The syntactic process
Coupling CCG and hybrid logic dependency semantics
ACL '02 Proceedings of the 40th Annual Meeting on Association for Computational Linguistics
Discriminative language modeling with conditional random fields and the perceptron algorithm
ACL '04 Proceedings of the 42nd Annual Meeting on Association for Computational Linguistics
Wide-coverage efficient statistical parsing with ccg and log-linear models
Computational Linguistics
How the statistical revolution changes (computational) linguistics
ILCL '09 Proceedings of the EACL 2009 Workshop on the Interaction between Linguistics and Computational Linguistics: Virtuous, Vicious or Vacuous?
Probabilistic models for disambiguation of an HPSG-based chart generator
Parsing '05 Proceedings of the Ninth International Workshop on Parsing Technology
Perceptron reranking for CCG realization
EMNLP '09 Proceedings of the 2009 Conference on Empirical Methods in Natural Language Processing: Volume 1 - Volume 1
Optimising incremental dialogue decisions using information density for interactive systems
EMNLP-CoNLL '12 Proceedings of the 2012 Joint Conference on Empirical Methods in Natural Language Processing and Computational Natural Language Learning
| Hi-index | 0.00 |
This paper shows that using linguistically motivated features for English that-complementizer choice in an averaged perceptron model for classification can improve upon the prediction accuracy of a state-of-the-art realization ranking model. We report results on a binary classification task for predicting the presence/absence of a that-complementizer using features adapted from Jaeger's (2010) investigation of the uniform information density principle in the context of that-mentioning. Our experiments confirm the efficacy of the features based on Jaeger's work, including information density--based features. The experiments also show that the improvements in prediction accuracy apply to cases in which the presence of a that-complementizer arguably makes a substantial difference to fluency or intelligiblity. Our ultimate goal is to improve the performance of a ranking model for surface realization, and to this end we conclude with a discussion of how we plan to combine the local complementizer-choice features with those in the global ranking model.