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.
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.
Original language | English |
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Title of host publication | Proceedings of the Joint Workshop on Unsupervised and Semi-Supervised Learning in NLP |
Place of Publication | Stroudsburg, PA, USA |
Publisher | Association for Computational Linguistics |
Pages | 55-59 |
Number of pages | 5 |
Publication status | Published - 2012 |
Publication series
Name | ROBUS-UNSUP '12 |
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Publisher | Association for Computational Linguistics |