Adquisición no supervisada de aspectos de un dominio para Minería de Opiniones Basada en Aspectos

The automatic analysis of opinions, which usually receives the name of opinion mining or sentiment analysis, has gained a great importance during the last decade. This is mainly due to the overgrown of online content in the Internet. The so-called aspect based opinion mining systems aim to detect the sentiment at “aspect” level (i.e. the precise feature being opinionated in a clause or sentence). In order to detect such aspects it is required some knowledge about the domain under analysis. The vocabulary in different domains may vary, and different words are interesting features in different domains. We aim to generate a list of domain related words and expressions from unlabeled domain texts, in a completely unsupervised way, as a first step to a more complex opinion mining system.El análisis automático de la opinión, que usualmente recibe el nombre minería de opinión o análisis del sentimiento, ha cobrado una gran importancia durante la última década. La minería de opinión basada en aspectos se centra en detectar el sentimiento con respecto a “aspectos” de la entidad examinada (i.e. características o partes concretas evaluadas en una sentencia). De cara a detectar dichos aspectos se requiere una cierta información sobre el dominio o temática del contenido analizado, ya que el vocabulario varía de un dominio a otro. El objetivo de este trabajo es generar de manera automática una lista de aspectos del dominio partiendo de un set de textos sin etiquetar, de manera completamente no supervisada, como primer paso para el desarrollo de un sistema más completo.This work has been partially funded by OpeNER (FP7-ICT-2011-SME-DCL-296451) and SKaTer (TIN2012-38584-C06-02)

Visual mining of semi-structured data

Background Vicomtech is visiting CERN to expose their activities and explore possible lines of collaboration. As part of the programme they will be offering a presentation, staged in three parts: Presentation of Vicomtech &ndash; Se&aacute;n Gaines Descriptions of technologies and specialities &ndash; Dr. Jorge Posada Details on projects related to the development of visually-based algorithms for intelligent storage, processing, visualization and interaction with Big Data, for massive sources of information. &ndash; Dr. Marco Quartulli. The full programme to the visit is here Abstract Mining semi-structured data is fundamental for archive monitoring, understanding and exploitation. Typical analysis systems are based on a three-tiered architecture, in which efficient databases feed highly parallelised application servers that in turn feed client user interfaces. Yet the sharing of analysis, content identification and semantic level summarization tasks among the two bottom layers of the architecture &ndash; the highly parallel application server and a shared database &ndash; are key to allowing the user interface deal interactively with large data volumes. In this framework, we present approaches based on NoSQL/MapReduce, experiences with column-based scientific DBMSs and considerations on graph databases for the efficient processing of large repositories. Specific attention is devoted to Visual Analytics methodologies resulting in multi-user interfaces centring on multiple linked representations implementing interaction techniques based on concurrent brushing in multiple dimensions. These considerations and experiences provide the core for operational and pre-operational systems for mining multimedia archives, for analysing social streams for cyber-security applications and for search engines dedicated to Earth observation products. Extensions to content-based global and local image retrieval, to mining compressed streams, to multi-modal search and to mining activity data collected by distributed mobile networks are introduced.</p

OpeNER: reconocimiento de entidades nombradas con polaridad

Actualmente existe una gran cantidad de empresas ofreciendo servicios para el análisis de contenido y minería de datos de las redes sociales con el objetivo de realizar análisis de opiniones y gestión de la reputación. Un alto porcentaje de pequeñas y medianas empresas (pymes) ofrecen soluciones específicas a un sector o dominio industrial. Sin embargo, la adquisición de la necesaria tecnología básica para ofrecer tales servicios es demasiado compleja y constituye un sobrecoste demasiado alto para sus limitados recursos. El objetivo del proyecto europeo OpeNER es la reutilización y desarrollo de componentes y recursos para el procesamiento lingüístico que proporcione la tecnología necesaria para su uso industrial y/o académico.Currently there are a many companies offering Content Analytics and Social Internet Mining services for the purposes of Opinion Mining and Reputation Management. A high percentage of Small and Medium Enterprises (SMEs) are active offering niche solutions to specific segments of the market and/or domains. However, acquiring or developing the base qualifying technologies required to enter the market is an expensive undertaking that redirects the already limited resources of SMEs away from offering products and services that the market demands. The main goal of the OpeNER European project is the reuse and repurposing of existing language resources and data sets to provide a set of underlying technologies to the broader industrial and academic community.The research leading to these results has received funding from the European Research Council under the European Union's Seventh Framework Programme (FP/2007-2013)/ERC Grant Agreement n. 296451

Artificial Driving Intelligence and Moral Agency: Examining the Decision Ontology of Unavoidable Road Traffic Accidents through the Prism of the Trolley Dilemma

The question of the capacity of artificial intelligence to make moral decisions has been a key focus of investigation in robotics for decades. This question has now become pertinent to automated vehicle technologies, as a question of understanding the capacity of artificial driving intelligence to respond to unavoidable road traffic accidents. Artificial driving intelligence will make a calculated decision that could equate to deciding who lives and who dies. In calculating such important decisions, does the driving intelligence require moral intelligence and a capacity to make informed moral decisions? Artificial driving intelligence will be determined by at very least, state laws, driving codes, and codes of conduct relating to driving behaviour and safety. Does it also need to be informed by ethical theories, human values, and human rights frameworks? If so, how can this be achieved and how can we ensure there are no moral biases in the moral decision-making algorithms? The question of moral capacity is complex and has become the ethical focal point of this technology. Research has centred on applying Philippa Foot’s famous trolley dilemma. We claim that before applications attempt to focus on moral theories, there is a necessary precedent to utilise the trolley dilemma as an ontological experiment. The trolley dilemma is succinct in identifying important ontological differences between human driving intelligence and artificial driving intelligence. In this paper, we argue that when the trolley dilemma is focused upon ontology, it has the potential to become an important elucidatory tool. It can act as a prism through which one can perceive different ontological aspects of driving intelligence and assess response decisions to unavoidable road traffic accidents. The identification of the ontological differences is integral to understanding the underlying variances that support human and artificial driving decisions. Ontologically differentiating between these two contexts allows for a more complete interrogation of the moral decision-making capacity of the artificial driving intelligence