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EMNLP
2007

Dependency Parsing and Domain Adaptation with LR Models and Parser Ensembles

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Dependency Parsing and Domain Adaptation with LR Models and Parser Ensembles
We present a data-driven variant of the LR algorithm for dependency parsing, and extend it with a best-first search for probabilistic generalized LR dependency parsing. Parser actions are determined by a classifier, based on features that represent the current state of the parser. We apply this parsing framework to both tracks of the CoNLL 2007 shared task, in each case taking advantage 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-ofdomain training set, in a scheme similar to one iteration of co-training.
Kenji Sagae, Jun-ichi Tsujii
Added 29 Oct 2010
Updated 29 Oct 2010
Type Conference
Year 2007
Where EMNLP
Authors Kenji Sagae, Jun-ichi Tsujii
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