IEEE Transactions on Pattern Analysis and Machine Intelligence
Foundations of statistical natural language processing
Foundations of statistical natural language processing
Speech and Language Processing: An Introduction to Natural Language Processing, Computational Linguistics, and Speech Recognition
Squibs and discussions: the DOP Estimation method is biased and inconsistent
Computational Linguistics
Head-driven statistical models for natural language parsing
Head-driven statistical models for natural language parsing
Estimation of probabilistic context-free grammars
Computational Linguistics
Three generative, lexicalised models for statistical parsing
ACL '98 Proceedings of the 35th Annual Meeting of the Association for Computational Linguistics and Eighth Conference of the European Chapter of the Association for Computational Linguistics
Three new probabilistic models for dependency parsing: an exploration
COLING '96 Proceedings of the 16th conference on Computational linguistics - Volume 1
Corpus-based induction of syntactic structure: models of dependency and constituency
ACL '04 Proceedings of the 42nd Annual Meeting on Association for Computational Linguistics
Searching for Part of Speech Tags That Improve Parsing Models
GoTAL '08 Proceedings of the 6th international conference on Advances in Natural Language Processing
Viterbi training improves unsupervised dependency parsing
CoNLL '10 Proceedings of the Fourteenth Conference on Computational Natural Language Learning
Unsupervised induction of tree substitution grammars for dependency parsing
EMNLP '10 Proceedings of the 2010 Conference on Empirical Methods in Natural Language Processing
| Hi-index | 0.00 |
We introduce Probabilistic Constrained W-grammars (PCW-grammars), a two-level formalism capable of capturing grammatical frameworks used in three different state of the art grammar formalism, namely Bilexical Grammars, Markov Rules, and Stochastic Tree Substitution Grammars. For each of them we provide an embedding into PCW-grammars, which allows us to derive properties about their expressive power and consistency, and relations between the formalisms studied.