In natural language understanding, there is a hierarchy of lenses through which we can extract meaning - from words to sentences to paragraphs to documents. At the document level, one of the most useful ways to understand text is by analyzing its topics.
Stan modeling language and C++ library for Bayesian inference. NUTS adaptive HMC (MCMC) sampling, automatic differentiation, R, shell interfaces. Gelman.
J. Eisenstein, B. O'Connor, N. Smith, und E. Xing. Proceedings of the 2010 Conference on Empirical Methods in Natural Language Processing, Seite 1277--1287. Stroudsburg, PA, USA, Association for Computational Linguistics, (2010)
C. Kling, J. Kunegis, S. Sizov, und S. Staab. Proceedings of the 7th ACM international conference on Web search and data mining, Seite 603--612. ACM, (2014)
Q. He, B. Chen, J. Pei, B. Qiu, P. Mitra, und L. Giles. Proceedings of the 18th ACM conference on Information and knowledge management, Seite 957--966. New York, NY, USA, ACM, (2009)
S. Vakulenko, O. Müller, und J. vom Brocke. Proceedings of the International Conference on Information Systems - Building a Better World through Information Systems, ICIS 2014, Auckland, New Zealand, December 14-17, 2014, Association for Information Systems, (2014)