1. Using google N-Gram corpus. http://googlesystem.blogspot.com/2008/05/using-googles-n-gram-corpus.html
2. M. Acharya, T. Xie, J. Pei, J. Xu, Mining api patterns as partial orders from source code: from usage scenarios to specifications, in ESEC-FSE ’07: Proceedings of the 6th Joint Meeting of the European Software Engineering Conference and the ACM SIGSOFT Symposium on the Foundations of Software Engineering, (ACM, 2007), pp. 25–34
3. E. Arisoy, T.N. Sainath, B. Kingsbury, B. Ramabhadran, Deep neural network language models, in Proceedings of the NAACL-HLT 2012 Workshop: Will We Ever Really Replace the N-gram Model? On the Future of Language Modeling for HLT, WLM ’12, (Association for Computational Linguistics, 2012), pp. 20–28
4. Y. Bengio, R. Ducharme, P. Vincent, C. Janvin, A neural probabilistic language model. J. Mach. Learn. Res. 3, 1137–1155 (2003)
5. M. Bruch, M. Monperrus, M. Mezini, Learning from examples to improve code completion systems, in Proceedings of the the 7th Joint Meeting of the European Software Engineering Conference and the ACM SIGSOFT Symposium on The Foundations of Software Engineering, ESEC/FSE ’09, (ACM, 2009), pp. 213–222