Data Expansion for Named Entity Recognition based on migration learning

The pivotal role of named entity recognition in the field of natural language comprehension significantly influences numerous tasks, including the likes of machine translation, the distillation of events, and the fabrication of knowledge graphs. Confronting the issue of data scarcity, the method initially entails the acquisition of dense semantic insights through the preliminary training of the BERT framework on an expansive textual database. Subsequent refinement of these initially trained models is tailored specifically for nuanced NER assignments, enhancing the detection of sector-specific entities. This refinement is complemented by the integration of character-level feature depiction and the employment of Conditional Random Field layers, augmenting the precision of entity demarcation. A thorough evaluation conducted using datasets from NER that are accessible to the public corroborates the efficacy of this approach, showcasing commendable results in precision and retrieval rates. When juxtaposed with the conventional methodologies predicated on rules or feature engineering, this innovative approach demonstrates notable enhancements in the identification of a diverse array of entity categories, evidenced by an increment of $0.02 \%$ in precision and a rise of $3.21 \%$ in retrieval.