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Register a new userAutomatic Classification of Attributes in German Adjective-Noun Phrases
التصنيف التلقائي للسمات في عبارات الأسماء الألمانية
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قدم الصفات مثل الثقيلة (كما هو الحال في الأمطار الغزيرة) والرياح (كما في يوم عاصف) القيم المحتملة لشدة السمات ومناخها على التوالي. لا تتحقق السمات نفسها بشكل علني وهناها هذه المنطقة الضالة. في حين يمكن استنتاج هذه السمات بسهولة من قبل البشر، فإن تصنيفهم التلقائي يشكل مهمة صعبة للنماذج الحسابية. نقدم المساهمات التالية: (1) نكتسب رؤى جديدة في مهمة اختيار السمات للألمانية. وبشكل أكثر تحديدا، نطور نماذج حسابية لهذه المهمة التي يمكن أن تعميم البيانات غير المرئية. علاوة على ذلك، نوضح أن دقة التصنيف تعتمد، في جملة أمور، على درجة Polysemy في Lexemes المعنية، على إمكانات تعميم البيانات التدريبية وعلى درجة الشفافية الدلالية في أزواج صفة الأسماء المعنية. (2) نحن نقدم الموارد الأولى للتجارب الحسابية واللغوية مع أزواج الأسماء المصرفية الألمانية التي يمكن استخدامها في اختيار السمات والمهام ذات الصلة. من أجل حماية آثار الحفظ غير المرغوب فيه، نقدم طريقة تكبير البيانات التلقائي استنادا إلى مورد معجمي يمكن أن يزيد من حجم بيانات التدريب إلى حد كبير.
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 attributes 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.
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