Method for Generating Subject Area Associative Portraits: Different Examples
288 Int'l Conf. Artificial Intelligence | ICAI'15 | Method for Generating Subject Area Associative Portraits: Different Examples I. Galina1, M. Charnine1, N. Somin1, V. Nikolaev1, Yu. Morozova1, O. Zolotarev2 1Institute of Informatics Problems, Federal Research Center, Russian Academy of Sciences, Moscow, Russia 2Russian New University, Moscow, Russia Tatarstan". Information sources for this topic are: official Abstract - For the last several years a group of scientists has websites of state institutions, political parties; national and been working on creation of a knowledge extraction system regional media; social network Vkontakte, Twitter based on the automated generation of subject area associative microblogging, etc.); "Monitoring of public opinion in the portraits (SAAP) and on the construction of semantic context socio-political sphere", tapering to a training sample "Protest spaces (SCS) [1-11]. The ideology of the SAAP is based on activity"; and AUV (Autonomous underwater vehicles") in the distributional hypothesis, stating that semantically similar Russian and English languages; "Computer Science" (in (or related) lexemes have similar context and, conversely, English), etc. similar context has similar lexemes. In the applied model we use an extended hypothesis, that includes not only the study of On these topics we found and processed from ten thousand to similarities and differences in the contexts of individual one and a half million documents in Russian and English lexemes, but also arbitrary multiple lexemes fragments languages, a total of about 160 GB. For example, on the SA (significant phrases - SP) too. of "Socio-political portrait of Tatarstan" formed the texts Keywords: Subject Area, Associative Portraits, keywords digest of about 20 GB, and on the subject of "Protest activity" extracting, significant phrases, thesaurus, Big Data there was found over 28 GB of test information.
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