Efficient Named Entity Recognition with Overlapping and Nested Mentions Using Hypergraphs and Neural Network

Abstract—Named Entity Recognition (NER)

remains a pivotal task in Natural Language

Processing (NLP), crucial for information

extraction and document classification applications.

Traditional models like linear-chain Conditional

Random Fields (CRFs) struggle to handle

overlapping and nested mentions, which frequently

occur in specialized domainssuch as biomedical and

legal texts. This study addresses these limitations by

proposing an advanced model that integrates

mention hypergraphs, mention separators, and

neural networks to efficiently recognize complex

entity structures while maintaining linear time

complexity. The model's performance is tested

across multiple datasets, including ACE2004,

ACE2005, and GENIA, demonstrating significant

improvements in F1 scores and scalability

compared to baseline models. The proposed

approach also introduces a dynamic feature

composition mechanism that enhances the

recognition of rare and unseen words, making it

adaptable to diverse text genres, including noisy

and informal data. The results suggest that the

model excels in traditional NER tasks and extends

its applicability to low-resource settings and real-

world datasets. Future work could further optimize

computational efficiency and explore domain-

specific adaptations to refine performance in

specialized fields.

Keywords—Named Entity Recognition (NER),

Overlapping Mentions, Nested Mentions,

Hypergraph Models, Neural Networks, Mention

Separators, Entity Recognition Scalability, Sequence

Labeling, Biomedical Text Processing, Semantic

Hierarchies.