Learning dictionaries for information extraction by multi-level bootstrapping
AAAI '99/IAAI '99 Proceedings of the sixteenth national conference on Artificial intelligence and the eleventh Innovative applications of artificial intelligence conference innovative applications of artificial intelligence
Automatic labeling of semantic roles
Computational Linguistics
LaTaT: language and text analysis tools
HLT '01 Proceedings of the first international conference on Human language technology research
Inducing information extraction systems for new languages via cross-language projection
COLING '02 Proceedings of the 19th international conference on Computational linguistics - Volume 1
Towards a resource for lexical semantics: a large German corpus with extensive semantic annotation
ACL '03 Proceedings of the 41st Annual Meeting on Association for Computational Linguistics - Volume 1
PARAPHRASE '03 Proceedings of the second international workshop on Paraphrasing - Volume 16
Exploiting paraphrases in a Question Answering system
PARAPHRASE '03 Proceedings of the second international workshop on Paraphrasing - Volume 16
Inducing frame semantic verb classes from WordNet and LDOCE
ACL '04 Proceedings of the 42nd Annual Meeting on Association for Computational Linguistics
Inducing frame semantic verb classes from WordNet and LDOCE
ACL '04 Proceedings of the 42nd Annual Meeting on Association for Computational Linguistics
Frame semantic enhancement of lexical-semantic resources
DeepLA '05 Proceedings of the ACL-SIGLEX Workshop on Deep Lexical Acquisition
Hi-index | 0.00 |
This paper presents SemFrame, a system that automatically induces the names and internal structures of semantic frames. After SemFrame identifies sets of frame-evoking verb synsets, the conceptual density of nodes in the WordNet network for corresponding nouns and noun synsets is computed and analyzed. Conceptually dense nodes are candidates for frame names and frame slots. Ca. 76% of the frame names and 87% of the frame slots generated by SemFrame are rated adequate by human judges.