Named Entity Recognition (NER) models play a crucial role in various NLP tasks, including
information extraction (IE) and text understanding. In academic writing, references to …

The emergence of large language models (LLMs) has marked a significant milestone in the
evolution of natural language processing. With the expanded use of LLMs in multiple fields …

Fine-grained few-shot entity extraction in the chemical domain faces two unique challenges.
First, compared with entity extraction tasks in the general domain, sentences from chemical …

In medical research, the rapid proliferation of condition-specific studies has led to an
information overload, making it challenging for researchers and practitioners to stay abreast …

Regarding the existing models for feature extraction of complex similar entities, there are
problems in the utilization of relative position information and the ability of key feature …

The aviation products' manufacturing industry is undergoing a profound transformation
towards intelligence, among which the construction of a knowledge graph specifically for the …

This paper examines how geometric deep learning techniques may be employed to analyze
academic collaboration networks (ACNs) and how using textual information drawn from …

Joint entity-relation extraction is a fundamental task in the construction of large-scale
knowledge graphs. This task relies not only on the semantics of the text span but also on its …

Named Entity Recognition (NER) is important in fields where researchers have to review
large amounts of scientific text, such as plant pathology. However, NER is especially difficult …