An All-Subtrees Approach to Unsupervised Parsing

We investigate generalizations of the allsubtrees "DOP" approach to unsupervised parsing. Unsupervised DOP models assign all possible binary trees to a set of sentences and next use (a large random subset of) all subtrees from these binary trees to compute the most probable parse trees. We will test both a relative frequency estimator for unsupervised DOP and a maximum likelihood estimator which is known to be statistically consistent. We report state-ofthe-art results on English (WSJ), German (NEGRA) and Chinese (CTB) data. To the best of our knowledge this is the first paper which tests a maximum likelihood estimator for DOP on the Wall Street Journal, leading to the surprising result that an unsupervised parsing model beats a widely used supervised model (a treebank PCFG).

• Type: Conference
• Year: 2006
• Where: ACL
• Authors: Rens Bod
• Keywords: ACL 2006, ACL 2007, Maximum Likelihood Estimator, Unsupervised Dop, Unsupervised Parsing