically emphasizing analytical and computational aspects. By using the Picard–Lindelöf and fixed-point theorems, we prove the existence of unique solutions and examine their stability according to the Ulam-Hyers criterion. We also investigate the effect of external forces and the system’s sensitivity to initial conditions. This investigation applies Euler and Runge–Kutta fourth-order (RK4) methods to a mass-spring-damper system for numerical approximation. A detailed analysis of the numerical approaches, including a rigorous evaluation of both absolute and relative errors, demonstrates the efficacy of these techniques compared to the exact solutions. This robust examination enhances the theoretical foundations and practical use of such ODEs in understanding complex behavioral patterns, showcasing the connection between theoretical understanding and real-world applications.Open Access funding provided thanks to the CRUE-CSIC agreement with Springer Nature. This research is supported by the University of Alicante, Spain, the Spanish Ministry of Science and Innovation, the Generalitat Valenciana, Spain, and the European Regional Development Fund (ERDF) through the following funding: At the national level, the following projects were granted: TRIVIAL (PID2021-122263OB-C22); and CORTEX (PID2021-123956OB-I00), funded by MCIN/AEI/10.13039/501100011033 and, as appropriate, by “ERDF A way of making Europe”, by the“European Union” or by the “European Union NextGenerationEU/PRTR”. At regional level, the Generalitat Valenciana (Conselleria d’Educacio, Investigacio, Cultura i Esport), Spain, granted funding for NL4DISMIS (CIPROM/2021/21)

Going beyond traditional QA systems: challenges and keys in opinion question answering

The treatment of factual data has been widely studied in different areas of Natural Language Processing (NLP). However, processing subjective information still poses important challenges. This paper presents research aimed at assessing techniques that have been suggested as appropriate in the context of subjective - Opinion Question Answering (OQA). We evaluate the performance of an OQA with these new components and propose methods to optimally tackle the issues encountered. We assess the impact of including additional resources and processes with the purpose of improving the system performance on two distinct blog datasets. The improvements obtained for the different combination of tools are statistically significant. We thus conclude that the proposed approach is adequate for the OQA task, offering a good strategy to deal with opinionated questions.This paper has been partially supported by Ministerio de Ciencia e Innovación - Spanish Government (grant no. TIN2009-13391-C04-01), and Conselleria d'Educación - Generalitat Valenciana (grant no. PROMETEO/2009/119 and ACOMP/2010/286)

Spreading semantic information by Word Sense Disambiguation

This paper presents an unsupervised approach to solve semantic ambiguity based on the integration of the Personalized PageRank algorithm with word-sense frequency information. Natural Language tasks such as Machine Translation or Recommender Systems are likely to be enriched by our approach, which includes semantic information that obtains the appropriate word-sense via support from two sources: a multidimensional network that includes a set of different resources (i.e. WordNet, WordNet Domains, WordNet Affect, SUMO and Semantic Classes); and the information provided by word-sense frequencies and word-sense collocation from the SemCor Corpus. Our series of results were analyzed and compared against the results of several renowned studies using SensEval-2, SensEval-3 and SemEval-2013 datasets. After conducting several experiments, our procedure produced the best results in the unsupervised procedure category taking SensEval campaigns rankings as reference.This research work has been partially funded by the University of Alicante, Generalitat Valenciana , Spanish Government, Ministerio de Educación, Cultura y Deporte and ASAP - Ayudas Fundación BBVA a equipos de investigación científica 2016(FUNDACIONBBVA2-16PREMIO) through the projects, TIN2015- 65100-R, TIN2015-65136-C2-2-R, PROMETEOII/2014/001, GRE16- 01: “Plataforma inteligente para recuperación, análisis y representación de la información generada por usuarios en Internet” and PR16_SOC_0013

Text2Onto - A Framework for Ontology Learning and Data-driven Change Discovery

Cimiano P, Völker J. Text2Onto - A Framework for Ontology Learning and Data-driven Change Discovery. In: Montoyo A, Munoz R, Metais E, eds. Natural language processing and information systems : 10th International Conference on Applications of Natural Language to Information Systems, NLDB 2005, Alicante, Spain, June 15 - 17, 2005 ; proceedings. Lecture notes in computer science, 3513. Springer; 2005: 227-238

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

Automatic Discovery of Heterogeneous Machine Learning Pipelines: An Application to Natural Language Processing

This paper presents AutoGOAL, a system for automatic machine learning (AutoML) that uses heterogeneous techniques. In contrast with existing AutoML approaches, our contribution can automatically build machine learning pipelines that combine techniques and algorithms from different frameworks, including shallow classifiers, natural language processing tools, and neural networks. We define the heterogeneous AutoML optimization problem as the search for the best sequence of algorithms that transforms specific input data into the desired output. This provides a novel theoretical and practical approach to AutoML. Our proposal is experimentally evaluated in diverse machine learning problems and compared with alternative approaches, showing that it is competitive with other AutoML alternatives in standard benchmarks. Furthermore, it can be applied to novel scenarios, such as several NLP tasks, where existing alternatives cannot be directly deployed. The system is freely available and includes in-built compatibility with a large number of popular machine learning frameworks, which makes our approach useful for solving practical problems with relative ease and effort.This research has been supported by a Carolina Foundation grant in agreement with University of Alicante and University of Havana. Moreover, it has also been partially funded by both aforementioned universities, the Generalitat Valenciana (Conselleria d’Educació, Investigació, Cultura i Esport) and the Spanish Government through the projects LIVING-LANG (RTI2018-094653-B-C22) and SIIA (PROMETEO/2018/089, PROMETEU/2018/089)

Aportaciones para la mejora en la Enseñanza de la Ingeniería del Software utilizando las nuevas tecnologías de la información

Debido a las características de la Ingeniería del Software es difícil trasladar a un aula las particularidades que se encuentran en el proceso de análisis y diseño de un caso real de Sistemas de Información. En este artículo se propone un método que no solo prima la interactuación alumno-empresa a través de convenios de colaboración que permiten al alumno enfrentarse con problemas reales de Ingenieria del software sino que también presente las diversas posibilidades que aportan las nuevas tecnologías a nuestro método de enseñanza. El artículo muestra cómo el empleo de estas tecnologías nos permite llevar una planificación más estricta respecto a las entregas parciales y seguimiento de los proyectos, obligando a los alumnos a llevar el proyecto de forma más continuada, organizada y formativa