معرفی
Liangping Ding is a Researcher at the Manchester Institute of Innovation Research, University of Manchester, specializing in computational analysis of scientific literature and research systems.
Research Interests:
- Science of Science: Investigating scientific workforce mobility (particularly Chinese researchers), knowledge diffusion, and research impact metrics
- Natural Language Processing: Developing deep learning models for biomedical text mining, named entity recognition, and keyphrase extraction in Chinese scientific literature
- Machine Learning Applications: Creating cascading frameworks for drug target extraction and confidence calibration techniques
- Climate Change Research: Analyzing scientific evidence evolution in IPCC assessment reports
- Generative AI: Exploring AI's role in scientific innovation and discovery
Publication analysis reveals three distinct research phases: foundational work (2019-2021) focused on medical text mining using BERT models for move recognition and keyphrase extraction; methodological development (2022) advancing named entity recognition via distant supervision; and expanded scope (2023-2025) incorporating climate change analysis, drug target extraction, and science of science investigations. Recent work demonstrates growing emphasis on generative AI and international scientific collaboration patterns.
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