By Dmitry I. Ignatov, Mikhail Yu. Khachay, Alexander Panchenko, Natalia Konstantinova, Rostislav E. Yavorsky
This publication constitutes the lawsuits 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 rigorously reviewed and chosen from seventy four submissions. they're provided including three brief commercial papers, four invited papers and tutorials. The papers care for subject matters similar to research of pictures and movies; normal language processing and computational linguistics; social community research; desktop studying and information mining; recommender structures and collaborative applied sciences; semantic net, ontologies and their functions; research of socio-economic facts.
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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
Reducing the overlapping between the topic-word distributions is known to make the learned topics more interpretable . 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.
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148–163. : Relation extraction with matrix factorization and universal schemas. , Kirchhoﬀ, K. ) HLT-NAACL, pp. 74–84. , Jones, R: Learning dictionaries for information extraction by multi-level bootstrapping. In: Proceedings of the Sixteenth National Conference on Artiﬁcial Intelligence and the Eleventh Innovative Applications of Artiﬁcial Intelligence Conference, Menlo Park, CA, USA, AAAI ’99/IAAI ’99, pp. 474–479. American Association for Artiﬁcial Intelligence (1999) 28 N. : Global inference for entity and relation identiﬁcation via a linear programming formulation.