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Register a new userLasige-BioTM at ProfNER: BiLSTM-CRF and contextual Spanish embeddings for Named Entity Recognition and Tweet Binary Classification
LASIGE-BIOTM في الوكيل: Bilstm-CRF و Auttastual Spanish Ageddings for Named Entity Areachition و Tweet تصنيف ثنائي
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تصف الورقة مشاركة فريق Lasig-Biotm في المسارات الفرعية A و B من PROFORNER، والتي تستند إلى: I) نموذج Bilstm-CRF الذي يرفع إلى تضيير الكلمات السياقية والكلفة الكلاسيكية للاعتراف بتذكر وتصنيفها، والثاني)على وحدة نمطية قائمة على القواعد لتصنيف تغريدات.في مرحلة التقييم، حقق نموذجنا درجة F1 من 0.917 (0،031 أكثر من المتوسط) في المسار الفرعي ونتيجة F1 من 0.727 (0،034 أقل من المتوسط) في المسار الفرعي B.
The paper describes the participation of the Lasige-BioTM team at sub-tracks A and B of ProfNER, which was based on: i) a BiLSTM-CRF model that leverages contextual and classical word embeddings to recognize and classify the mentions, and ii) on a rule-based module to classify tweets. In the Evaluation phase, our model achieved a F1-score of 0.917 (0,031 more than the median) in sub-track A and a F1-score of 0.727 (0,034 less than the median) in sub-track B.
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