Guest Speaker Ivan Titov

2015/12/23

Guest Speaker Ivan Titov

On Tuesday, January 19th 2016, Prof. Dr. Ivan Titov (University of Amsterdam, Netherlands ) will give a guest lecture at 11:30 in S02|02 B002 (Hochschulstr. 10).

Title: Learning Shallow Semantics with Little or No Supervision

Abstract:

Inducing meaning representations from text is one of the key objectives of natural language processing. Most existing statistical semantic analyzers rely on large human-annotated datasets, which are expensive to create and exist only for a very limited number of languages. Even then, they are not very robust, cover only a small proportion of semantic constructions appearing in the labeled data, and are domain-dependent. We investigate approaches which do not use any labeled data but induce shallow semantic representations (i.e. semantic roles and frames) from unannotated texts. Unlike semantically-annotated data, unannotated texts are plentiful and available for many languages and many domains which makes our approach particularly promising. I will contrast the generative framework (incl. our non-parametric Bayesian model) and a new approach called reconstruction-error minimization (REM) for semantics. Unlike the more traditional generative framework, REM lets us effectively train expressive feature-rich models in an unsupervised way. Moreover, it allows us to specialize our representations to be useful for (basic forms of) semantic inference. We show that REM achieves state-of-the-art results on the unsupervised semantic role labeling task (across languages without any language-specific tuning) and significantly outperforms generative counterparts on the unsupervised relation discovery task.

Joint work with Ehsan Khoddam, Alex Klementiev and Diego Marcheggiani.

Bio:

Ivan Titov joined the faculty of the University of Amsterdam in April 2013. Before that he was at the Saarland University as a junior faculty and head of a research group (2009 – 2013), following a postdoc at the University of Illinois at Urbana-Champaign. He received his Ph.D. in Computer Science from the University of Geneva in 2008 and his master's degree in Applied Mathematics from the St. Petersburg State Polytechnic University (Russia) in 2003.

His research interests are in statistical natural language processing (models of syntax, semantics and sentiment) and machine learning (structured prediction methods, latent variable models, Bayesian methods).

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