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
PhraseNet: towards context sensitive lexical semantics
CONLL '03 Proceedings of the seventh conference on Natural language learning at HLT-NAACL 2003 - Volume 4
Evaluating WordNet-based Measures of Lexical Semantic Relatedness
Computational Linguistics
Semantic role labeling via FrameNet, VerbNet and PropBank
ACL-44 Proceedings of the 21st International Conference on Computational Linguistics and the 44th annual meeting of the Association for Computational Linguistics
Adding predicate argument structure to the Penn TreeBank
HLT '02 Proceedings of the second international conference on Human Language Technology Research
Making senses: bootstrapping sense-tagged lists of semantically-related words
CICLing'06 Proceedings of the 7th international conference on Computational Linguistics and Intelligent Text Processing
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
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In recent years, many large-scale semantic resources have been built in the NLP community, but how to apply them in real text semantic parsing is still a big problem. In this paper, we propose a new computational framework to deal with this problem. Its key parts are a lexical semantic ontology (LSO) representation to integrate abundant information contained in current semantic resources, and a LSO schema to automatically reorganize all this semantic knowledge in a hierarchical network. We introduce an algorithm to build the LSO schema by a three-step procedure: to build a knowledge base of lexical relationship, to accumulate all information in it to generate basic LSO nodes, and to build a LSO schema through hierarchical clustering based on different semantic relatedness measures among them. The preliminary experiments have shown promising results to indicate its computability and scaling-up characteristics. We hope it can play an important role in real world semantic computation applications.