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Computer Science @ Rochester
Monday, February 16, 2004
11:00 AM
CSB 209
Martha Palmer
U. Pennsylvania
Putting Meaning into Your Trees
The current success of applications of machine learning techniques to tasks such as part-of-speech tagging and parsing has kindled the hope that these same techniques might have equal or greater success in other areas such as lexical semantics. Advances in automated and semi-automated methods of acquiring lexical semantics would release the field from its dependence on well-defined sub-domains and enable broad-coverage natural language processing. However, supervised machine learning requires large amounts of publicly available training data, and a prerequisite for this training data is general agreement on which elements should be tagged and with what tags. With respect to lexical semantics, this type of general agreement has been strikingly elusive.

A recent consensus on a task-oriented level of semantic representation to be layered on top of the existing Penn Treebank syntactic structures has been achieved. This level, know as the Proposition Bank, or PropBank, consists of argument labels for the semantic roles of individual verbs and similar predicating expressions such as participial modifiers and nominalizations. This talk will describe the PropBank verb semantic role annotation being done at Penn for both English and Chinese. The annotation process will be discussed as well as the use of existing lexical resources such as WordNet, Levin classes and VerbNet. Similar projects include the FrameNet Project at Berkeley and the Prague Tectogrammatics project. PropBank annotation is shallower than the Prague Tectogrammatics project and more broad coverage than FrameNet, in that every verb instance in the corpus has to be annotated.

The talk will also briefly describe progress in developing automatic semantic role labelers based on this training data and investigations into the role of sense distinctions in improving performance.