Intelligent sentence completion based on global context dependent recurrent neural network language model

Author:

Yang Tao1,Deng Hongli1

Affiliation:

1. China West Normal University

Funder

China West Normal University Deep Learning Innovation Team Project

Sichuan Provincial Department of Education Key Project

Publisher

ACM Press

Reference18 articles.

1. Woods A (2016). Exploiting linguistic features for sentence completion. Proceedings of the 54th Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers), 2, 438--442.

2. Zweig G, Platt J C, Meek C, et al. (2012). Computational approaches to sentence completion. Proceedings of the 50th Annual Meeting of the Association for Computational Linguistics, Long Papers, Volume 1, 601--610.

3. Mnih A and Teh Y W (2012). A fast and simple algorithm for training neural probabilistic language models. arXiv preprint arXiv:1206.6426.

4. Mikolov T, Chen K, Corrado G, et al. (2013). Efficient estimation of word representations in vector space. arXiv preprint arXiv:1301.3781.

5. Zweig G and Burges C J (2011). The microsoft research sentence completion challenge[R].[S.l.]: Citeseer.

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