Token Classification

Token classification, a core natural language processing task, aims to assign labels to individual words or sub-word units within a text, enabling applications like named entity recognition and part-of-speech tagging. Current research emphasizes leveraging large language models (LLMs), particularly transformer-based architectures, often incorporating techniques like bidirectional representations and fine-tuning on domain-specific datasets to improve accuracy and address challenges such as data imbalance and noisy data. This work is significant for advancing information extraction from diverse text sources, including those with complex layouts or code-mixed languages, and improving the performance of downstream applications in various fields.