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تسجيل مستخدم جديدANEA: Distant Supervision for Low-Resource Named Entity Recognition
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نشر من قبل
Michael A. Hedderich
تاريخ النشر
2021
مجال البحث
الهندسة المعلوماتية
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English
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Distant supervision allows obtaining labeled training corpora for low-resource settings where only limited hand-annotated data exists. However, to be used effectively, the distant supervision must be easy to gather. In this work, we present ANEA, a tool to automatically annotate named entities in texts based on entity lists. It spans the whole pipeline from obtaining the lists to analyzing the errors of the distant supervision. A tuning step allows the user to improve the automatic annotation with their linguistic insights without labelling or checking all tokens manually. In six low-resource scenarios, we show that the F1-score can be increased by on average 18 points through distantly supervised data obtained by ANEA.
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