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
AAAI'96 Proceedings of the thirteenth national conference on Artificial intelligence - Volume 2
Head-Driven Statistical Models for Natural Language Parsing
Computational Linguistics
History-Based Inside-Outside Algorithm
Proceedings of the 2006 conference on ECAI 2006: 17th European Conference on Artificial Intelligence August 29 -- September 1, 2006, Riva del Garda, Italy
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The performance of PCFGs estimated from tree banks is shown to be sensitive to the particular way in which linguistic constructions are represented as trees in the tree bank. This paper presents a theoretical analysis of the effect of different tree representations for PP attachment on PCFG models, and introduces a new methodology for empirically examining such effects using tree transformations. It shows that one transformation, which copies the label of a parent node onto the labels of its children, can improve the performance of a PCFG model in terms of labelled precision and recall on held out data from 73% (precision) and 69% (recall) to 80% and 79% respectively. It also points out that if only maximum likelihood parses are of interest then many productions can be ignored, since they are subsumed by combinations of other productions in the grammar. In the Penn II tree bank grammar, almost 9% of productions are subsumed in this way.