Abstract: 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.
Biographical information: Martha Palmer is an Associate Professor in the Computer and Information Sciences Department of the University of Pennsylvania, but will be moving to the University of Colorado in the summer of 2005. She has been a member of the Advisory Committee for the DARPA TIDES program, the Chair of SIGLEX, the Chair of SIGHAN, and is now President of the Association for Computational Linguistics. Her early work on lexically based semantic interpretation formed the basis of the successful DARPA-funded message processing system, Pundit, and fostered a continuing interest in Information Extraction (ACE) and Machine Translation (TIDES). Her interest in lexical semantics and verb classes also led to her involvement in SENSEVAL and the development of English VerbNet and the English, Chinese and Korean Proposition Banks.
Homepage: http://www.cis.upenn.edu/~mpalmer
From the home page, follow the publications link to download the following papers relevant to the talk:
# Martha Palmer, Dan Gildea, Paul Kingsbury, The Proposition Bank: An Annotated Corpus of Semantic Roles, submitted to Computational Linguistics, December, 2003.
# Martha Palmer, Hoa Dang and Christiane Fellbaum, Making Fine-grained and Coarse-grained sense distinctions, both manually and automatically, Journal of Natural Language Engineering, submitted
Shorter versions:
Dan Gildea and Martha Palmer, The Necessity of Parsing for Predicate Argument Recognition, The 40th Meeting of the Association for Computational Linguistics, ACL-02, Philadelphia, PA, July 7-12, 2002.
Christiane Fellbaum, Martha Palmer, Hoa Trang Dang, Lauren Delfs, Susannc Wolf. Manual and Automatic Semantic Annotation with WordNet, SIGLEX Workshop on WordNet and other Lexical Resources (NAACL-01), Invited Talk, 8 pg., Pittsburgh. PA, June 2,3, 2001.
Hoa Trang Dang, Karin Kipper, Martha Palmer, Joseph Rosenzweig,
Investigating regular sense extensions based on intersective Levin classes.
Coling/ACL-98, 36th Association of Computational Linguistics Conference,
pp 293-300, Montreal CA, August 11-17, 1998.