A sequential algorithm for training text classifiers
SIGIR '94 Proceedings of the 17th annual international ACM SIGIR conference on Research and development in information retrieval
Coping with syntactic ambiguity or how to put the block in the box on the table
Computational Linguistics
Active learning for statistical natural language parsing
ACL '02 Proceedings of the 40th Annual Meeting on Association for Computational Linguistics
Sample Selection for Statistical Parsing
Computational Linguistics
Online large-margin training of dependency parsers
ACL '05 Proceedings of the 43rd Annual Meeting on Association for Computational Linguistics
Online Passive-Aggressive Algorithms
The Journal of Machine Learning Research
Dependency Parsing
On the complexity of non-projective data-driven dependency parsing
IWPT '07 Proceedings of the 10th International Conference on Parsing Technologies
Parsing '05 Proceedings of the Ninth International Workshop on Parsing Technology
Efficient third-order dependency parsers
ACL '10 Proceedings of the 48th Annual Meeting of the Association for Computational Linguistics
Using smaller constituents rather than sentences in active learning for Japanese dependency parsing
ACL '10 Proceedings of the 48th Annual Meeting of the Association for Computational Linguistics
Very high accuracy and fast dependency parsing is not a contradiction
COLING '10 Proceedings of the 23rd International Conference on Computational Linguistics
Semi-supervised dependency parsing using lexical affinities
ACL '12 Proceedings of the 50th Annual Meeting of the Association for Computational Linguistics: Long Papers - Volume 1
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Current successful probabilistic parsers require large treebanks which are difficult, time consuming, and expensive to produce. Some parts of these data do not contain any useful information for training a parser. Active learning strategies allow to select the most informative samples for annotation. Most existing active learning strategies for parsing rely on selecting uncertain sentences for annotation. We show in this paper that selecting full sentences is not an optimal solution and propose a way to select only subparts of sentences.