Long-Range Named Entity Recognition: A Comprehensive Survey

The exponential growth of unstructured digital text has created a pressing need for sophisticated Natural Language Processing (NLP) methods to extract meaningful information. Named Entity Recognition (NER), the task of identifying and classifying named entities in text, is a cornerstone of this effort. While traditional NER has achieved remarkable success on short, self-contained texts, its application to long-form docu-ments—such as legal contracts, clinical records, and scientific literature—presents formidable challenges. This survey provides a comprehensive analysis of the state-of-the-art in Long-Range Named Entity Recognition. We trace the evolution from classical statistical models to the rise of Transformers, detailing the inher-ent quadratic complexity of models like BERT that limits their scalability. We conduct an in-depth exploration of the primary architectural paradigms designed to overcome this bottleneck: efficient Transformers that employ sparse attention mechanisms, and graph-based approaches that model explicit relational struc-tures within documents. Furthermore, we investigate critical challenges, including the data scarcity problem in specialized domains and unique linguistic complexities in multilingual con-texts. Drawing from recent analyses, we synthesize persistent open problems in document-level information extraction, focusing on long-distance coreference resolution and the need for robust, multi-step reasoning. Finally, we chart a course for future research, postulating that the next generation of solutions will be found in hybrid architectures that synergistically combine the strengths of deep sequential encoders with structured reasoning frameworks

Named Entity Recognition, Long-Range NER, Transformers

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