Neural Coreference

Neural coreference resolution aims to automatically identify all mentions of the same real-world entity within a text, a crucial task for natural language understanding. Current research focuses on improving the accuracy and efficiency of these systems, exploring architectures like end-to-end neural models, sentence-incremental approaches to reduce computational complexity, and hybrid rule-neural systems combining heuristic rules with neural networks, often leveraging pre-trained language models like BERT. These advancements are driving progress in various NLP applications, including question answering, information extraction, and knowledge base construction. While incorporating linguistic theories like centering theory shows limited additional benefit with modern models, ongoing work emphasizes improving handling of long documents and multilingual contexts.