Paper: K-Best Combination of Syntactic Parsers

ACL ID D09-1161
Title K-Best Combination of Syntactic Parsers
Venue Conference on Empirical Methods in Natural Language Processing
Session Main Conference
Year 2009

In this paper, we propose a linear model-based general framework to combine k-best parse outputs from multiple parsers. The proposed framework leverages on the strengths of pre- vious system combination and re-ranking techniques in parsing by integrating them into a linear model. As a result, it is able to fully utilize both the logarithm of the probability of each k-best parse tree from each individual parser and any additional useful features. For feature weight tuning, we compare the simu- lated-annealing algorithm and the perceptron algorithm. Our experiments are carried out on both the Chinese and English Penn Treebank syntactic parsing task by combining two state- of-the-art parsing models, a head-driven lexi- calized model and a latent-annotation-based un-lexicalized m...