Inducing deterministic Prolog parsers from treebanks: a machine learning approach
AAAI '94 Proceedings of the twelfth national conference on Artificial intelligence (vol. 1)
Using inductive logic programming to automate the construction of natural language parsers
Using inductive logic programming to automate the construction of natural language parsers
Machine Learning
Building a large annotated corpus of English: the penn treebank
Computational Linguistics - Special issue on using large corpora: II
Unsupervised construction of a multilingual WordNet from parallel corpora
MCTLLL '09 Proceedings of the Workshop on Natural Language Processing Methods and Corpora in Translation, Lexicography, and Language Learning
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There is a history of research focussed on learning of shift-reduce parsers from syntactically annotated corpora by the means of machine learning techniques based on logic. The presence of lexical semantic tags in the treebank has proved useful for learning semantic constraints which limit the amount of nondeterminism in the parsers. The level of generality of the semantic tags used is of direct importance to that task. We combine the ILP system Lapis with the lexical resource WordNet to learn parsers with semantic constraints. The generality of these constraints is automatically selected by Lapis from a number of options provided by the corpus annotator. The performance of the parsers learned is evaluated on an original corpus also described in the article.