Paper: Unsupervised Parse Selection for HPSG

ACL ID D10-1068
Title Unsupervised Parse Selection for HPSG
Venue Conference on Empirical Methods in Natural Language Processing
Session Main Conference
Year 2010

Parser disambiguation with precision gram- mars generally takes place via statistical rank- ing of the parse yield of the grammar using a supervised parse selection model. In the standard process, the parse selection model is trained over a hand-disambiguated treebank, meaning that without a significant investment of effort to produce the treebank, parse selec- tion is not possible. Furthermore, as treebank- ing is generally streamlined with parse selec- tion models, creating the initial treebank with- out a model requires more resources than sub- sequent treebanks. In this work, we show that, by taking advantage of the constrained nature of these HPSG grammars, we can learn a dis- criminative parse selection model from raw text in a purely unsupervised fashion. This al- lows us to bootstr...