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Culotta and J. Sorensen, \u201cDependency tree kernels for relation extraction,\u201d Meeting of the Association for Computational Linguistics, Barcelona, Spain, DBLP, pp.423-429, July 2004. 10.3115\/1218955.1219009","DOI":"10.3115\/1218955.1219009"},{"key":"5","doi-asserted-by":"crossref","unstructured":"[5] R.C. Bunescu and R.J. Mooney, \u201cA shortest path dependency kernel for relation extraction,\u201d Conference on Human Language Technology and Empirical Methods in Natural Language Processing, Association for Computational Linguistics, pp.724-731, 2005. 10.3115\/1220575.1220666","DOI":"10.3115\/1220575.1220666"},{"key":"6","doi-asserted-by":"crossref","unstructured":"[6] T.-V.T. Nguyen, A. Moschitti, and G. 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Guo, \u201cGenerating text with deep reinforcement learning,\u201d Computer Science, vol.40, no.4, pp.1-5, 2015."},{"key":"10","doi-asserted-by":"crossref","unstructured":"[10] J. He, J. Chen, X. He, J. Gao, L. Li, L. Deng, and M. Ostendorf,\u201cDeep reinforcement learning with a natural language action space,\u201d Meeting of the Association for Computational Linguistics, pp.1621-1630, 2016. 10.18653\/v1\/p16-1153","DOI":"10.18653\/v1\/P16-1153"},{"key":"11","doi-asserted-by":"publisher","unstructured":"[11] S. Hochreiter and J. Schmidhuber, \u201cLong short-term memory,\u201d Neural Computation, vol.9, no.8, pp.1735-1780, 1997. 10.1162\/neco.1997.9.8.1735","DOI":"10.1162\/neco.1997.9.8.1735"},{"key":"12","doi-asserted-by":"crossref","unstructured":"[12] K.S. Tai, R. Socher, and C.D. 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