Download Analysis of Images, Social Networks and Texts: Third by Dmitry I. Ignatov, Mikhail Yu. Khachay, Alexander Panchenko, PDF

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

This publication constitutes the complaints 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 offered including three brief commercial papers, four invited papers and tutorials. The papers take care of issues comparable to research of pictures and video clips; ordinary language processing and computational linguistics; social community research; laptop studying and knowledge mining; recommender platforms and collaborative applied sciences; semantic net, ontologies and their functions; research of socio-economic info.

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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

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After the information is structured and added to a database it can be used by a wide range of NLP applications, including information retrieval, question answering and many others. g. JASPER (Andersen et al. 1992), specially built for Reuters. Later research was greatly inspired by a series of Message Understanding Conferences (MUC)1 , which were initiated and financed by the Defense Advanced Research Projects Agency (DARPA) to encourage the development of new methods in information extraction. The importance of the MUCs was not the conferences themselves, but the evaluations and evaluation competitions they proposed (Grishman and Sundheim 1996).

2011) identified this problem with previous RE systems and try to address this issue by employing Multi-Instance Multi-label (MIML) approach. They employ distant learning with Multi-Instance learning with overlapping relations (where two same instances may be in two different relations). The resulting system MultiR achieves competitive or higher precision over all ranges of recall. Surdeanu et al. (2012) tackle the same problem. They identify two main problems of distant learning: (1) some training examples obtained through this heuristic are not valid (they report 31 %), (2) the same pair of entities can have several relations.

1414–1420. : Subtree mining for relation extraction from Wikipedia. , Zhai, C. ) Human Language Technology Conference of the North American Chapter of the Association of Computational Linguistics, Proceedings, Rochester, New York, USA, pp. 125–128. : Factorizing YAGO: scalable machine learning for linked data. In: Proceedings of the 21st International Conference on World Wide Web, WWW ’12, pp. 271–280. : Organizing and searching the world wide web of facts - step two: harnessing the wisdom of the crowds.

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