From grammar to lexicon: unsupervised learning of lexical syntax
Computational Linguistics - Special issue on using large corpora: II
Automatic extraction of subcategorization from corpora
ANLC '97 Proceedings of the fifth conference on Applied natural language processing
Automatic acquisition of a large subcategorization dictionary from corpora
ACL '93 Proceedings of the 31st annual meeting on Association for Computational Linguistics
Using predicate-argument structures for information extraction
ACL '03 Proceedings of the 41st Annual Meeting on Association for Computational Linguistics - Volume 1
Statistical filtering and subcategorization frame acquisition
EMNLP '00 Proceedings of the 2000 Joint SIGDAT conference on Empirical methods in natural language processing and very large corpora: held in conjunction with the 38th Annual Meeting of the Association for Computational Linguistics - Volume 13
Using unknown word techniques to learn known words
EMNLP '10 Proceedings of the 2010 Conference on Empirical Methods in Natural Language Processing
Investigating the cross-linguistic potential of VerbNet: style classification
COLING '10 Proceedings of the 23rd International Conference on Computational Linguistics
Acquisition of unknown word paradigms for large-scale grammars
COLING '10 Proceedings of the 23rd International Conference on Computational Linguistics: Posters
Learning syntactic verb frames using graphical models
ACL '12 Proceedings of the 50th Annual Meeting of the Association for Computational Linguistics: Long Papers - Volume 1
Classifying French verbs using French and English lexical resources
ACL '12 Proceedings of the 50th Annual Meeting of the Association for Computational Linguistics: Long Papers - Volume 1
Methodological Review: Approaches to verb subcategorization for biomedicine
Journal of Biomedical Informatics
| Hi-index | 0.00 |
This paper presents a system capable of automatically acquiring subcategorization frames (SCFs) for French verbs from the analysis of large corpora. We applied the system to a large newspaper corpus (consisting of 10 years of the French newspaper 'Le Monde') and acquired subcategorization information for 3267 verbs. The system learned 286 SCF types for these verbs. From the analysis of 25 representative verbs, we obtained 0.82 precision, 0.59 recall and 0.69 F-measure. These results are comparable with those reported in recent related work.