Subscribe to the gold package and get unlimited access to Shamra Academy
Register a new userGerman- Saudi Relations On the Eve of the SecondWorld War
التطور التاريخي للعلاقات الألمانية – السعودية في ثلاثينيات القرن العشرين في ضوء الوثائق الألمانية
1200
0 44
0
(
0
)
Authors
محمد أحمد( باحث )
Created by
Shamra Editor
Ask ChatGPT about the research
يستند هذا البحث إلى ركيزة أساسية، هي تتبع التطور التاريخي للعلاقات الألمانية - السعودية في حقبة ما قبل الحرب العالمية الثانية، و ركزت الدراسة على عرض تاريخي لمجمل العلاقات الأوروبية – العربية في ضوء الأحداث التاريخية المهمة التي وقعت في مرحلة ما بين الحربين و لاسيما سياسة ألمانيا الخارجية و موقعها في مسرح السياسة الدولية، و يجيب البحث عن أسئلة مشروعة مثل: ما مدى استجابة ألمانيا لمطالبة السعودية بشأن تزويدها بأسلحة متطورة؟ و ما موقع ألمانيا في استراتيجية الملك عبد العزيز آل سعود ضمن سياسته الخارجية.؟ و ما أهم النتائج التي تمخضت عنها العلاقات الألمانية – السعودية عشية الحرب العالمية الثانية؟
This research mainly is based on one basic topic, namely, the observation of German-Saudi relations in the period prior to the Second World War. This study focuses on brief historical presentation of Arab- European relation in the light of the important historical events which took place in the period between the world wars, it also observes the foreign policy of Germany and its position in the stage of international policy Furthermore, the research answers legitimate questions such as: To what extent had Germany responded to the Saudi request for providing the kingdom with advanced weapons ? What is the strategy of king Abdul Aziz AL Saud in foreign policy? And What are the most important results of the German- Saudi relations on the eve of the Second World War?
References used
زياني، أمل: علاقات المملكة العربية السعودية في النطاق الإقليمي،القاهرة .1989
الموسوعة العربية، الجمهورية العربية السورية، دمشق، الطبعة الأولى 2001
Wolffsohn, Michael : German Saudi Arabien Arms Deals , 1936 – 1939 , Frankfurt 1985
rate research
Read More
The German Byzantine relations between 1137-1146 were
alliance relationships against Roger II King of Sicily, because in the
increasing of the influence of Roger II in Italy the influence of
German and Byzantine empires was diminished, also the Byzantine
Emperor John Komenan saw in the ambitions of Roger II a direct
threat to the interests of Byzantium in the East and the West.
Historically speaking, the German legal language is widely neglected in NLP research, especially in summarization systems, as most of them are based on English newspaper articles. In this paper, we propose the task of automatic summarization of Germa
n court rulings. Due to their complexity and length, it is of critical importance that legal practitioners can quickly identify the content of a verdict and thus be able to decide on the relevance for a given legal case. To tackle this problem, we introduce a new dataset consisting of 100k German judgments with short summaries. Our dataset has the highest compression ratio among the most common summarization datasets. German court rulings contain much structural information, so we create a pre-processing pipeline tailored explicitly to the German legal domain. Additionally, we implement multiple extractive as well as abstractive summarization systems and build a wide variety of baseline models. Our best model achieves a ROUGE-1 score of 30.50. Therefore with this work, we are laying the crucial groundwork for further research on German summarization systems.
The widespread use of the Internet and the rapid dissemination of information poses the challenge of identifying the veracity of its content. Stance detection, which is the task of predicting the position of a text in regard to a specific target (e.g
. claim or debate question), has been used to determine the veracity of information in tasks such as rumor classification and fake news detection. While most of the work and available datasets for stance detection address short texts snippets extracted from textual dialogues, social media platforms, or news headlines with a strong focus on the English language, there is a lack of resources targeting long texts in other languages. Our contribution in this paper is twofold. First, we present a German dataset of debate questions and news articles that is manually annotated for stance and emotion detection. Second, we leverage the dataset to tackle the supervised task of classifying the stance of a news article with regards to a debate question and provide baseline models as a reference for future work on stance detection in German news articles.
Adjectives such as heavy (as in heavy rain) and windy (as in windy day) provide possible values for the attributes intensity and climate, respectively. The attributes themselves are not overtly realized and are in this sense implicit. While these att
ributes can be easily inferred by humans, their automatic classification poses a challenging task for computational models. We present the following contributions: (1) We gain new insights into the attribute selection task for German. More specifically, we develop computational models for this task that are able to generalize to unseen data. Moreover, we show that classification accuracy depends, inter alia, on the degree of polysemy of the lexemes involved, on the generalization potential of the training data and on the degree of semantic transparency of the adjective-noun pairs in question. (2) We provide the first resource for computational and linguistic experiments with German adjective-noun pairs that can be used for attribute selection and related tasks. In order to safeguard against unwelcome memorization effects, we present an automatic data augmentation method based on a lexical resource that can increase the size of the training data to a large extent.
This paper presents a data set of German fairy tales, manually annotated with character networks which were obtained with high inter rater agreement. The release of this corpus provides an opportunity of training and comparing different algorithms fo
r the extraction of character networks, which so far was barely possible due to heterogeneous interests of previous researchers. We demonstrate the usefulness of our data set by providing baseline experiments for the automatic extraction of character networks, applying a rule-based pipeline as well as a neural approach, and find the neural approach outperforming the rule-approach in most evaluation settings.
Log in to be able to interact and post comments
comments
Fetching comments
Sign in to be able to follow your search criteria
