Learning grammars for noun phrase extraction by partition search
In: Proceedings of the LREC 2002 workshop on linguistic knowledge acquisition and representation: bootstrapping annotated language data, Las Palmas, Canary Islands, Spain.
This paper describes an application of Grammar Learning by Partition Search to noun phrase extraction, an essential task in information extraction and many other N L P applications. Grammar Learning by Partition Search is a general method for automatically constructing grammars for a range of parsing tasks; it constructs an optimised probabilistic context-free grammar by searching a space of nonterminal set partitions, looking for a partition that maximises parsing performance and minimises grammar size. The idea is that the considerable time and cost involved in building new grammars can be avoided if instead existing grammars can be automatically adapted to new parsing tasks and new domains. This paper presents results for applying Partition Search to the tasks of (i) identifying flat N P chunks, and (ii) identifying all N Ps in a text. For N P chunking, Partition Search improves a general baseline result by 12.7%, and a method- specific baseline by 2.2%. For N P identification, Partition Search improves the general baseline by 21.45%, and the method-specific one by 3.48%. Even though the grammars are nonlexicalised, results for N P identification closely match the best existing results for lexicalised approaches.
Actions (login required)
Downloads per month over past year