Span-based Neural Model for Multilingual Flat and Nested Named Entity Recognition
Mohammad Golam Sohrab, Md. Shoaib Bhuiyan · 2021 IEEE 10th Global Conference on Consumer Electronics (GCCE) · 2021
Named entity recognition (NER) is an important task of finding entities with specific semantic types such as Protein, Cell, and RNA in text by addressing mostly with sequence labeling-based approach. In this paper, we present a bidirectional encoder representations from transformers (BERT)-based neural exhaustive approach that addresses flat and nested entities by reasoning over all the spans within a specified maximum span length. We evaluate the BERT-based span representation approach over the multilingual data sets of different domains including biomedical and general to address flat and nested entities. Experiment results show that span-aware-based approach is very effective to leverage NER.