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Guest lecture by Paola Merlo Geneve


One of the currently debated topics in Natural Language Processing is the problem of semantic role labelling. Given a sentence like "I want to reserve a flight from Geneva to Gotenborg'', how do we determine automatically that "from Geneva'' is the source of the flight and "to Gotenborg'' is the destination? The solution to this problem lies at the heart of all applications that require language understanding, such as dialogue systems, question answering, or machine translation. Recent successes of machine learning methods in statistical parsing and lexical acquisition pave the way to a learning approach for this problem too. In this talk, I will report experiments that explore learning of syntactic and semantic representations. First, we extend a state-of-the-art statistical parser to produce a richly annotated tree that identifies and labels nodes with semantic role labels as well as syntactic labels. Secondly, we explore rule-based and learning techniques to extract predicate-argument structures from this enriched output. The learning method is competitive with previous single-system proposals for semantic role labelling, yields the best reported precision, and produces a rich output. In combination with other high-recall semantic role labelling systems it yields an F-measure of 81%.

Date: 2008-05-30 15:15 - 17:00

Location: Humanisten F236


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