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Register a new userComboNER: A Lightweight All-In-One POS Tagger, Dependency Parser and NER
COMPONER: TAGGER خفيفة الوزن الكل في واحد Tagger، محلل التبعية و NER
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تركز معالجة اللغة الطبيعية الحالية بقوة على زيادة الدقة.يأتي التقدم بتكلفة نماذج فائقة الثقيلة مع مئات الملايين أو حتى مليارات المعلمات.ومع ذلك، فإن المهام النحوية البسيطة مثل وضع العلامات على جزء من الكلام (POS) أو تحليل التبعية أو التعرف على الكيان المسمى (NER) لا تحتاج إلى أكبر النماذج لتحقيق نتائج مقبولة.تمشيا مع هذا الافتراض، نحاول تقليل حجم النموذج الذي ينفذ بشكل مشترك جميع المهام الثلاثة.نقدم Comboner: أداة خفيفة الوزن، أوامر ذات حجم أصغر من المحولات الحديثة.يعتمد على مدمج الكلمات الفرعية المدربة مسبقا بنية الشبكة العصبية المتكررة.يعمل COMBONER على بيانات اللغة البولندية.يحتوي النموذج على مخرجات لوضع العلامات على نقاط البيع والتحليل التبعية و NER.تحتوي ورقةنا على بعض الأفكار من ضبط النموذج الدقيق والتقارير عن نتائجها الإجمالية.
The current natural language processing is strongly focused on raising accuracy. The progress comes at a cost of super-heavy models with hundreds of millions or even billions of parameters. However, simple syntactic tasks such as part-of-speech (POS) tagging, dependency parsing or named entity recognition (NER) do not require the largest models to achieve acceptable results. In line with this assumption we try to minimize the size of the model that jointly performs all three tasks. We introduce ComboNER: a lightweight tool, orders of magnitude smaller than state-of-the-art transformers. It is based on pre-trained subword embeddings and recurrent neural network architecture. ComboNER operates on Polish language data. The model has outputs for POS tagging, dependency parsing and NER. Our paper contains some insights from fine-tuning of the model and reports its overall results.
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