Paper: Stop-probability estimates computed on a large corpus improve Unsupervised Dependency Parsing

ACL ID P13-1028
Title Stop-probability estimates computed on a large corpus improve Unsupervised Dependency Parsing
Venue Annual Meeting of the Association of Computational Linguistics
Session Main Conference
Year 2013
Authors

Even though the quality of unsupervised dependency parsers grows, they often fail in recognition of very basic dependencies. In this paper, we exploit a prior knowledge of STOP-probabilities (whether a given word has any children in a given direc- tion), which is obtained from a large raw corpus using the reducibility principle. By incorporating this knowledge into Depen- dency Model with Valence, we managed to considerably outperform the state-of-the- art results in terms of average attachment score over 20 treebanks from CoNLL 2006 and 2007 shared tasks.