CICLing '07 Proceedings of the 8th International Conference on Computational Linguistics and Intelligent Text Processing
How context and semantic information can help a machine learning system?
MICAI'07 Proceedings of the artificial intelligence 6th Mexican international conference on Advances in artificial intelligence
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Automatic acquisition of lexical knowledge is critical to a wide range of natural language processing tasks. Verb knowledge is especially important in semantic parsing. Verbs denote relational information of lexicogrammar and semantically state the participants and event involved in the meaning construed. This paper describes a statistical distribution approach to reuse and integrate information from the Suggested Upper Merged Ontology (SUMO), WordNet and FrameNet. The mapping between word-meanings, frame-semantics and world concepts suggests a heuristic approach for linking WordNet verbs and FrameNet frames providing a knowledge base for Semantic Role Labeling(SRL), identifying the appropriate range of possible semantic roles with respect to the event evoked by verb. This is accomplished through the verbs covered by both FrameNet and WordNet, taking the shared lexical knowledge as learning data to map SUMO concepts with FrameNet frames. The exploitation of the mapping aims at automatic populating WordNet data to FrameNet frames constructing a knowledge base for semantic parsing.