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Summary of Deepdiveai: Identifying Ai Related Documents in Large Scale Literature Data, by Zhou Xiaochen et al.

by Zhou Xiaochen, Liang Xingzhou, Zou Hui, Lu Yi, Qu Jingjing

First submitted to arxiv on: 23 Aug 2024

Categories

  • Main: Artificial Intelligence (cs.AI)
  • Secondary: None

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GrooveSquid.com Paper Summaries

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Summary difficulty Written by Summary
High Paper authors High Difficulty Summary
Read the original abstract here
Medium GrooveSquid.com (original content) Medium Difficulty Summary
The proposed method integrates expert knowledge with advanced models to create an AI-related literature dataset, named DeepDiveAI. The approach consists of two stages: training an LSTM model using expert-curated classification datasets to classify coarse AI related records, and then refining the results using a BERT binary classifier. This workflow enables efficient and accurate identification of AI-related literature from large-scale datasets.
Low GrooveSquid.com (original content) Low Difficulty Summary
This paper creates a new dataset called DeepDiveAI that helps find important information about artificial intelligence in big libraries. They use two steps to make this happen: first, they train a special kind of computer model using expert-approved lists of what’s important and what’s not. Then, they use another tool to make sure the results are super accurate. The goal is to help people quickly and easily find AI-related information.

Keywords

» Artificial intelligence  » Bert  » Classification  » Lstm  

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