Dependency Parsing Domain Adaptation Using Transductive SVM

Research output: Chapter in Book/Report/Conference proceedingConference contribution

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.
Original languageEnglish
Title of host publicationProceedings of the Joint Workshop on Unsupervised and Semi-Supervised Learning in NLP
Place of PublicationStroudsburg, PA, USA
PublisherAssociation for Computational Linguistics
Pages55-59
Number of pages5
Publication statusPublished - 2012

Publication series

NameROBUS-UNSUP '12
PublisherAssociation for Computational Linguistics

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