Using Zero-Shot Transfer to Initialize azWikiNER, a Gold Standard Named Entity Corpus for the Azerbaijani Language

• Published in: Text, Speech, and Dialogue pp. 305 - 317
• Main Authors: Ibiyev, Kamran, Novak, Attila
• Format: Book Chapter
• Language: English
• Published: Cham Springer International Publishing
• Series: Lecture Notes in Computer Science
• Subjects: Azerbaijani; M-BERT; Named Entity Recognition; WikiAnn; XLM-RoBERTa
• ISBN: 303083526X; 9783030835262
• ISSN: 0302-9743; 1611-3349
• DOI: 10.1007/978-3-030-83527-9_26

Named Entity Recognition (NER) is one of the primary fields of Natural Language Processing, focused on analyzing and determining the entities in a given text. In this paper, we present a gold standard named entity dataset for Azerbaijani created from the Azerbaijani portion of WikiAnn, a ‘silver standard’ NER dataset generated from Wikipedia. In a zero-shot cross-lingual transfer scenario, we used an M-BERT-based NER model trained on the English Ontonotes corpus to add new entity types to the corpus. The output of the model was then hand-corrected. We evaluate the accuracy of the original WikiAnn corpus, the zero-shot performance of two models trained on the Ontonotes corpus, and two transformer-based NER models trained on the training part of the final corpus: one based on M-BERT and another based XLM-RoBERTa. We release the corpus and the trained models to the public.