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Improved Parsing and POS Tagging Using Inter-Sentence Consistency Constraints
2012
Conference on Empirical Methods in Natural Language Processing
State-of-the-art statistical parsers and POS taggers perform very well when trained with large amounts of in-domain data. When training data is out-of-domain or limited, accuracy degrades. In this paper, we aim to compensate for the lack of available training data by exploiting similarities between test set sentences. We show how to augment sentencelevel models for parsing and POS tagging with inter-sentence consistency constraints. To deal with the resulting global objective, we present an
dblp:conf/emnlp/RushRCG12
fatcat:igcltvl73nb3lo4jons7m6u5mm