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Opinion Mining in Social Networks versus Electoral Polls

The recent failures of traditional poll models, like the predictions in United Kingdom with the Brexit, or in United States presidential election with the victory of Donald Trump, have been noteworthy. With the decline of traditional poll models and the growth of the social networks, automatic tools are gaining popularity to make predictions in this context. In this paper we present our approximation and compare it with a real case: the 2017 French presidential election.

Diseño, compilación y anotación de un corpus para la detección de mensajes suicidas en redes sociales

Con el fin de desarrollar un sistema de prevención del suicidio en la red, se ha compilado y anotado un corpus piloto de mensajes de ideación suicida extraídos de las redes sociales. Los textos se han obtenido tanto de la Web como de la Deep Web, y se han seleccionado textos escritos tanto en español como en inglés. Para caracterizar semánticamente cada mensaje, éstos han sido anotados según su relación con el fenómeno suicida (pro-suicida, instigador, anti-suicidio, etc.).

Cross-Document Event Ordering through Temporal Relation Inference and Distributional Semantic Models

This paper focuses on the contribution of temporal relations inference and distributional semantic models to the event ordering task. Our system automatically builds ordered timelines of events from different written texts in English by performing first temporal clustering and then semantic clustering. In order to determine temporal compatibility, an inference from the temporal relationships between events –automatically extracted from a Temporal Information Processing system– is applied.

Cross-document event ordering through temporal, lexical and distributional knowledge

In this paper we present a system that automatically builds ordered timelines of events from different written texts in English. The system deals with problems such as automatic event extraction, cross-document temporal relation extraction and cross-document event coreference resolution. Its main characteristic is the application of three different types of knowledge: temporal knowledge, lexical-semantic knowledge and distributional-semantic knowledge, in order to anchor and order the events in the timeline. It has been evaluated within the framework of SemEval 2015.

Natural Language Processing technologies study for Document Profiling

Nowadays, search for documents on Internet is becoming increasingly difficult. The reason is the amount of content published by users (articles, comments, blogs, reviews). How to facilitate that the users can find their required documents? What would be necessary to provide useful document meta-data for supporting search engines? In this article, we present a study of some Natural Language Processing (NLP) technologies can be useful for facilitating the proper identification of documents according to the user needs.

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.

Escenarios y caracterización de entidades digitales (report interno)

Se presentan los escenarios propuestos asíc omo la caracterización inicial de las entides digitales para el ámbito del proyecto de acuerdo con los trabajos previos efectuados por UA

Innovative Semi-Automatic Methodology to Annotate Emotional Corpora

Detecting depression or personality traits, tutoring and student behaviour systems, or identifying cases of cyber-bulling are a few of the wide range of the applications, in which the automatic detection of emotion is a crucial element. Emotion detection has the potential of high impact by contributing the benefit of business, society, politics or education. Given this context, the main objective of our research is to contribute to the resolution of one of the most important challenges in textual emotion detection task: the problems of emotional corpora annotation.

Analizando opiniones en las redes sociales

La Web 2.0 ha focalizado la importancia de la información, no en unos pocos expertos en un tema, sino en una multitud de opiniones vertidas por usuarios a través de diversos medios en las redes sociales. Debido a ello, han cobrado un mayor interés los sistemas que son capaces de determinar qué es lo que piensan los usuarios sobre un determinado concepto, agregando diferentes fuentes de datos y aplicando cálculos de polaridad de las opiniones, que permiten determinar y comparar esos conceptos con otros similares.

Gestión de resúmenes para dispositivos móviles

Los dispositivos móviles han cambiado notablemente la forma en la que los usuarios acceden a la información disponible en Internet. Estos dispositivos permiten un acceso instantáneo desde cualquier lugar, pero tienen una serie de limitaciones importantes sobre los ordenadores personales. Su limitada pantalla, así como en ocasiones la limitada capacidad de recepción de la información, dado el coste, hacen que la selección de información a acceder sea todavía más importante.

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