Year: 2004
Type: conference-paper
Abstract: This paper describes the role of supertagging in a wide-coverage CCG parser which uses a log-linear model to select an analysis. The supertagger reduces the derivation space over which model estimation is performed, reducing the space required for discriminative training. It also dramatically increases the speed of the parser. We show that large increases in speed can be obtained by tightly integrating the supertagger with the CCG grammar and parser. This is the first work we are aware of to successfully integrate a supertagger with a full parser which uses an automatically extracted grammar. We also further reduce the derivation space using constraints on category combination. The result is an accurate wide-coverage CCG parser which is an order of magnitude faster than comparable systems for other linguistically motivated formalisms.
Authors: Stephen Clark, James R. Curran
Institutions: University of Edinburgh, The University of Sydney
Language: English
Subfield: Artificial Intelligence
Field: Computer Science
Domain: Physical Sciences
Open Access status: bronze
OpenAlex ID: W2074729706