Cognitive development as optimisation
Computational models of learning
Language (vol.1)
Learnable classes of categorial grammars
Learnable classes of categorial grammars
Statistical Language Learning
Theory of Syntactic Recognition for Natural Languages
Theory of Syntactic Recognition for Natural Languages
Head-driven statistical models for natural language parsing
Head-driven statistical models for natural language parsing
Deterministic parsing of syntactic non-fluencies
ACL '83 Proceedings of the 21st annual meeting on Association for Computational Linguistics
Semi-supervised CCG lexicon extension
EMNLP '11 Proceedings of the Conference on Empirical Methods in Natural Language Processing
Lexical generalization in CCG grammar induction for semantic parsing
EMNLP '11 Proceedings of the Conference on Empirical Methods in Natural Language Processing
| Hi-index | 0.00 |
In this paper we report on an unsupervised approach to learning Categorial Grammar (CG) lexicons. The learner is provided with a set of possible lexical CG categories, the forward and backward application rules of CG and unmarked positive only corpora. Using the categories and rules, the sentences from the corpus are probabilistically parsed. The parses of this example and the set of parses of earlier examples in the corpus are used to build a lexicon and annotate the corpus. We report the results from experiments on two generated corpora and also on the more complicated LLL corpus, that contains examples from subsets of English syntax. These show that the system is able to generate reasonable lexicons and provide accurately parsed corpora in the process. We also discuss ways in which the approach can be scaled up to deal with larger and more diverse corpora.