Suchergebnisse

ZUSAMMENFASSUNG Die Gesellschaftsformen der Europäischen Union: Probleme und Perspektiven In der vorliegenden Arbeit werden die Gesellschaftsformen der Europäischen Union, die für sie gesetzten Ziele und eingeführte oder noch geplante Regelungen dieser Formen behandelt. Es wird auch analysiert, ob die Regelungen der Gesellschaftsformen deren Zielen entsprechen. Ausserdem wird die im EU-Primärrecht verankerte Niederlassungsfreiheit, die Grenzen der Niederlassungsfreiheit und deren Verhältnis zu den Gesellschaftsformen der Europäischen Union behandelt. Der erste Teil der Arbeit befasst sich mit der im EU-Primärrecht verankerten Niederlassungsfreiheit für Gesellschaften. Es werden auch heutige Grenzen der Niederlassungsfreiheit auf Grund der Rechtsprechung des Europäischen Gerichtshofes festgestellt. Ausserdem wird das Verhältnis zwischen der Niederlassungsfreiheit und den Gesellschaftsformen der Europäischen Union dargestellt. Im zweiten Teil der Arbeit werden folgende, schon vorhandene Gesellschaftsformen der Europäischen Union analysiert: Die Europäische Wirtschaftliche Interessenvereinigung (EWIV), die Europäische Gesellschaft (SE) und die Europäische Genossenschaft (SCE). Es wird kurz auf die historische Entwicklung dieser Formen hingewiesen, dann befasst man sich mit der Feststellung der Ziele, die für die einzelnen Gesellschaftsformen gesetzt sind und mit der Regelungsanalyse der einzelnen Gesellschaftsformen. Es werden auch die größten Probleme bezüglich dieser Gesellschaftsformen klargemacht und die möglichen Optionen zur Lösung dieser Probleme vorgestellt. Der dritte Teil der Arbeit untersucht folgende geplante Gesellschaftsformen der Europäischen Union und deren geplante Regelung: die Europäische Privatgesellschaft (SPE), die Europäische Stiftung (EF), der Europäische Verein (EA) und die Europäische Gegenseitigkeitsgesellschaft (ME). In diesem Teil wird die Regelung der geplanten Gesellschaftsformen der Europäischen Union im Bezug auf die gesetzten Ziele beurteilt und mögliche Zukunftsperspektiven der geplanten und schon vorhandenen Gesellschaftsformen dargestellt. Am Ende der Arbeit werden Schlussfolgerungen zu den in der Arbeit gesetzten Zielen und Aufgaben vorgelegt.

ZUSAMMENFASSUNG Die Gesellschaftsformen der Europäischen Union: Probleme und Perspektiven In der vorliegenden Arbeit werden die Gesellschaftsformen der Europäischen Union, die für sie gesetzten Ziele und eingeführte oder noch geplante Regelungen dieser Formen behandelt. Es wird auch analysiert, ob die Regelungen der Gesellschaftsformen deren Zielen entsprechen. Ausserdem wird die im EU-Primärrecht verankerte Niederlassungsfreiheit, die Grenzen der Niederlassungsfreiheit und deren Verhältnis zu den Gesellschaftsformen der Europäischen Union behandelt. Der erste Teil der Arbeit befasst sich mit der im EU-Primärrecht verankerten Niederlassungsfreiheit für Gesellschaften. Es werden auch heutige Grenzen der Niederlassungsfreiheit auf Grund der Rechtsprechung des Europäischen Gerichtshofes festgestellt. Ausserdem wird das Verhältnis zwischen der Niederlassungsfreiheit und den Gesellschaftsformen der Europäischen Union dargestellt. Im zweiten Teil der Arbeit werden folgende, schon vorhandene Gesellschaftsformen der Europäischen Union analysiert: Die Europäische Wirtschaftliche Interessenvereinigung (EWIV), die Europäische Gesellschaft (SE) und die Europäische Genossenschaft (SCE). Es wird kurz auf die historische Entwicklung dieser Formen hingewiesen, dann befasst man sich mit der Feststellung der Ziele, die für die einzelnen Gesellschaftsformen gesetzt sind und mit der Regelungsanalyse der einzelnen Gesellschaftsformen. Es werden auch die größten Probleme bezüglich dieser Gesellschaftsformen klargemacht und die möglichen Optionen zur Lösung dieser Probleme vorgestellt. Der dritte Teil der Arbeit untersucht folgende geplante Gesellschaftsformen der Europäischen Union und deren geplante Regelung: die Europäische Privatgesellschaft (SPE), die Europäische Stiftung (EF), der Europäische Verein (EA) und die Europäische Gegenseitigkeitsgesellschaft (ME). In diesem Teil wird die Regelung der geplanten Gesellschaftsformen der Europäischen Union im Bezug auf die gesetzten Ziele beurteilt und mögliche Zukunftsperspektiven der geplanten und schon vorhandenen Gesellschaftsformen dargestellt. Am Ende der Arbeit werden Schlussfolgerungen zu den in der Arbeit gesetzten Zielen und Aufgaben vorgelegt.

Engl. Zsfassung u.d.T.: 1941 June uprising

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.