Analysis of Images, Social Networks and Texts: Third - download pdf or read online

By Dmitry I. Ignatov, Mikhail Yu. Khachay, Alexander Panchenko, Natalia Konstantinova, Rostislav E. Yavorsky

ISBN-10: 3319125796

ISBN-13: 9783319125794

ISBN-10: 331912580X

ISBN-13: 9783319125800

This publication constitutes the court cases of the 3rd foreign 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 conscientiously reviewed and chosen from seventy four submissions. they're provided including three brief commercial papers, four invited papers and tutorials. The papers take care of issues reminiscent of research of pictures and video clips; traditional language processing and computational linguistics; social community research; computing device studying and information mining; recommender structures and collaborative applied sciences; semantic internet, ontologies and their functions; research of socio-economic information.

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Extra info for Analysis of Images, Social Networks and Texts: Third International Conference, AIST 2014, Yekaterinburg, Russia, April 10-12, 2014, Revised Selected Papers

Example text

Mining sequential patterns: generalizations and performance improvements. , Gardarin, G. ) EDBT 1996. LNCS, vol. 1057, pp. 3–17. : YAGO: a core of semantic knowledge. In: Proceedings of WWW-07, pp. : Multi-instance multi-label learning for relation extraction. In: Proceedings of the 2012 Joint Conference on Empirical Methods in Natural Language Processing and Computational Natural Language Learning, EMNLP-CoNLL ’12, pp. 455–465. : Reducing wrong labels in distant supervision for relation extraction.

We show that many models, which previously required a complicated inference, can be obtained “in one line” within ARTM. The weights in a linear combination of regularizers can be adopted during the iterative process. Our experiments demonstrate that ARTM can combine regularizers that improve many criteria at once almost without a loss of the likelihood. 2 Topic Models PLSA and LDA In this section we describe Probabilistic Latent Sematic Analysis (PLSA) model, which was historically a predecessor of LDA.

A regularizer that minimizes covariance between vectors φt , φwt φws → max, R(Φ) = −τ t∈T s∈T \t w∈W leads to the following equation of the M-step: φwt ∝ nwt − τ φwt φws s∈T \t + . That is, for each word w the highest probabilities φwt will increase from iteration to iteration, while small probabilities will decrease, and may eventually turn into zeros. Therefore, this regularizer also stimulates sparsity. Besides, it has another useful property, which is to group stop-words into separate topics [21].

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Analysis of Images, Social Networks and Texts: Third International Conference, AIST 2014, Yekaterinburg, Russia, April 10-12, 2014, Revised Selected Papers by Dmitry I. Ignatov, Mikhail Yu. Khachay, Alexander Panchenko, Natalia Konstantinova, Rostislav E. Yavorsky


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