Abstract
In this paper we propose a neural network based classifier voting approach to dependency parsing using multiple classifiers as component systems in an ensemble and a neural network algorithm as an oracle. We show significant improvements over the best component systems for both transition-based and graph-based dependency parsing. We also investigate different weighting schemes for voting among individual classifiers in the ensemble. All our experiments were conducted on Hindi and Telugu language data but the approach is language-independent.
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Notes
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LAS: Labeled Accuracy Score; UAS: Unlabled Accuracy Score; LS: Labeled Accuracy score.
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Kanneganti, S., Mujadia, V., Sharma, D.M. (2018). Classifier Ensemble Approach to Dependency Parsing. In: Gelbukh, A. (eds) Computational Linguistics and Intelligent Text Processing. CICLing 2017. Lecture Notes in Computer Science(), vol 10761. Springer, Cham. https://doi.org/10.1007/978-3-319-77113-7_13
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