A Cross-Lingual Framework for Named Entity Dataset Construction Using Knowledge Transfer

In this study, a novel approach for creating a top-performing Persian named entity recognition dataset using cross-lingual transfer learning has been proposed. The method proposed here can overcome the main limitations that exist in the existing projection-based approaches through translation-aware alignment, dual matching, and noise filtering methods. The created dataset works well in many different named entity recognition models. This paper proposes a new approach and a valuable linguistic resource for low-resource language processing tasks. In future work, we intend to focus on extending the proposed method into new languages, employing active learning methods, and improving the alignment step with the help of multilingual representations.

کلیدواژه ها:

Named Entity Recognition (NER) ، Cross-Lingual Transfer Learning ، Low-Resource Languages ، Natural Language Processing ، Annotation Projection