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Fecha : 3 Noviembre 2015, Horario: 9,30 a 13h.
Lugar: Sala J.Mira, ETSI Informática de la UNED, 4 planta.
Enlace para el streaming.
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
On why discourse is good for sentiment analysis
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.
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
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.
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