Summary of Extraction Of Research Objectives, Machine Learning Model Names, and Dataset Names From Academic Papers and Analysis Of Their Interrelationships Using Llm and Network Analysis, by S. Nishio et al.
Extraction of Research Objectives, Machine Learning Model Names, and Dataset Names from Academic Papers and Analysis of Their Interrelationships Using LLM and Network Analysis
by S. Nishio, H. Nonaka, N. Tsuchiya, A. Migita, Y. Banno, T. Hayashi, H. Sakaji, T. Sakumoto, K. Watabe
First submitted to arxiv on: 22 Aug 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI); Computation and Language (cs.CL)
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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 A novel methodology is proposed in this study for extracting combinations of tasks, machine learning models, and datasets from scientific papers, aiming to facilitate automatic recommendation of suitable methods. The approach leverages LLM, embedding model, and network clustering techniques to analyze relationships between extracted information. Evaluations on financial domain papers demonstrate the method’s effectiveness, showcasing potential applications in areas like ESG data. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This study helps people identify the best machine learning models and datasets for specific tasks. It does this by analyzing academic papers and extracting important information like tasks, methods, and dataset names. The researchers use special techniques to understand relationships between these pieces of information. They test their method on financial papers and find it works well. This could be useful in areas like using environmental, social, and governance data. |
Keywords
» Artificial intelligence » Clustering » Embedding » Machine learning
