Lexicalization and Generative Power in CCG

Lexicalization and Generative Power in CCG

Marco Kuhlmann, Alexander Koller and Giorgio Satta, Lexicalization and Generative Power in CCG, 2015, Computational linguistics Association for Computational Linguistics (Print), (41), 2, 215-247. http://dx.doi.org/10.1162/COLI_a_00219 Copyright: Massachusetts Institute of Technology Press (MIT Press): STM Titles / MIT Press http://mitpress.mit.edu/main/home/default.asp?sid=19E29805-C0A0-4642-8...

Generative CCG Parsing with OOV Prediction

This paper presents our system for the CIPS-SIGHAN-2014 bakeoff task of Simplified Chinese Parsing (Task 3). The system adopts a generative model with OOV prediction model. The former has a PCFG form while the latter uses a three-layer hierarchical Bayesian model. The final performance on the test corpus is reported together with the performance of the OOV model.

Dependency Trees and the Strong Generative Capacity of CCG

We propose a novel algorithm for extracting dependencies from the derivations of a large fragment of CCG. Unlike earlier proposals, our dependency structures are always tree-shaped. We then use these dependency trees to compare the strong generative capacities of CCG and TAG and obtain surprising results: Both formalisms generate the same languages of derivation trees – but the mechanisms they ...

Statisti al Parsing for CCG with Simple Generative Models

Statistical Parsing for CCG with Simple Generative Models

This paper presents a statistical parser for a wide-coverage Combinatory Categorial Grammar (CCG) derived from the Penn Treebank. The Treebank is translated to a corpus of canonical CCG derivations. We de ne a generative statistical model over CCG derivations and train it on the transformed Treebank. This model is evaluated using Parseval measures and the accuracy of recovery of word-word depen...