استخراج روابط محلی وابسته به ترتیب کلمات با استفاده از یک مدل سلسله‌مراتبی بیز

[1]      Croft, B. and J. Lafferty, “Language modeling for information retrieval. Vol. 13. Springer Science & Business Media, 2013.

[2]      Wallach, H.M. “Topic modeling: beyond bag-of-words”. ACM.2006.

[3]      3.   Manning, C.D., et al., Introduction to Information Retrieval. Cambridge University Press. 496. 2008.

[4]      Seneff, S. “The use of linguistic hierarchies in speech understanding”. in ICSLP. 1998.

[5]      Galescu, L. and J.F. Allen. “Hierarchical statistical language models: experiments on in-domain adaptation”. in INTERSPEECH. 2000.

[6]      Blei, D.M., A.Y. Ng, and M.I. Jordan, “Latent Dirichlet allocation”. Journal of machine Learning research. 3(Jan): p. 993-1022. 2003.

[7]      Noji, H., D. Mochihashi, and Y. Miyao. “Improvements to the Bayesian Topic N-Gram Models”. In EMNLP, pp. 1180-1190. 2013.

[8]      Graves, A. and N. Jaitly. “Towards End-To-End Speech Recognition with Recurrent Neural Networks”. in ICML. 2014.

[9]      Evershed, J. and K. Fitch. “Correcting noisy OCR: Context beats confusion”. in Proceedings of the First International Conference on Digital Access to Textual Cultural Heritage. ACM. 2014.

[10]      Carlson, A. and I. Fette. “Memory-based context-sensitive spelling correction at web scale. in Machine learning and applications, sixth international conference on. ICMLA. IEEE.  2007.

[11]      Kneser, R. and H. Ney. “Improved backing-off for m-gram language modeling”. in 1995 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP). IEEE. 1995.

[12]      Ney, H., U. Essen, and R. Kneser, “On structuring probabilistic dependences in stochastic language modelling”. Computer Speech & Language. 8(1): p. 1-38. 1994.

[13]      Jelinek, F. “Interpolated estimation of Markov source parameters from sparse data”. in Proc. Workshop on Pattern Recognition in Practice, 1980.

[14]      Chen, S.F. and J. Goodman, “An empirical study of smoothing techniques for language modeling”. Computer Speech & Language. 13(4): p. 359-394. 1999.

[15]      De Mulder, W., S. Bethard, and M.-F. Moens, “A survey on the application of recurrent neural networks to statistical language modeling”. Computer Speech & Language. 30(1): p. 61-98. 2015.

[16]      Deschacht, K., J. De Belder, and M.-F. Moens, “The latent words language model”. Computer Speech & Language. 26(5): p. 384-409. 2012.

[17]      Deoras, A., et al., “Approximate inference: A sampling based modeling technique to capture complex dependencies in a language model”. Speech Communication. 55(1): p. 162-177. 2013.

[18]      Sidorov, G., et al., “Syntactic n-grams as machine learning features for natural language processing”. Expert Systems with Applications. 41(3): p. 853-860. 2014.

[19]      Deoras, A., et al. “Variational approximation of long-span language models for LVCSR”. in 2011 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP). IEEE. 2011.

[20]      Minka, T. and J. Lafferty. “Expectation-propagation for the generative aspect mode”l. Morgan Kaufmann Publishers Inc. 2002.

[21]      Griffiths, T.L. and M. Steyvers, “Finding scientific topics”. Proceedings of the National academy of Sciences, 101(suppl 1): p. 5228-5235. 2004.

[22]      رمضان هاونگی, «موقعیت‌یابی ربات براساس فیلتر ذره‌ای بهبود یافته با فیلتر کالمن گروهی هوشمند و گام MCMC».مجله مهندسی برق دانشگاه تبریز، دروه 46، شماره 4،صفحه 345-356. 1395.

[23]      سیامک عبداله‌زاده و دیگران، «استفاده از خوشه‌بندی و مدل مارکوف جهت پیش‌بینی درخواست آتی کاربر در وب». مجله مهندسی برق دانشگاه تبریز، دروه 45، شماره 3،صفحه 89-96. 1394.

[24]      Chelba, C., et al., “One billion word benchmark for measuring progress in statistical language modeling”. arXiv preprint arXiv:1312.3005, 2013.