By Dmitry I. Ignatov, Mikhail Yu. Khachay, Alexander Panchenko, Natalia Konstantinova, Rostislav E. Yavorsky
This publication constitutes the complaints of the 3rd overseas convention on research of pictures, Social Networks and Texts, AIST 2014, held in Yekaterinburg, Russia, in April 2014. The eleven complete and 10 brief papers have been rigorously reviewed and chosen from seventy four submissions. they're awarded including three brief commercial papers, four invited papers and tutorials. The papers care for issues corresponding to research of pictures and movies; ordinary language processing and computational linguistics; social community research; computing device studying and information mining; recommender structures and collaborative applied sciences; semantic net, ontologies and their purposes; research of socio-economic information.
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Additional info for Analysis of Images, Social Networks and Texts: Third International Conference, AIST 2014, Yekaterinburg, Russia, April 10-12, 2014, Revised Selected Papers
The overlap of the supporting sets is also employed to identify subsumption relations between various synsets. 2 Distant Learning Mintz et al. (2009) introduce a new term “distant supervision”. The authors use a large semantic database Freebase containing 7,300 relations between 9 million named entities. For each pair of entities that appears in Freebase relation, they identify all sentences containing those entities in a large unlabeled corpus. At the next step textual features to train a relation classiﬁer are extracted.
A document may be excluded if it is too short or does not contain topical words. Note 2. The system of Eqs. (8)–(10) deﬁnes a regularized EM-algorithm. It keeps E-step from (4) and redeﬁnes M-step by regularized Eqs. (9), (10). If R(Φ, Θ) = 0 then the regularized topic model is reduced to the usual PLSA. Tutorial on Probabilistic Topic Modeling 35 Proof. For the local minimum (Φ, Θ) of the problem (7), (3) the KKT conditions (see Appendix A) can be written as follows: ndw d θtd ∂R + = λt − λwt ; p(w | d) ∂φwt λwt ≥ 0; λwt φwt = 0.
Learning of a topic model from a text collection is an ill-posed inverse problem of stochastic matrix factorization. Generally it has an inﬁnite set of solutions. To choose a better solution we add a weighted sum of problem-oriented regularization penalty terms to the log-likelihood. Then the model inference in ARTM can be performed by a simple diﬀerentiation of the regularizers over model parameters. We show that many models, which previously required a complicated inference, can be obtained “in one line” within ARTM.