المفاهيم الأساسية
Community challenges in biomedical text mining play a crucial role in advancing technology innovation and interdisciplinary collaboration, fostering the development of state-of-the-art solutions for data mining and information processing in biomedical research.
الملخص
The field of biomedical research has seen a surge in textual data accumulation from various sources. Biomedical text mining, particularly community challenges, has emerged as a solution to efficiently process and analyze this vast amount of data. These challenges promote technology innovation, interdisciplinary collaboration, and have significant implications for translational informatics applications.
Key points:
Biomedical text mining addresses the challenge of manually processing extensive textual resources.
Community challenges provide platforms for researchers to develop cutting-edge solutions.
Tasks include named entity recognition, relation extraction, knowledge graph construction, and more.
The review highlights the contributions and limitations of these community challenges.
Future directions focus on comprehensive evaluation benchmarks, multi-source data handling, leveraging domain-specific knowledge, ensuring data privacy, enhancing interpretability, and integrating with clinical applications.
الإحصائيات
Over the past few decades, the field of biomedical research has witnessed a remarkable growth in the accumulation of extensive amounts of textual data[1].
BioNLP techniques have found extensive applications in scientific research and clinical practice[7].
In 2023, based on the benchmark of CBLUE [64], CCKS organized a task which transformed various NLP tasks within different medical scenarios into prompt-based language generation tasks[59].
اقتباسات
"No datasets were created in this study. The datasets in evaluation tasks of community challenges can be found in related websites or papers." - Content Source