Probabilistic reasoning in intelligent systems: networks of plausible inference
Probabilistic reasoning in intelligent systems: networks of plausible inference
Introduction to Bayesian Networks
Introduction to Bayesian Networks
Corpus-Driven Unsupervised Learning of Verb Subcategorization Frames
AI*IA '97 Proceedings of the 5th Congress of the Italian Association for Artificial Intelligence on Advances in Artificial Intelligence
Accurate methods for the statistics of surprise and coincidence
Computational Linguistics - Special issue on using large corpora: I
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
COLING '98 Proceedings of the 17th international conference on Computational linguistics - Volume 1
Automatic acquisition of a large subcategorization dictionary from corpora
ACL '93 Proceedings of the 31st annual meeting on Association for Computational Linguistics
Japanese case structure analysis by unsupervised construction of a case frame dictionary
COLING '00 Proceedings of the 18th conference on Computational linguistics - Volume 1
Automatic extraction of subcategorization frames for Czech
COLING '00 Proceedings of the 18th conference on Computational linguistics - Volume 2
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Learning Bayesian Belief Networks from corpora has been applied to the automatic acquisition of verb subcategorization frames for Modern Greek (MG). We are incorporating minimal linguistic resources, i.e. morphological tagging and phrase chunking, since a general-purpose syntactic parser for MG is currently unavailable. Comparative experimental results have been evaluated against Naive Bayes classification, which is based on the conditional independence assumption along with two widely used methods, Log-Likelihood (LLR) and Relative Frequencies Threshold (RFT). We have experimented with a balanced corpus in order to assure unbiased behavior of the training model. Results have depicted that obtaining the inferential dependencies of the training data could lead to a precision improvement of about 4% compared to that of Naive Bayes and 7% compared to LLR and RFT Moreover, we have been able to achieve a precision exceeding 87% on the identification of subcategorization frames which are not known beforehand, while limited training data are proved to endow with satisfactory results.