DeepDiveAI: Identifying AI Related Documents in Large Scale Literature Data
<|reference_start|>DeepDiveAI: Identifying AI Related Documents in Large Scale Literature Data: This paper presents DeepDiveAI, a comprehensive dataset specifically curated to identify AI-related research papers from a large-scale academic literature database. The dataset was created using an advanced Long Short-Term Memory (LSTM) model trained on a binary classification task to distinguish between AI-related and non-AI-related papers. The model was trained and validated on a vast dataset, achieving high accuracy, precision, recall, and F1-score. The resulting DeepDelveAI dataset comprises over 9.4 million AI-related papers published since Dartmouth Conference, from 1956 to 2024, providing a crucial resource for analyzing trends, thematic developments, and the evolution of AI research across various disciplines.<|reference_end|>
@article{xiaochen2024deepdiveai:,
    title={DeepDiveAI: Identifying AI Related Documents in Large Scale Literature
  Data},
    author={Zhou Xiaochen, Liang Xingzhou, Zou Hui, Lu Yi, Qu Jingjing},
    journal={arXiv preprint arXiv:2408.12871},
    year={2024},
    archivePrefix={arXiv},
    eprint={2408.12871},
    primaryClass={cs.AI}
}