Support Vector Machine Active Learning with Application sto Text Classification
ICML '00 Proceedings of the Seventeenth International Conference on Machine Learning
Building a large annotated corpus of English: the penn treebank
Computational Linguistics - Special issue on using large corpora: II
Supervised grammar induction using training data with limited constituent information
ACL '99 Proceedings of the 37th annual meeting of the Association for Computational Linguistics on Computational Linguistics
Prediction, Learning, and Games
Prediction, Learning, and Games
Non-projective dependency parsing using spanning tree algorithms
HLT '05 Proceedings of the conference on Human Language Technology and Empirical Methods in Natural Language Processing
Online Passive-Aggressive Algorithms
The Journal of Machine Learning Research
Interactive information extraction with constrained conditional random fields
AAAI'04 Proceedings of the 19th national conference on Artifical intelligence
Training dependency parsers by jointly optimizing multiple objectives
EMNLP '11 Proceedings of the Conference on Empirical Methods in Natural Language Processing
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We introduce lightly supervised learning for dependency parsing. In this paradigm, the algorithm is initiated with a parser, such as one that was built based on a very limited amount of fully annotated training data. Then, the algorithm iterates over unlabeled sentences and asks only for a single bit of feedback, rather than a full parse tree. Specifically, given an example the algorithm outputs two possible parse trees and receives only a single bit indicating which of the two alternatives has more correct edges. There is no direct information about the correctness of any edge. We show on dependency parsing tasks in 14 languages that with only 1% of fully labeled data, and light-feedback on the remaining 99% of the training data, our algorithm achieves, on average, only 5% lower performance than when training with fully annotated training set. We also evaluate the algorithm in different feedback settings and show its robustness to noise.