Artificial Intelligence

Cross Domain Subjectivity Classification using Multi-View Learning

When:
Monday, December 12, 2011, 4:00pm - 5:00pm PDTiCal
Where:
11th Floor Conf. Room (#1135)
Speaker:
Gael Dias (University of Caen Basse-Normandie, France)
Description:

Abstract:
In this talk, we will present our research on learning models with high cross domain accuracy for subjectivity classification. After a small introduction about related works and challenges of sentiment analysis, we will start by presenting new features for subjectivity analysis. Then, we will present two different paradigms of multi-view learning strategies to learn transfer models: multi-view learning with agreement and guided multi-view learning. Then, we will present an exhaustive evaluation based on both paradigms including two states-of-the-art algorithms and show that accuracy over 91% can be obtained using three views. In our concluding remarks, we will talk about future extensions of the presented methodology. Then, we will briefly present the Human Language Technology team of the GREYC Laboratory of the University of Caen Basse-Normandie (France) and present projects that are being studied ans further prospects.

Biography: Gael Dias is full professor at the University of Caen Basse-Normandie (France). His research interests include unsupervised methodologies for text mining, information retrieval and text summarization. His recent research focuses on Sentiment Analysis, Ontology Learning, Lexical Semantics, Web Personalization and Collaboration, Temporal Information Retrieval, and Paraphrase Extraction and Identification. He has served on program committees of international conferences and workshops such as ACL/HLT 2011, COLING 2010, IJCNLP/ACL 2009, ACL 2007, HLT-NAACL 2007, COLING/ACL 2006 as well as is/was a reviewer for Information Processing and Management, IEEE Transactions on Audio, Speech and Language Processing, Natural Language Engineering Journal, Journal of Language Resources and Evaluation, Journal of Computer Speech and Language and ACM Transactions on Speech and Language Processing.

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