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    <abstract>Knowledge extraction from scientific literature is a major issue, crucial to promoting transparency, reproducibility, and innovation in the research community. In this work, we present a novel approach towards the identification, extraction and analysis of dataset and code/software mentions within scientific literature. We introduce a comprehensive dataset, synthetically generated by ChatGPT and meticulously curated, augmented, and expanded with real snippets of scientific text from full-text publications in Computer Science using a human-in-the-loop process. The dataset contains snippets highlighting mentions of the two research artifact (RA) types: dataset and code/software, along with insightful metadata including their Name, Version, License, URL as well as the intended Usage and Provenance. We also fine-tune a simple Large Language Model (LLM) using Low-Rank Adaptation (LoRA) to transform the Research Artifact Analysis (RAA) into an instruction-based Question Answering (QA) task. Ultimately, we report the improvements in performance on the test set of our dataset when compared to other base LLM models. Our method provides a significant step towards facilitating accurate, effective, and efficient extraction of datasets and software from scientific papers, contributing to the challenges of reproducibility and reusability in scientific research.</abstract>
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%0 Conference Proceedings
%T Empowering Knowledge Discovery from Scientific Literature: A novel approach to Research Artifact Analysis
%A Stavropoulos, Petros
%A Lyris, Ioannis
%A Manola, Natalia
%A Grypari, Ioanna
%A Papageorgiou, Haris
%Y Tan, Liling
%Y Milajevs, Dmitrijs
%Y Chauhan, Geeticka
%Y Gwinnup, Jeremy
%Y Rippeth, Elijah
%S Proceedings of the 3rd Workshop for Natural Language Processing Open Source Software (NLP-OSS 2023)
%D 2023
%8 December
%I Association for Computational Linguistics
%C Singapore
%F stavropoulos-etal-2023-empowering
%X Knowledge extraction from scientific literature is a major issue, crucial to promoting transparency, reproducibility, and innovation in the research community. In this work, we present a novel approach towards the identification, extraction and analysis of dataset and code/software mentions within scientific literature. We introduce a comprehensive dataset, synthetically generated by ChatGPT and meticulously curated, augmented, and expanded with real snippets of scientific text from full-text publications in Computer Science using a human-in-the-loop process. The dataset contains snippets highlighting mentions of the two research artifact (RA) types: dataset and code/software, along with insightful metadata including their Name, Version, License, URL as well as the intended Usage and Provenance. We also fine-tune a simple Large Language Model (LLM) using Low-Rank Adaptation (LoRA) to transform the Research Artifact Analysis (RAA) into an instruction-based Question Answering (QA) task. Ultimately, we report the improvements in performance on the test set of our dataset when compared to other base LLM models. Our method provides a significant step towards facilitating accurate, effective, and efficient extraction of datasets and software from scientific papers, contributing to the challenges of reproducibility and reusability in scientific research.
%R 10.18653/v1/2023.nlposs-1.5
%U https://aclanthology.org/2023.nlposs-1.5/
%U https://doi.org/10.18653/v1/2023.nlposs-1.5
%P 37-53

Markdown (Informal)

[Empowering Knowledge Discovery from Scientific Literature: A novel approach to Research Artifact Analysis](https://aclanthology.org/2023.nlposs-1.5/) (Stavropoulos et al., NLPOSS 2023)

• Empowering Knowledge Discovery from Scientific Literature: A novel approach to Research Artifact Analysis (Stavropoulos et al., NLPOSS 2023)

ACL

• Petros Stavropoulos, Ioannis Lyris, Natalia Manola, Ioanna Grypari, and Haris Papageorgiou. 2023. Empowering Knowledge Discovery from Scientific Literature: A novel approach to Research Artifact Analysis. In Proceedings of the 3rd Workshop for Natural Language Processing Open Source Software (NLP-OSS 2023), pages 37–53, Singapore. Association for Computational Linguistics.