The nature of statistical learning theory
The nature of statistical learning theory
Automatic labeling of semantic roles
Computational Linguistics
Semantic Role Parsing: Adding Semantic Structure to Unstructured Text
ICDM '03 Proceedings of the Third IEEE International Conference on Data Mining
COLING '98 Proceedings of the 17th international conference on Computational linguistics - Volume 1
The necessity of parsing for predicate argument recognition
ACL '02 Proceedings of the 40th Annual Meeting on Association for Computational Linguistics
A maximum entropy approach to FrameNet tagging
NAACL-Short '03 Proceedings of the 2003 Conference of the North American Chapter of the Association for Computational Linguistics on Human Language Technology: companion volume of the Proceedings of HLT-NAACL 2003--short papers - Volume 2
Target word detection and semantic role chunking using support vector machines
NAACL-Short '03 Proceedings of the 2003 Conference of the North American Chapter of the Association for Computational Linguistics on Human Language Technology: companion volume of the Proceedings of HLT-NAACL 2003--short papers - Volume 2
Using predicate-argument structures for information extraction
ACL '03 Proceedings of the 41st Annual Meeting on Association for Computational Linguistics - Volume 1
Use of support vector learning for chunk identification
ConLL '00 Proceedings of the 2nd workshop on Learning language in logic and the 4th conference on Computational natural language learning - Volume 7
Use of deep linguistic features for the recognition and labeling of semantic arguments
EMNLP '03 Proceedings of the 2003 conference on Empirical methods in natural language processing
Identifying semantic roles using Combinatory Categorial Grammar
EMNLP '03 Proceedings of the 2003 conference on Empirical methods in natural language processing
Semantic role labeling using different syntactic views
ACL '05 Proceedings of the 43rd Annual Meeting on Association for Computational Linguistics
Semantic role labeling using dependency trees
COLING '04 Proceedings of the 20th international conference on Computational Linguistics
A global joint model for semantic role labeling
Computational Linguistics
CU-COMSEM: exploring rich features for unsupervised web personal name disambiguation
SemEval '07 Proceedings of the 4th International Workshop on Semantic Evaluations
Semantic role chunking combining complementary syntactic views
CONLL '05 Proceedings of the Ninth Conference on Computational Natural Language Learning
Cross-lingual annotation projection of semantic roles
Journal of Artificial Intelligence Research
Developing an algorithm for mining semantics in texts
CICLing'12 Proceedings of the 13th international conference on Computational Linguistics and Intelligent Text Processing - Volume Part II
| Hi-index | 0.00 |
In this paper, a framework for the development of a fast, accurate, and highly portable semantic chunker is introduced. The framework is based on a non-overlapping, shallow tree-structured language. The derivation of the tree is considered as a sequence of tagging actions in a predefined linguistic context, and a novel semantic chunker is accordingly developed. It groups the phrase chunks into the arguments of a given predicate in a bottom-up fashion. This is quite different from current approaches to semantic parsing or chunking that depend on full statistical syntactic parsers that require tree bank style annotation. We compare it with a recently proposed word-by-word semantic chunker and present results that show that the phrase-by-phrase approach performs better than its word-by-word counterpart.