39 citations · 101 across the 14 of their papers we have counts for
7 papers · 1 filter
Enhancing Scientific Named Entity Recognition via Large Language Models: A Type-driven Multi-task Learning Approach
Tong Bao, Yi Zhao, Heng Zhang +1
Scientific named entity recognition (SciNER) plays a crucial role in information extraction and knowledge discovery from scientific texts. Recently, large language models (LLMs) ha…
Exploring the relationship between team institutional composition and novelty in academic papers based on fine-grained knowledge entities
Ziling Chen, Chengzhi Zhang, Heng Zhang +3
The composition of author teams is an important factor influencing the novelty of academic papers. However, existing studies have paid limited attention to the role of institutiona…
Revealing the Technology Development of Natural Language Processing: A Scientific Entity-Centric Perspective
Heng Zhang, Chengzhi Zhang, Yuzhuo Wang
Most studies on technology development have been conducted from a thematic perspective, but the topics are coarse-grained and insufficient to accurately represent technology. The d…
Automatic Generation of Highlights for Academic Paper Via Prompt-based Learning
Yi Xiang, Chengzhi Zhang, Heng Zhang
Highlights provide a concise summary of the main contributions of an academic paper and help readers quickly understand its focus. However, many journals do not provide highlights,…
The Effect of Gender Diversity on Scientific Team Impact: A Team Roles Perspective
Yi Zhao, Yongjun Zhu, Donghun Kim +4
The influence of gender diversity on the success of scientific teams is of great interest to academia. However, prior findings remain inconsistent, and most studies operationalize…
Automated Generation of Research Workflows from Academic Papers: A Full-text Mining Framework
Heng Zhang, Chengzhi Zhang
The automated generation of research workflows is essential for improving the reproducibility of research and accelerating the paradigm of "AI for Science". However, existing metho…
Enhancing Abstractive Summarization of Scientific Papers Using Structure Information
Tong Bao, Heng Zhang, Chengzhi Zhang
Abstractive summarization of scientific papers has always been a research focus, yet existing methods face two main challenges. First, most summarization models rely on Encoder-Dec…