Algorithms on strings, trees, and sequences: computer science and computational biology
Algorithms on strings, trees, and sequences: computer science and computational biology
Foundations of statistical natural language processing
Foundations of statistical natural language processing
The String-to-String Correction Problem
Journal of the ACM (JACM)
Data-Oriented Parsing
A stochastic parts program and noun phrase parser for unrestricted text
ANLC '88 Proceedings of the second conference on Applied natural language processing
Tree-gram parsing lexical dependencies and structural relations
ACL '00 Proceedings of the 38th Annual Meeting on Association for Computational Linguistics
The Proposition Bank: An Annotated Corpus of Semantic Roles
Computational Linguistics
Introduction to the CoNLL-2005 shared task: semantic role labeling
CONLL '05 Proceedings of the Ninth Conference on Computational Natural Language Learning
A joint model for semantic role labeling
CONLL '05 Proceedings of the Ninth Conference on Computational Natural Language Learning
Generalized inference with multiple semantic role labeling systems
CONLL '05 Proceedings of the Ninth Conference on Computational Natural Language Learning
Semantic role labeling as sequential tagging
CONLL '05 Proceedings of the Ninth Conference on Computational Natural Language Learning
Semantic role chunking combining complementary syntactic views
CONLL '05 Proceedings of the Ninth Conference on Computational Natural Language Learning
Semantic role labeling using complete syntactic analysis
CONLL '05 Proceedings of the Ninth Conference on Computational Natural Language Learning
Applying spelling error correction techniques for improving semantic role labelling
CONLL '05 Proceedings of the Ninth Conference on Computational Natural Language Learning
A text-based decision support system for financial sequence prediction
Decision Support Systems
| Hi-index | 12.05 |
A two-phase annotation method for semantic labeling in natural language processing is proposed. The dynamic programming approach stresses on a non-exact string matching which takes full advantage of the underlying grammatical structure of the parse trees in a Treebank. The first phase of the labeling is a coarse-grained syntactic parsing which is complementary to a semantic dissimilarities analysis in its latter phase. The approach goes beyond shallow parsing to a deeper level of case role identification, while preserving robustness, without being bogged down into a complete linguistic analysis. The paper presents experimental results for recognizing more than 50 different semantic labels in 10,000 sentences. Results show that the approach improves the labeling, even though with incomplete information. Detailed evaluations are discussed in order to justify its significances.