Paper: Combining PCFG-LA Models with Dual Decomposition: A Case Study with Function Labels and Binarization

ACL ID D13-1116
Title Combining PCFG-LA Models with Dual Decomposition: A Case Study with Function Labels and Binarization
Venue Conference on Empirical Methods in Natural Language Processing
Session Main Conference
Year 2013
Authors

It has recently been shown that different NLP models can be effectively combined using dual decomposition. In this paper we demon- strate that PCFG-LA parsing models are suit- able for combination in this way. We exper- iment with the different models which result from alternative methods of extracting a gram- mar from a treebank (retaining or discarding function labels, left binarization versus right binarization) and achieve a labeled Parseval F-score of 92.4 on Wall Street Journal Sec- tion 23 ? this represents an absolute improve- ment of 0.7 and an error reduction rate of 7% over a strong PCFG-LA product-model base- line. Although we experiment only with bina- rization and function labels in this study, there is much scope for applying this approach to other grammar extraction strateg...