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SESION INAUGURAL DOCTORADO SISTEMAS INTELIGENTES, UNED, CURSO ACADEMICO 2015/16 Fecha : 3 Noviembre 2015, Horario: 9,30 a 13h. Lugar: Sala J.Mira, ETSI Informática de la UNED, 4 planta. Enlace para el streaming. https://canal.uned.es/teleacto/681.html Programa 9,30-10,15- Bienvenida y presentación actividades del doctorado. M.Felisa Verdejo, Coordinadora del programa de doctorado de SI. 10,25- 11,25- Conferencia invitada: On why discourse is good for sentiment analysis. Maite Taboada, Simon Fraser University, Vancouver, Canada 11,45- 12,45- Conferencia invitada : Learning Bayesian networks for Multi-Relational Data. Oliver Schulte, Simon Fraser University, Vancouver, Canada 12,45-13- Clausura. Resúmenes de las conferencias y biografías de los conferenciantes invitados: On why discourse is good for sentiment analysis Abstract The study of evaluation, sentiment and subjectivity is a multidisciplinary enterprise, including sociology, psychology, linguistics and computer science. In computer science and computational linguistics, sentiment analysis or opinion mining focuses on extracting sentiment at two main levels of granularity: the document and the sentence. The first level aims to categorize documents globally as being positive or negative towards a given topic, whereas at the sentence level the goal is to determine sentiment locally, using words or phrases in the sentence. Extraction methods in both cases rely on a variety of approaches going from bag-of-words representations to more sophisticated models that address the complexity of language, and insights from linguistics, such as the role of negation, speculation and various context-dependent phenomena. In this talk, I focus on how more linguistically-informed representations can contribute to the analysis and extraction of evaluation, subjectivity and opinion in text. In particular, I discuss the role of discourse or coherence relations in the interpretation of sentiment. Bio Maite Taboada is Professor of Linguistics at Simon Fraser University in Vancouver (Canada). She holds Licenciatura and PhD degrees from the Universidad Complutense de Madrid (Spain), and an MSc in Computational Linguistics from Carnegie Mellon University (USA). Maite works in the areas of discourse analysis, systemic functional linguistics and computational linguistics, currently focusing on coherence relations in discourse and on sentiment analysis. http://www.sfu.ca/~mtaboada/ Learning Bayesian networks for Multi-Relational Data. Oliver Schulte, Simon Fraser University, Vancouver, Canada Abstract: Many organizations maintain data in databases. Multi-relational databases contain information about entities, attributes of entities, links, and attributes of links. This talk presents methods for applying Bayesian network learning to multi-relational data. Generative graphical models like Bayesian networks support important applications such as information extraction, entity resolution, link-based clustering, link-based outlier detection, query optimization, and others. I describe a scalable parameter learning method, based on the Fast Moebius Transform, that integrates statistical information across multiple tables in the database. For learning the structure of a graphical model I describe a lattice search algorithm, that efficiently searches for probabilistic associations along increasingly longer relational pathways. These methods scale to millions of data records, for instance to data from the Internet Movie Database. Both theoretical arguments and empirical evidence indicate that Bayesian network learning provides excellent estimates of statistical associations in a relational database. Bio: Oliver Schulte is a Professor in the School of Computing Science at Simon Fraser University, Vancouver, Canada. He received his Ph.D. from Carnegie Mellon University in 1997. His current research focuses on machine learning for structured data, such as relational databases and event data. He has published papers in leading AI and machine learning venues on a variety of topics, including learning Bayesian networks, learning theory, game theory, and scientific discovery. While he has won some nice awards, his biggest claim to fame may be a draw against chess world champion Gary Kasparov. http://www.cs.sfu.ca/~oschulte/ Como llegar a la Escuela de Informática de la UNED https://www.google.es/maps/place/UNED:+Escuela+T%C3%A9cnica+Superior +de+Ingenier%C3%ADa+Inform%C3%A1tica/@40.4531597,3.7380099,15z/data=!4m6!1m3!3m2!1s0x0:0x874e55b37d2d193c!2sUNED:+E scuela+T%C3%A9cnica+Superior+de+Ingenier%C3%ADa+Inform%C3%A1tica !3m1!1s0x0:0x874e55b37d2d193c