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Register a new userOptimizing a Supervised Classifier for a Difficult Language Identification Problem
تحسين مصنف إشراف لمشكلة تحديد اللغة الصعبة
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توضح هذه الورقة النظام الذي طورته STATITATIAIRE D'Analyze StatistIck Des Tyses لتحديد الهوية الحالية (DLI) المهمة المشتركة لعام 2021. هذه المهمة صعبة للغاية لأن المواد تتكون من تعليقات YouTube قصيرة، مكتوبة في البرنامج النصي الروماني، من ثلاثةلغات Dravidian ذات الصلة ارتباطا وثيقا، وفئة رابعة تتكون من العديد من اللغات الأخرى في أبعاد متفاوتة، كلها مختلطة مع اللغة الإنجليزية.يتكون النظام المقترح من نموذج الانحدار اللوجستي الذي يستخدمه كلما يتميز فقط N-Grams من الشخصيات بحد أقصى طول 5. بعد تحسينها من حيث ترجيح الميزة ومعلمات المصنف، فهو المرتبة الأولى في التحدي.تقوم التحليلات الإضافية التي أجريت تسطير أهمية التحسين، خاصة عندما يكون مقياس الفعالية هو الماكرو F1.
This paper describes the system developed by the Laboratoire d'analyse statistique des textes for the Dravidian Language Identification (DLI) shared task of VarDial 2021. This task is particularly difficult because the materials consists of short YouTube comments, written in Roman script, from three closely related Dravidian languages, and a fourth category consisting of several other languages in varying proportions, all mixed with English. The proposed system is made up of a logistic regression model which uses as only features n-grams of characters with a maximum length of 5. After its optimization both in terms of the feature weighting and the classifier parameters, it ranked first in the challenge. The additional analyses carried out underline the importance of optimization, especially when the measure of effectiveness is the Macro-F1.
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