In this work, we propose to use distributed word representations in a greedy, transition-based dependency parsing framework. Instead of using a very large number of sparse indicator features, the multinomial logistic regression classifier employed by the parser learns and uses a small number of dense features, therefore it can work very fast. The distributed word representations are produced by a continuous skip-grammodel using a neural network architecture. Experiments on a Vietnamese dependency treebank show that the parser not only works faster but also achieves better accuracy in comparison to a conventional transition-based dependency parser.
CITATION STYLE
Le-Hong, P., Nguyen, T. M. H., Nguyen, T. L., & Ha, M. L. (2015). Fast dependency parsing using distributed word representations. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 9441, pp. 261–272). Springer Verlag. https://doi.org/10.1007/978-3-319-25660-3_22
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