Similar Text Fragments Extraction for Identifying Common Wikipedia Communities

Similar text fragments extraction from weakly formalized data is the task of natural language processing and intelligent data analysis and is used for solving the problem of automatic identification of connected knowledge fields. In order to search such common communities in Wikipedia, we propose to use as an additional stage a logical-algebraic model for similar collocations extraction. With Stanford Part-Of-Speech tagger and Stanford Universal Dependencies parser, we identify the grammatical characteristics of collocation words. WithWordNet synsets, we choose their synonyms. Our dataset includes Wikipedia articles from different portals and projects. The experimental results show the frequencies of synonymous text fragments inWikipedia articles that form common information spaces. The number of highly frequented synonymous collocations can obtain an indication of key common up-to-date Wikipedia communities

MODELOWANIE PROCESU ROZWOJU EDUKACJI WŁĄCZAJĄCEJ NA UKRAINIE

The article presents the results of research on the implementation of the principles of inclusive education in Ukraine, substantiates its relevance in the development of a democratic society and European integration policy. The principles of the state policy in the field of inclusive education, its normative-legal support and international practice of providing educational services to citizens with special needs are described. Advanced experience of inclusion introduction in the educational process of preschool, general secondary, vocational and higher education institutions is analyzed. Official statistics on the total number of people with disabilities in Ukraine; availability of special secondary education institutions (boarding schools) and their contingent; the number of children with disabilities in preschool education institutions, students with disabilities in full-time secondary education institutions, persons with disabilities in vocational education institutions, persons with disabilities among students of higher education institutions etc., have been handled. The importance and expediency of the scientific development of the principles of inclusive education, its practical implementation and study  of socio-economic effect are substantiated. Graphic visualization of the correlation between the employed persons with disabilities and persons without disabilities, number of children, pupils, students with special educational needs in education establishments of Ukraine is presented.W artykule przedstawiono wyniki badań nad procesem wdrażania zasad edukacji włączającej na Ukrainie, uzasadniono ich znaczenie w warunkach rozwoju społeczeństwa demokratycznego i europejskiej polityki integracyjnej państwa. Opisano zasady krajowej polityki państwa w zakresie edukacji włączającej, jej wsparcie regulacyjne i prawne oraz międzynarodową praktykę świadczenia usług edukacyjnych obywatelom ze specjalnymi potrzebami. Przeanalizowano najlepsze doświadczenia z wdrażania integracji w procesie edukacyjnym placówek przedszkolnych, ogólnokształcących, zawodowych i wyższych. Opracowano oficjalne dane statystyczne dla Ukrainy dotyczące całkowitej liczby osób niepełnosprawnych; dostępność specjalnych szkół średnich (internatów) i ich zespołów; liczby dzieci niepełnosprawnych w placówkach wychowania przedszkolnego, uczniów niepełnosprawnych w dziennych placówkach ogólnokształcących, osób niepełnosprawnych w placówkach kształcenia zawodowego (zawodowego i technicznego), osób niepełnosprawnych wśród studentów szkół wyższych itp. Uzasadniono wagę i celowość naukowego opracowania zasad edukacji włączającej, jej praktyczne zastosowanie oraz badanie efektu społeczno-ekonomicznego. Zaprezentowano graficzną wizualizację stosunku zatrudnionych osób z niepełnosprawnościami do osób sprawnych, liczby dzieci, uczniów i studentów ze specjalnymi potrzebami edukacyjnymi w instytucjach edukacyjnych Ukrainy

Continuous Speech Recognition of Kazakh Language

This article describes the methods of creating a system of recognizing the continuous speech of Kazakh language. Studies on recognition of Kazakh speech in comparison with other languages began relatively recently, that is after obtaining independence of the country, and belongs to low resource languages. A large amount of data is required to create a reliable system and evaluate it accurately. A database has been created for the Kazakh language, consisting of a speech signal and corresponding transcriptions. The continuous speech has been composed of 200 speakers of different genders and ages, and the pronunciation vocabulary of the selected language. Traditional models and deep neural networks have been used to train the system. As a result, a word error rate (WER) of 30.01% has been obtained

Logical-linguistic model for multilingual Open Information Extraction

Open Information Extraction (OIE) is a modern strategy to extract the triplet of facts from Web-document collections. However, most part of the current OIE approaches is based on NLP techniques such as POS tagging and dependency parsing, which tools are accessible not to all languages. In this paper, we suggest the logical-linguistic model, which basic mathematical means are logical-algebraic equations of finite predicates algebra. These equations allow expressing a semantic role of the participant of a triplet of the fact (Subject-Predicate-Object) due to the relations of grammatical characteristics of words in the sentence. We propose the model that extracts the unlimited domain-independent number of facts from sentences of different languages. The use of our model allows extracting the facts from unstructured texts without requiring a pre-specified vocabulary, by identifying relations in phrases and associated arguments in arbitrary sentences of English, Kazakh, and Russian languages. We evaluate our approach on corpora of three languages based on English and Kazakh bilingual news websites. We achieve the precision of facts extraction over 87% for English corpus, over 82% for Russian corpus and 71% for Kazakh corpus

Continuous Speech Recognition of Kazakh Language

This article describes the methods of creating a system of recognizing the continuous speech of Kazakh language. Studies on recognition of Kazakh speech in comparison with other languages began relatively recently, that is after obtaining independence of the country, and belongs to low resource languages. A large amount of data is required to create a reliable system and evaluate it accurately. A database has been created for the Kazakh language, consisting of a speech signal and corresponding transcriptions. The continuous speech has been composed of 200 speakers of different genders and ages, and the pronunciation vocabulary of the selected language. Traditional models and deep neural networks have been used to train the system. As a result, a word error rate (WER) of 30.01% has been obtained