Communications of the ACM - Special issue on parallelism
Forgetting Exceptions is Harmful in Language Learning
Machine Learning - Special issue on natural language learning
Head-driven statistical models for natural language parsing
Head-driven statistical models for natural language parsing
A non-projective dependency parser
ANLC '97 Proceedings of the fifth conference on Applied natural language processing
Parsing with the shortest derivation
COLING '00 Proceedings of the 18th conference on Computational linguistics - Volume 1
Concurrent lexicalized dependency parsing: the ParseTalk model
COLING '94 Proceedings of the 15th conference on Computational linguistics - Volume 1
TüSBL: a similarity-based chunk parser for robust syntactic processing
HLT '01 Proceedings of the first international conference on Human language technology research
The Penn Treebank: annotating predicate argument structure
HLT '94 Proceedings of the workshop on Human Language Technology
Statistical parsing with a context-free grammar and word statistics
AAAI'97/IAAI'97 Proceedings of the fourteenth national conference on artificial intelligence and ninth conference on Innovative applications of artificial intelligence
Parsing Ill-Formed Inputs with Constraint Graphs
CICLing '02 Proceedings of the Third International Conference on Computational Linguistics and Intelligent Text Processing
From shallow to deep parsing using constraint satisfaction
COLING '02 Proceedings of the 19th international conference on Computational linguistics - Volume 1
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Chunk parsing has focused on the recognition of partial constituent structures at the level of individual chunks. Little attention has been paid to the question of how such partial analyses can be combined into larger structures for complete utterances. Such larger structures are not only desirable for a deeper syntactic analysis. They also constitute a necessary prerequisite for assigning function-argument structure.The present paper offers a similarity-based algorithm for assigning functional labels such as subject, object, head, complement, etc. to complete syntactic structures on the basis of prechunked input.The evaluation of the algorithm has concentrated on measuring the quality of functional labels. It was performed on a German and an English treebank using two different annotation schemes at the level of function-argument structure. The results of 89.73 % correct functional labels for German and 90.40 % for English validate the general approach.