Suchergebnisse

The purpose of the article is to construct an up-to-date research methodology which will allow to examine territories within the context of sustainable development issues. Sustainable development of socio-economic and natural systems in its entirety is impossible without a comprehensive consideration of key factors and conditions that affect the state and prospects for their development, and without a targeted impact on these factors and conditions. The main result of the study is the adaptation of the methodology to the study of sustainable development of territories with different economic specialization and growth potential. A successful solution to this task is impossible without the development and consistent application of the research methodology, adequate to the goals and tasks set, to the current situation and the long-term trends in the development of nature and society. The results of our research suggest the solution of applied socio-economic tasks for the development of territories (subjects of the Russian Federation, small and medium-sized cities) to improve the quality of life and sustainable economic growth.

The article is devoted to the analysis of the correlation between the figures of the real author and the fictional author-narrator in the novel by M.Yu. Lermontov A Hero of Our Time. It is shown that as a result of the inclusion in the text of the second edition of the novel (1841) of the author's preface, containing Lermontov's polemic with the reviewers of the first edition, there was an interference of the "I" of the real creator of the work and the "I" of the fictional author, to whom the stories Bela, Princess Mary and the preface to the diary of the protagonist are attributed. It is significant that some of the biographical features of Lermontov and the fictional author coincide, and the two prefaces overlap. Thanks to all this, the effect of a non-trivial literary game arose, based on the illusion of the authenticity of the events set forth in the novel: the real author seems to get acquainted with
Pechorin. This game is fundamentally different from the Sternian play and violation of the boundaries of the real and the fictional in A.S. Pushkin's Eugene Onegin, whose main characters are represented both by real persons and creations of the author's imagination. Unlike Pushkin and many contemporary Russian prose writers, Lermontov does not expose, but, on the contrary, hides the fictional, purely literary nature of his narrative. At the same time, the creator of A Hero of Our Time does not resort to literary hoax, does not directly try to pass off either the first-level narrator or Pechorin as the real authors of the novel or part of it. The story of A. von Chamisso The Extraordinary Story of Peter Schlemil could serve as a model for such a game, in which the central character, who tells a fantastic story of the events of his life, is an acquaintance of the author and his real-life friends.

Volltext // 2011 digitalisiert von: Bayerische Staatsbibliothek, München. Exemplar mit der Signatur: München, Bayerische Staatsbibliothek -- Per. 198 f-22,1

Volltext // 2011 digitalisiert von: Bayerische Staatsbibliothek, München. Exemplar mit der Signatur: München, Bayerische Staatsbibliothek -- Per. 198 f-22,2

In our paper we present a corpus of transcribed Lithuanian parliamentary speeches. The corpus is prepared in a specific format, appropriate for different authorship identification tasks. The corpus consists of approximately 111 thousand texts (24 million words). Each text matches one parliamentary speech produced during an ordinary session from the period of 7 parliamentary terms starting on March 10, 1990 and ending on December 23, 2013. The texts are grouped into 147 categories corresponding to individual authors, therefore they can be used for authorship attribution tasks; besides, these texts are also grouped according to age, gender and political views, therefore they are also suitable for author profiling tasks. Whereas short texts complicate recognition of author speaking style and are ambiguous in relation to the style of other authors, we incorporated only texts containing not less than 100 words into the corpus. In order to make each category as comprehensive and representative as possible, we included only those authors, who produced speeches at least 200 times. All the texts are lemmatized, morphologically and syntactically annotated, tokenized into the character n-grams. The statistical information of the corpus is also available. We have also demonstrated that the created corpus can be effectively used in authorship attribution and author profiling tasks with supervised machine learning methods. The corpus structure also allows using it with unsupervised machine learning methods and can be used for creation of rule-based methods, as well as in different linguistic analyses.

In our paper we present a corpus of transcribed Lithuanian parliamentary speeches. The corpus is prepared in a specific format, appropriate for different authorship identification tasks. The corpus consists of approximately 111 thousand texts (24 million words). Each text matches one parliamentary speech produced during an ordinary session from the period of 7 parliamentary terms starting on March 10, 1990 and ending on December 23, 2013. The texts are grouped into 147 categories corresponding to individual authors, therefore they can be used for authorship attribution tasks; besides, these texts are also grouped according to age, gender and political views, therefore they are also suitable for author profiling tasks. Whereas short texts complicate recognition of author speaking style and are ambiguous in relation to the style of other authors, we incorporated only texts containing not less than 100 words into the corpus. In order to make each category as comprehensive and representative as possible, we included only those authors, who produced speeches at least 200 times. All the texts are lemmatized, morphologically and syntactically annotated, tokenized into the character n-grams. The statistical information of the corpus is also available. We have also demonstrated that the created corpus can be effectively used in authorship attribution and author profiling tasks with supervised machine learning methods. The corpus structure also allows using it with unsupervised machine learning methods and can be used for creation of rule-based methods, as well as in different linguistic analyses.

In our paper we present a corpus of transcribed Lithuanian parliamentary speeches. The corpus is prepared in a specific format, appropriate for different authorship identification tasks. The corpus consists of approximately 111 thousand texts (24 million words). Each text matches one parliamentary speech produced during an ordinary session from the period of 7 parliamentary terms starting on March 10, 1990 and ending on December 23, 2013. The texts are grouped into 147 categories corresponding to individual authors, therefore they can be used for authorship attribution tasks; besides, these texts are also grouped according to age, gender and political views, therefore they are also suitable for author profiling tasks. Whereas short texts complicate recognition of author speaking style and are ambiguous in relation to the style of other authors, we incorporated only texts containing not less than 100 words into the corpus. In order to make each category as comprehensive and representative as possible, we included only those authors, who produced speeches at least 200 times. All the texts are lemmatized, morphologically and syntactically annotated, tokenized into the character n-grams. The statistical information of the corpus is also available. We have also demonstrated that the created corpus can be effectively used in authorship attribution and author profiling tasks with supervised machine learning methods. The corpus structure also allows using it with unsupervised machine learning methods and can be used for creation of rule-based methods, as well as in different linguistic analyses.

In our paper we present a corpus of transcribed Lithuanian parliamentary speeches. The corpus is prepared in a specific format, appropriate for different authorship identification tasks. The corpus consists of approximately 111 thousand texts (24 million words). Each text matches one parliamentary speech produced during an ordinary session from the period of 7 parliamentary terms starting on March 10, 1990 and ending on December 23, 2013. The texts are grouped into 147 categories corresponding to individual authors, therefore they can be used for authorship attribution tasks; besides, these texts are also grouped according to age, gender and political views, therefore they are also suitable for author profiling tasks. Whereas short texts complicate recognition of author speaking style and are ambiguous in relation to the style of other authors, we incorporated only texts containing not less than 100 words into the corpus. In order to make each category as comprehensive and representative as possible, we included only those authors, who produced speeches at least 200 times. All the texts are lemmatized, morphologically and syntactically annotated, tokenized into the character n-grams. The statistical information of the corpus is also available. We have also demonstrated that the created corpus can be effectively used in authorship attribution and author profiling tasks with supervised machine learning methods. The corpus structure also allows using it with unsupervised machine learning methods and can be used for creation of rule-based methods, as well as in different linguistic analyses.

In our paper we present a corpus of transcribed Lithuanian parliamentary speeches. The corpus is prepared in a specific format, appropriate for different authorship identification tasks. The corpus consists of approximately 111 thousand texts (24 million words). Each text matches one parliamentary speech produced during an ordinary session from the period of 7 parliamentary terms starting on March 10, 1990 and ending on December 23, 2013. The texts are grouped into 147 categories corresponding to individual authors, therefore they can be used for authorship attribution tasks; besides, these texts are also grouped according to age, gender and political views, therefore they are also suitable for author profiling tasks. Whereas short texts complicate recognition of author speaking style and are ambiguous in relation to the style of other authors, we incorporated only texts containing not less than 100 words into the corpus. In order to make each category as comprehensive and representative as possible, we included only those authors, who produced speeches at least 200 times. All the texts are lemmatized, morphologically and syntactically annotated, tokenized into the character n-grams. The statistical information of the corpus is also available. We have also demonstrated that the created corpus can be effectively used in authorship attribution and author profiling tasks with supervised machine learning methods. The corpus structure also allows using it with unsupervised machine learning methods and can be used for creation of rule-based methods, as well as in different linguistic analyses.

Sostavleno po osnovnym zakonam, ustavam, prikazam po voennomu vi︠e︡domstvu, t︠s︡irkuli︠a︡ram glavnago shtaba, otzyvam glavnago shtaba, t︠s︡irkuli︠a︡rnym predpisanīi︠a︡m nachalʹstva, prikazam po voennym okrugam, pi︠e︡shenīi︠a︡m glavnago voennago suda i drugim zakonnym dokumentam po 1-e i︠a︡nvari︠a︡ 1899 goda. ; Mode of access: Internet.

Collection : Letopisʹ revolûcii ; 1 ; Collection : Letopisʹ revolûcii ; 1 ; Collection : Летопись революции ; 1 ; Collection : Летопись революции ; 1

The article features the meeting held on November 21–22, 1915, where representatives of the authorities, municipal and public organizations discussed the issues of assistance to World War I refugees on the territory of the Irkutsk Governorate General. The study is based on the previously unstudied meeting minutes and the newspaper reports and describes the participants, the main issues, and the results of the discussion. The research made it obvious that initially the authorities did not plan to accommodate the refugees in such a remote region but were forced to redirect them farther east because the western regions were overcrowded. The cities of Eastern Siberiadid not have time to prepare for such a massive resettlement. The migrants were hastily resettled, provided with rations and medical assistance. The money, however minimal and irregular, came mostly from the government, the department of migration, the All-Russian Union ofCities, and local charitable organizations. At the meeting, it was proposed to set up a general Siberian Committee for Assistance to Refugees, to organize local refugee departments, and assign all expenses to the state. ; Статья посвящена совещанию, состоявшемуся 21–22 ноября 1915 г., на котором представители власти, городов и общественных организаций обсуждали вопросы приема и организации помощи беженцам Первой мировой войны в пределах Иркутского генерал-губернаторства. Исследование основано на протоколах совещания и материалах периодической печати, которые введены в научный оборот впервые. Охарактеризованы участники, главные темы, результаты обсуждения. Показано, что власти первоначально не планировали размещать беженцев в столь отдаленном регионе, но из-за переполненности западных областей были вынуждены направлять их далее на восток. Города Восточной Сибири не успели подготовиться к такому массовому переселению. Людей спешно расселяли, организовывали питательные, медицинские пункты, налаживали пайковое обеспечение. На эти цели использовали правительственные субсидии (хотя помощь от казны была минимальной, поступала несвоевременно), ресурсы переселенческого ведомства, Всероссийского союза городов, местных благотворительных организаций. Участники совещания предлагали создать общесибирский комитет помощи беженцам, организовать систему поддержки беженцев на территории уездов, все расходы по содержанию беженцев и выселенцев возложить исключительно на государство.