Abstract
The training phase is time-consuming for structured learning, especially for supper-tagging tasks. In this paper, we propose an online distributed Passive-Aggression (PA) by averaging parameters for parallel training, which can reduce the training time significantly. We also give theoretic analysis for its convergence. Experimental results show that our method can accelerate the training process significantly with comparable or even better accuracy.
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References
Ando, R.K., Zhang, T.: A framework for learning predictive structures from multiple tasks and unlabeled data. J. Mach. Learn. Res. 6, 1817–1853 (2005)
Zhang, Y., Clark, S.: A fast decoder for joint word segmentation and POS-tagging using a single discriminative model. In: Proceedings of the 2010 Conference on Empirical Methods in Natural Language Processing, EMNLP 2010, pp. 843–852. Association for Computational Linguistics, Stroudsburg (2010)
Crammer, K., Dekel, O., Keshet, J., Shalev-Shwartz, S., Singer, Y.: Online passive-aggressive algorithms. Journal of Machine Learning Research 7, 551–585 (2006)
Rosenblatt, F.: The perceptron: A probabilistic model for information storage and organization in the brain. Psychological Review 65(6), 386–408 (1958)
Collins, M.: Discriminative training methods for hidden markov models: Theory and experiments with perceptron algorithms. In: Proceedings of the 2002 Conference on Empirical Methods in Natural Language Processing (2002)
McDonald, R., Pereira, F., Ribarov, K., Hajič, J.: Non-projective dependency parsing using spanning tree algorithms. In: Proc. of HLT-EMNLP (2005)
Dean, J., Ghemawat, S.: Mapreduce: simplified data processing on large clusters. Commun. ACM 51, 107–113 (2008)
Qiu, X., Zhang, Q., Huang, X.: FudanNLP: A toolkit for Chinese natural language processing. In: Proceedings of ACL (2013)
Jin, C., Chen, X.: The fourth international Chinese language processing bakeoff: Chinese word segmentation, named entity recognition and Chinese pos tagging. In: Sixth SIGHAN Workshop on Chinese Language Processing, p. 69 (2008)
Chu, C.T., Kim, S.K., Lin, Y.A., Ng, A.Y.: Map-reduce for machine learning on multicore. Architecture 19, 281 (2007)
Chang, E.Y., Zhu, K., Wang, H., Bai, H., Li, J., Qiu, Z., Cui, H.: Psvm: Parallelizing support vector machines on distributed computers. Change 20(2), 1–8 (2007)
Cristianini, N., Shawe-Taylor, J.: An introduction to support Vector Machines: and other kernel-based learning methods. Cambridge Univ. Pr. (2000)
Wolfe, J., Haghighi, A., Klein, D.: Fully distributed em for very large datasets. In: Proceedings of the 25th International Conference on Machine Learning, ICML 2008, pp. 1184–1191. ACM, New York (2008)
Chiang, D., Marton, Y., Resnik, P.: Online large-margin training of syntactic and structural translation features. In: Proceedings of the Conference on Empirical Methods in Natural Language Processing, pp. 224–233. Association for Computational Linguistics (2008)
Crammer, K., Singer, Y.: Ultraconservative online algorithms for multiclass problems. Journal of Machine Learning Research 3, 951–991 (2003)
McDonald, R., Hall, K., Mann, G.: Distributed training strategies for the structured perceptron. In: Human Language Technologies: The 2010 Annual Conference of the North American Chapter of the Association for Computational Linguistics, HLT 2010, pp. 456–464. Association for Computational Linguistics, Stroudsburg (2010)
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Zhao, J., Qiu, X., Liu, Z., Huang, X. (2013). Online Distributed Passive-Aggressive Algorithm for Structured Learning. In: Sun, M., Zhang, M., Lin, D., Wang, H. (eds) Chinese Computational Linguistics and Natural Language Processing Based on Naturally Annotated Big Data. NLP-NABD CCL 2013 2013. Lecture Notes in Computer Science(), vol 8202. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-41491-6_12
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DOI: https://doi.org/10.1007/978-3-642-41491-6_12
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-41490-9
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