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Register a new userPersonality Trait Identification Using the Russian Feature Extraction Toolkit
شخصية شخصية الهوية باستخدام مجموعة أدوات استخراج الميزة الروسية
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هندسة ميزة هي خطوة مهمة في خطوط أنابيب NLP الكلاسيكية، ولكن قد لا يكون مهندسو تعلم الآلات على بينة من الإشارات للبحث عنها عند معالجة نص اللغة الأجنبية.مجموعة أدوات الاستخراج الروسية (RFET) هي مجموعة من مكتبات استخراج الميزات المجمعة لسهولة الاستخدام من قبل المهندسين الذين لا يتحدثون الروسية.تتضمن مجموعة الميزات الحالية ل RFET ميزات تنطبق على الأنواع عبر وسائل التواصل الاجتماعي للنص ومهام العلوم الاجتماعية الحاسوبية.نوضح فعالية الأداة باستخدامه في مهمة تحديد سمة الشخصية.قارنا أداء آلات ناقلات الدعم (SVMS) المدربين مع وبدون الميزات التي توفرها RFET؛نحن أيضا قارنها مع SVM مع ميزات التضمين العصبية الناتجة عن عقوبة المرور.
Feature engineering is an important step in classical NLP pipelines, but machine learning engineers may not be aware of the signals to look for when processing foreign language text. The Russian Feature Extraction Toolkit (RFET) is a collection of feature extraction libraries bundled for ease of use by engineers who do not speak Russian. RFET's current feature set includes features applicable to social media genres of text and to computational social science tasks. We demonstrate the effectiveness of the tool by using it in a personality trait identification task. We compare the performance of Support Vector Machines (SVMs) trained with and without the features provided by RFET; we also compare it to a SVM with neural embedding features generated by Sentence-BERT.
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