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Register a new userUTFPR at SemEval-2021 Task 1: Complexity Prediction by Combining BERT Vectors and Classic Features
Outfpr في مهمة Semeval-2021 1: تنبؤ التعقيد من خلال الجمع بين ناقلات Bert والسمات الكلاسيكية
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نحن تصف أنظمة Utfpr المقدمة إلى تنبؤ التعقيد المعجمي المهمة المشتركة في Semeval 2021. إنهم يقومون بتنبؤ التعقيد من خلال الجمع بين الميزات الكلاسيكية، مثل تردد الكلمة، تردد N-Gram، طول الكلمة، وعدد الحواس، مع ناقلات Bert.نحن نختبر العديد من مجموعات الميزات ونماذج تعلم الآلات في تجاربنا وتجد أن ناقلات بيرت، حتى لو لم تكن محسنة للمهمة في متناول اليد، هي مكملة كبيرة للميزات الكلاسيكية.نجد أيضا أن استخدام مبدأ التكوين يمكن أن يساعد في تنبؤ تعقيد العبارة.أنظمتنا تضع 45 من أصل 55 من الكلمات الواحدة والثمانين من أصل 38 للعبارات.
We describe the UTFPR systems submitted to the Lexical Complexity Prediction shared task of SemEval 2021. They perform complexity prediction by combining classic features, such as word frequency, n-gram frequency, word length, and number of senses, with BERT vectors. We test numerous feature combinations and machine learning models in our experiments and find that BERT vectors, even if not optimized for the task at hand, are a great complement to classic features. We also find that employing the principle of compositionality can potentially help in phrase complexity prediction. Our systems place 45th out of 55 for single words and 29th out of 38 for phrases.
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