Bi LSTM CRF

Bidirectional Long Short-Term Memory networks coupled with Conditional Random Fields (Bi-LSTM-CRFs) are a powerful sequence labeling technique frequently used in natural language processing tasks, particularly named entity recognition. Current research focuses on applying Bi-LSTM-CRFs to diverse applications, including dataset mention extraction from scientific articles, address parsing for geocoding, and morphological analysis of languages like Japanese. The robustness and relatively high accuracy of Bi-LSTM-CRFs make them valuable tools for improving information extraction and processing across various domains, contributing to advancements in fields like scientific data management and e-commerce recommendation systems.