Dependency Parsing domain adaptation using transductive SVM

Authors:
Antonio Valerio Miceli-Barone;Giuseppe Attardi
Affiliations:
University of Pisa, Pisa, Italy;University of Pisa, Pisa, Italy
Venue:
ROBUS-UNSUP '12 Proceedings of the Joint Workshop on Unsupervised and Semi-Supervised Learning in NLP
Year:
2012

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Abstract

Dependency Parsing domain adaptation involves adapting a dependency parser, trained on an annotated corpus from a given domain (e.g., newspaper articles), to work on a different target domain (e.g., legal documents), given only an unannotated corpus from the target domain. We present a shift/reduce dependency parser that can handle unlabeled sentences in its training set using a transductive SVM as its action selection classifier. We illustrate the the experiments we performed with this parser on a domain adaptation task for the Italian language.