# DeepDiveAI: Identifying AI Related Documents in Large Scale Literature Data

arXiv: https://arxiv.org/abs/2408.12871 · HTML: https://mlsanity.com/paper/2408.12871/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 10 of its 29 references that are in the map.
- First posted on arXiv 23 Aug 2024.

## Builds on (most cited first, 10 of 10)

- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [Distributed Representations of Words and Phrases and their Compositionality](https://mlsanity.com/paper/1310.4546/) (2013)
- [Qwen Technical Report](https://mlsanity.com/paper/2309.16609/) (2023)
- [GPTs are GPTs: An Early Look at the Labor Market Impact Potential of Large Language Models](https://mlsanity.com/paper/2303.10130/) (2023)
- [Deep Learning Based Text Classification: A Comprehensive Review](https://mlsanity.com/paper/2004.03705/) (2020)
- [A Comparison of LSTM and BERT for Small Corpus](https://mlsanity.com/paper/2009.05451/) (2020)
- [Large Language Models Are Zero-Shot Text Classifiers](https://mlsanity.com/paper/2312.01044/) (2023)
- [A brief history of AI: how to prevent another winter (a critical review)](https://mlsanity.com/paper/2109.01517/) (2021)
- [Which type of citation analysis generates the most accurate taxonomy of scientific and technical knowledge?](https://mlsanity.com/paper/1511.05078/) (2015)
- [AI on AI: Exploring the Utility of GPT as an Expert Annotator of AI Publications](https://mlsanity.com/paper/2403.09097/) (2024)

## Built on by (most cited first, 2 of 2)

- [Reducing Hallucinations in LLM-based Scientific Literature Analysis Using Peer Context Outlier Detection](https://mlsanity.com/paper/2604.01461/) (2026)
- [A Global Dataset Mapping the AI Innovation from Academic Research to Industrial Patents](https://mlsanity.com/paper/2503.09257/) (2025)

## Abstract (arXiv)

In this paper, we propose a method to automatically classify AI-related documents from large-scale literature databases, leading to the creation of an AI-related literature dataset, named DeepDiveAI. The dataset construction approach integrates expert knowledge with the capabilities of advanced models, structured across two global stages. In the first stage, expert-curated classification datasets are used to train an LSTM model, which classifies coarse AI related records from large-scale datasets. In the second stage, we use Qwen2.5 Plus to annotate a random 10% of the coarse AI-related records, which are then used to train a BERT binary classifier. This step further refines the coarse AI related record set to obtain the final DeepDiveAI dataset. Evaluation results demonstrate that the entire workflow can efficiently and accurately identify AI-related literature from large-scale datasets.