The nature of statistical learning theory
The nature of statistical learning theory
Making large-scale support vector machine learning practical
Advances in kernel methods
Class-Based Construction of a Verb Lexicon
Proceedings of the Seventeenth National Conference on Artificial Intelligence and Twelfth Conference on Innovative Applications of Artificial Intelligence
Automatic verb classification based on statistical distributions of argument structure
Computational Linguistics
A maximum-entropy-inspired parser
NAACL 2000 Proceedings of the 1st North American chapter of the Association for Computational Linguistics conference
Investigating regular sense extensions based on intersective Levin classes
COLING '98 Proceedings of the 17th international conference on Computational linguistics - Volume 1
Automatic labeling of semantic roles
ACL '00 Proceedings of the 38th Annual Meeting on Association for Computational Linguistics
Support Vector Learning for Semantic Argument Classification
Machine Learning
A study on convolution kernels for shallow semantic parsing
ACL '04 Proceedings of the 42nd Annual Meeting on Association for Computational Linguistics
Shallow Semantic Parsing Based on FrameNet, VerbNet and PropBank
Proceedings of the 2006 conference on ECAI 2006: 17th European Conference on Artificial Intelligence August 29 -- September 1, 2006, Riva del Garda, Italy
Introduction to the CoNLL-2005 shared task: semantic role labeling
CONLL '05 Proceedings of the Ninth Conference on Computational Natural Language Learning
Putting pieces together: combining FrameNet, VerbNet and WordNet for robust semantic parsing
CICLing'05 Proceedings of the 6th international conference on Computational Linguistics and Intelligent Text Processing
A Computational Framework to Integrate Different Semantic Resources
TSD '08 Proceedings of the 11th international conference on Text, Speech and Dialogue
Shallow Semantic Parsing Based on FrameNet, VerbNet and PropBank
Proceedings of the 2006 conference on ECAI 2006: 17th European Conference on Artificial Intelligence August 29 -- September 1, 2006, Riva del Garda, Italy
Kernel-based relation extraction from investigative data
Proceedings of The Third Workshop on Analytics for Noisy Unstructured Text Data
New features for FrameNet: WordNet mapping
CoNLL '09 Proceedings of the Thirteenth Conference on Computational Natural Language Learning
Coreference systems based on kernels methods
COLING '08 Proceedings of the 22nd International Conference on Computational Linguistics - Volume 1
Syntactic and semantic kernels for short text pair categorization
EACL '09 Proceedings of the 12th Conference of the European Chapter of the Association for Computational Linguistics
Syntactic Structural Kernels for Natural Language Interfaces to Databases
ECML PKDD '09 Proceedings of the European Conference on Machine Learning and Knowledge Discovery in Databases: Part I
Encoding tree pair-based graphs in learning algorithms: the textual entailment recognition case
TextGraphs-3 Proceedings of the 3rd Textgraphs Workshop on Graph-Based Algorithms for Natural Language Processing
Semantic structure from correspondence analysis
TextGraphs-3 Proceedings of the 3rd Textgraphs Workshop on Graph-Based Algorithms for Natural Language Processing
A comparative study on generalization of semantic roles in FrameNet
ACL '09 Proceedings of the Joint Conference of the 47th Annual Meeting of the ACL and the 4th International Joint Conference on Natural Language Processing of the AFNLP: Volume 1 - Volume 1
Kernel-Based Learning for Domain-Specific Relation Extraction
AI*IA '09: Proceedings of the XIth International Conference of the Italian Association for Artificial Intelligence Reggio Emilia on Emergent Perspectives in Artificial Intelligence
Wikipedia as frame information repository
EMNLP '09 Proceedings of the 2009 Conference on Empirical Methods in Natural Language Processing: Volume 1 - Volume 1
Convolution kernels on constituent, dependency and sequential structures for relation extraction
EMNLP '09 Proceedings of the 2009 Conference on Empirical Methods in Natural Language Processing: Volume 3 - Volume 3
Cross-lingual annotation projection of semantic roles
Journal of Artificial Intelligence Research
Syntactic and semantic structure for opinion expression detection
CoNLL '10 Proceedings of the Fourteenth Conference on Computational Natural Language Learning
Automatic discovery of manner relations and its applications
EMNLP '10 Proceedings of the 2010 Conference on Empirical Methods in Natural Language Processing
Kernel engineering for fast and easy design of natural language applications
COLING '10 Proceedings of the 23rd International Conference on Computational Linguistics: Kernel Engineering for Fast and Easy Design of Natural Language Applications
Unified Semantic Role Labeling for Verbal and Nominal Predicates in the Chinese Language
ACM Transactions on Asian Language Information Processing (TALIP)
Structured lexical similarity via convolution kernels on dependency trees
EMNLP '11 Proceedings of the Conference on Empirical Methods in Natural Language Processing
Semantic mapping between natural language questions and SQL queries via syntactic pairing
NLDB'09 Proceedings of the 14th international conference on Applications of Natural Language to Information Systems
Structural relationships for large-scale learning of answer re-ranking
SIGIR '12 Proceedings of the 35th international ACM SIGIR conference on Research and development in information retrieval
Verb classification using distributional similarity in syntactic and semantic structures
ACL '12 Proceedings of the 50th Annual Meeting of the Association for Computational Linguistics: Long Papers - Volume 1
Natural language opinion search on blogs
PRICAI'12 Proceedings of the 12th Pacific Rim international conference on Trends in Artificial Intelligence
Hi-index | 0.00 |
This article describes a robust semantic parser that uses a broad knowledge base created by interconnecting three major resources: FrameNet, VerbNet and PropBank. The FrameNet corpus contains the examples annotated with semantic roles whereas the VerbNet lexicon provides the knowledge about the syntactic behavior of the verbs. We connect VerbNet and FrameNet by mapping the FrameNet frames to the VerbNet Intersective Levin classes. The PropBank corpus, which is tightly connected to the VerbNet lexicon, is used to increase the verb coverage and also to test the effectiveness of our approach. The results indicate that our model is an interesting step towards the design of more robust semantic parsers.