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Stroudsburg PA: Association for Computational Linguistics."},{"key":"e_1_3_3_101_1","doi-asserted-by":"crossref","unstructured":"Zhou H. Y. Zhang and S. Huang. 2015. \u201cA neural probabilistic structured-prediction model for transition-based dependency parsing.\u201d In Proc. 53rd Annual Meeting of the Association for Computational Linguistics and the 7th Int. Joint Conf. on Natural Language Processing 1213\u20131222. 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