Title :
Improving dependency analysis by syntactic parser combination
Author :
Brunet-Manquat, Francis
Author_Institution :
Lab. CLIPS, IMAG, Grenoble, France
fDate :
30 Oct.-1 Nov. 2005
Abstract :
The goal of this article is to present our work about a combination of several syntactic parsers to produce a more robust parser. We have built a platform which allows us to compare syntactic parsers for a given language by splitting their results in elementary pieces, normalizing them, and comparing them with reference results. The same platform is used to combine several parsers to produce a dependency parser that has larger coverage and is more robust than its component parsers. In the future, it should be possible to "compile" the knowledge extracted from several analyzers into an autonomous dependency parser.
Keywords :
computational linguistics; grammars; natural languages; dependency analysis; dependency parser; knowledge extraction; syntactic parser combination; Data analysis; Data mining; Laboratories; Natural languages; Production; Robustness; Speech recognition; Standardization; Tagging; Voting;
Conference_Titel :
Natural Language Processing and Knowledge Engineering, 2005. IEEE NLP-KE '05. Proceedings of 2005 IEEE International Conference on
Print_ISBN :
0-7803-9361-9
DOI :
10.1109/NLPKE.2005.1598826