Suchergebnisse

Apart from the economic impact of the online gambling industry, the social, public order and health-related consequences of the industry merit analysis to inform appropriate action, regulatory or otherwise. The omnipresence of ICTs, the inability to use technologies properly, along with the growth of online gambling channels, have acted simultaneously as a catalyst for the spread of pathological and problematic gambling. In this context, social networks have become a highly effective platform to instil positive attitudes towards the products of gambling operators. This work uses the Natural Language Processing based web application "GPLSI Social Analytics" to track, in real time, the conversations generated on Twitter about the Spanish domain accounts of the main online sports gambling operators. The findings indicate that most of the messages about these operators are positive and surprise is the predominant emotion associated with them. The notion of responsible online gambling barely receives a mention in the conversations analysed. Given the role of new technologies as access facilitators and potential enhancers of addictive behaviours, it is necessary to adopt measures directed at social networks that guarantee the coexistence of the right to freedom of expression with the protection of the most vulnerable populations. ; This research work has been partially funded by Generalitat Valenciana through project "SIIA: Tecnologías del lenguaje humano para una sociedad inclusiva, igualitaria, y accessible" with grant reference PROMETEU/2018/089, by the Spanish Government through project RTI2018-094653-B-C22: "Modelang: Modeling the behavior of digital entities by Human Language Technologies'', as well as being partially supported by a grant from the Fondo Europeo de Desarrollo Regional (FEDER) and the LIVINGLANG project (RTI2018-094653-B-C21) from the Spanish Government. Additionally, it has been backed by the work of both COST Actions: CA19134 – "Distributed Knowledge Graphs" and CA19142 – "Leading Platform for European Citizens, Industries, Academia and Policymakers in Media Accessibility".

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.

In recent year the great popularity that enjoys mobile technologies has led most users to become consumers and producers of information on the network. Many studies speak about this phenomenon as an activity that is capable of doubling or tripling existing content on an annual basis. The huge amount of information makes the current user oriented systems, like retrieval systems, recommender systems and others, become less efficient, especially when users require specific information and answers according to their needs and preferences. These facts make necessary to equip these systems with proper Natural Language technologies able to provide the information that users demand adapted to each context and content type. In this article, it is presented a study of some Natural Language Processing technologies that can be useful to facilitate the proper identification of documents according to the user needs. For this purpose, it is designed a document pro le that will be able to represent semantic meta-data extracted from documents. The research is basically focused on the study of different language technologies in order to support the creation this novel document profile proposal from semantic perspectives. ; This work is funded by a FPU predoctoral fellowship granted by University of Alicante (UAFPU2015-5999), and has also been partially funded by the University of Alicante, Generalitat Valenciana, Spanish Government, Ministerio de Educación, Cultura y Deporte and Ayudas Fundación BBVA a equipos de investigación científica 2016 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 ASAP.

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.

Ontologies are appropriate structures for capturing and representing the knowledge about a domain or task. However, the design and further population of them are both difficult tasks, normally addressed in a manual or in a semi-automatic manner. The goal of this article is to de_ne and extend a task-oriented ontology schema that semantically represents the information contained in texts. This information can be extracted using Human Language Technologies, and throughout this work, the whole process to design such ontology schema is described. Then, we also describe an algorithm to automatically populate ontologies based our Human Language Technology oriented schema, avoiding the unnecessary duplication of instances, and having as a result the required information in a more compact and useful format ready to exploit. Tangible results are provided, such as permanent online access points to the ontology schema, an example bucket (i.e. ontology instance repository) based on a real scenario, and a documentation Web page. ; This research work has been partially funded by the University of Alicante, Generalitat Valenciana, Spanish Government, Ministerio de Educación, Cultura y Deporte and Ayudas Fundación BBVA a equipos de investigación científica 2016 through the projects TIN2015-65100-R, TIN2015-65136-C2-2-R, PROMETEU/2018/089,"Plataforma inteligente para recuperación, análisis y representación de la información generada por usuarios en Internet" (GRE16-01) and "Análisis de Sentimientos Aplicado a la Prevención del Suicidio en las Redes Sociales" (ASAP).

The writing style used in social media usually contains informal elements that can lower the performance of Natural Language Processing applications. For this reason, text normalisation techniques have drawn a lot of attention recently when dealing with informal content. However, not all the texts present the same level of informality and may not require additional pre-processing steps. Therefore, in this paper we explore the results of applying lexical normalisation applied to a sentiment analysis classification task on Web 2.0 texts, shows more than a 2.6% improvement over average F1 for the most informal data. ; El tipo de lenguaje empleado en las redes sociales suele incluir elementos informales que pueden afectar el rendimiento de las herramientas de procesamiento del lenguaje natural. El uso de técnicas de normalización léxica es una de las opciones que se han estado usando a la hora de tratar contenidos de la Web 2.0. Sin embargo, no todos los textos requieren dicho pre-procesamiento ya que pueden exhibir diferentes niveles de informalidad. En este trabajo exploramos el impacto de aplicar normalización léxica evaluando los resultados de un sistema de análisis del sentimiento antes y después de la normalización. Los resultados de nuestra investigación muestran una mejora de más del 2.6% sobre el F1 para los textos más informales. ; This work has been partially funded by the European Commission under the 7th Framework Programme for Research and Technological Development through the SAM (FP7-611312) project, by the Spanish Government through the ATTOS (TIN2012-38536-C03-03) and LEGOLANGUAGE (TIN2012-31224) projects, and by the University of Alicante through the project "Explotación y tratamiento de la información disponible en Internet para la anotación y generación de textos adaptados al usuario" (GRE13-15) and "Tratamiento inteligente de la información para la ayuda a la toma de decisiones" (GRE12-44).

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. ; In this work, we present a sentence-level subjectivity detection method, which relies on Subjectivity Word Sense Disambiguation (SWSD). We use an unsupervised sense clustering-based method for SWSD. In our method, semantic resources tagged with emotions and sentiment polarities are used to apply subjectivity detection, intervening Word Sense Disambiguation sub-tasks. Through an experimental study, we empirically validated the proposed method over two subjectivity collections, MPQA Corpus and Movie Review Dataset, using three widely popular opinion-mining resources SentiWordNet, WordNet-Affect and Micro-WNOp. The results show that our proposal performs significantly better than our proposed baseline. ; This research work has been partially funded by the Spanish Government through the project TEXT-MESS 2.0 (TIN2009-13391-C04), "Análisis de Tendencias Mediante Técnicas de Opinión Semántica" (TIN2012-38536-C03-03), and SAM - Dynamic Social & Media Content Syndication for 2nd Screen (FP7-611312); and by the Valencian Government through the project PROMETEO (PROMETEO/2009/199).

Paper submitted to CICLing 2018, 19th International Conference on Computational Linguistics and Intelligent Text Processing, March 18 to 24, 2018, Hanoi, Vietnam. ; The use of social networks as a predictive tool to predict the outcome of an election can generate controversy; however if you have a methodology that tries to equate the extracted data as if they were obtained through a conventional survey, that is to say using weighting factors more than what usually should be done, polarity and relevance of each tweet, can make it a very reliable technique in light of the results obtained; the proposed methodology was applied in the presidential election of Ecuador on February 19th, 2017. ; 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.

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. ; En este artículo se presenta el enriquecimiento de la herramienta Integración de recursos Semánticos basados en WordNet (ISR-WN Enriquecido). Esta nueva propuesta mejora la anterior, introduciendo nuevos recursos tales como Semantic Classes y SentiWordNet a los anteriormente relacionados: SUMO, WordNet Domains y WordNet Affects. Previamente a la introducción de nuevos recursos se describe la arquitectura de esta propuesta explicando las particularidades de cada recurso integrado. Tras la descripción de la arquitectura se analizan los problemas surgidos tras el mapeo de diferentes versiones y cómo se han solucionado. Además, se muestran las ventajas que la introducción de este tipo de herramientas pueden proporcionar a diferentes aplicaciones de Procesamiento del Lenguaje Natural. Relacionado con esta cuestión también se demuestra que la integración de recursos semánticos permite adquirir una visión multidimensional en el análisis del lenguaje natural. ; This paper has been supported partially by Ministerio de Ciencia e Innovación - Spanish Government (grant no. TIN2009-13391-C04-01), and Conselleria d'Educació - Generalitat Valenciana (grant no. PROMETEO/2009/119, ACOMP/2010/288 and ACOMP/2011/001).

This paper introduces Hierarchical Machine Learning Optimisation (HML-Opt), an AutoML framework that is based on probabilistic grammatical evolution. HML-Opt has been designed to provide a flexible framework where a researcher can define the space of possible pipelines to solve a specific machine learning problem, which can range from high-level decisions about representation and features to low-level hyper-parameter values. The evaluation of HML-Opt is presented via two different case studies, both of which demonstrate that it is competitive with existing AutoML tools on a variety of benchmarks. Furthermore, HML-Opt can be applied to novel problems, such as knowledge extraction from natural language text, whereas other techniques are insufficiently flexible to capture the complexity of these scenarios. The source code for HML-Opt is available online for the research community. ; 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).

The massive amount of biomedical information published online requires the development of automatic knowledge discovery technologies to effectively make use of this available content. To foster and support this, the research community creates linguistic resources, such as annotated corpora, and designs shared evaluation campaigns and academic competitive challenges. This work describes an ecosystem that facilitates research and development in knowledge discovery in the biomedical domain, specifically in Spanish language. To this end, several resources are developed and shared with the research community, including a novel semantic annotation model, an annotated corpus of 1045 sentences, and computational resources to build and evaluate automatic knowledge discovery techniques. Furthermore, a research task is defined with objective evaluation criteria, and an online evaluation environment is setup and maintained, enabling researchers interested in this task to obtain immediate feedback and compare their results with the state-of-the-art. As a case study, we analyze the results of a competitive challenge based on these resources and provide guidelines for future research. The constructed ecosystem provides an effective learning and evaluation environment to encourage research in knowledge discovery in Spanish biomedical documents. ; This research has been partially supported by the University of Alicante and University of Havana, the Generalitat Valenciana (Conselleria d'Educació, Investigació, Cultura i Esport) and the Spanish Government through the projects SIIA (PROMETEO/2018/089, PROMETEU/2018/089) and LIVING-LANG (RTI2018-094653-B-C22).

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).

SAM is a social media platform that enhances the experience of watching video content in a conventional living room setting, with a service that lets the viewer use a second screen (such as a smart phone) to interact with content, context and communities related to the main video content. This article describes three key functionalities used in the SAM platform in order to create an advanced interactive and social second screen experience for users: semantic analysis, context awareness and dynamic communities. Both dataset-based and end user evaluations of system functionalities are reported in order to determine the effectiveness and efficiency of the components directly involved and the platform as a whole. ; This work has been partially funded by the European Commission under the Seventh (FP7 2007-2013) Framework Programme for Research and Technological Development through the SAM (FP7-611312) project, by the Spanish Government under project REDES (TIN2015-65136-C2-2-R) and by the Generalitat Valenciana under project (PROMETEU/2018/089).

ElectionMap es una aplicación web que realiza un seguimiento a los comentarios publicados en Twitter en relación a entidades que refieren a partidos políticos. Las opiniones de los usuarios sobre estas entidades son clasificadas según su valoración y posteriormente representadas en un mapa geográfico para conocer la aceptación social sobre agrupaciones políticas en las distintas regiones de la geografía española. ; ElectionMap is a web application that follows, in Twitter, entities previously established and related to the politics. The user's opinions about the entities are classified according to its valuation by using sentiment analysis processes. Afterwards the opinions are represented in a geographic map that allows to know the social acceptance of Spanish political parties in different geographical areas. ; ElectionMap es una aplicación web desarrollada por el Grupo de Procesamiento del Lenguaje Natural y Sistemas de Información (GPLSI) de la Universidad de Alicante. Esta aplicación ha sido parcialmente financiada por el Gobierno Español y la Comisión Europea a través de los proyectos: ATTOS (TIN2012-38536-C03-03), LEGOLANG (TIN2012-31224), SAM (FP7-611312) y FIRST (FP7-287607) y por la Universidad de Alicante a través del proyecto emergente "Explotación y tratamiento de la información disponible en Internet para la anotación y generación de textos adaptados al usuario" (GRE13-15).

Discovering the main features of virality patterns in Twitter is the focus of this research. Five trending topics related to the COVID-19 pandemic were selected for the study, with Spanish as the target language. To carry out the discovery of virality patterns, we applied opinion mining techniques that enable us to structure the information based on the polarity of the messages and the emotions they contain. After transforming the information from an unstructured textual representation to a structured one, data mining techniques were applied, specifically association rules mining. Message patterns with the highest virality (high shares and high likes), and at the same time the most relevant characteristics of the patterns with less impact were extracted. After an exhaustive analysis of the most relevant non-redundant rules, it can be concluded that messages with a high-negative polarity and a very high emotional charge, especially emotions that have intensified with the COVID-19 pandemic, such as fear, sadness, anger and surprise are more likely to go viral in social media. By contrast, messages with little news coverage in the media, few authors, and the absence of surprise are relevant features when it comes to seeing messages with very low dissemination in social media. ; This research work has been partially funded by Generalitat Valenciana through project "SIIA: Tecnologias del lenguaje humano para una sociedad inclusiva, igualitaria, y accesible" with grant reference PROMETEU/2018/089. This research work has been also funded by FEDER (EU) / Ministerio de Ciencia e Innovacion - Agencia Estatal de Investigacion (Spanish Government)" through project RTI2018-094653-B-C22: "Modelang: Modeling the behavior of digital entities by Human Language Technologies", and the LIVING-LANG project (RTI2018-094653-B-C21). Additionally, it has been backed by the work of both COST Actions: CA19134 – "Distributed Knowledge Graphs" and CA19142 – "Leading Platform for European Citizens, Industries, Academia and Policymakers in Media Accessibility".