Paper: Dependency Parsing and Domain Adaptation with LR Models and Parser Ensembles

ACL ID D07-1111
Title Dependency Parsing and Domain Adaptation with LR Models and Parser Ensembles
Venue Conference on Empirical Methods in Natural Language Processing
Session Main Conference
Year 2007
Authors
  • Kenji Sagae (University of Tokyo, Tokyo Japan)
  • Jun'ichi Tsujii (University of Tokyo, Tokyo Japan; University of Manchester, Manchester UK; National Center for Text Mining, UK)

We present a data-driven variant of the LR algorithm for dependency parsing, and ex- tend it with a best-first search for probabil- istic generalized LR dependency parsing. Parser actions are determined by a classifi- er, based on features that represent the cur- rent state of the parser. We apply this pars- ing framework to both tracks of the CoNLL 2007 shared task, in each case taking ad- vantage of multiple models trained with different learners. In the multilingual track, we train three LR models for each of the ten languages, and combine the analyses obtained with each individual model with a maximum spanning tree voting scheme. In the domain adaptation track, we use two models to parse unlabeled data in the target domain to supplement the labeled out-of- domain training set, in a sch...