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        <title>PIEKM: ML-based Procedural Information Extraction and Knowledge Management System for Materials Science Literature</title>
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        <namePart type="given">Huichen</namePart>
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            <title>Proceedings of the 2nd Conference of the Asia-Pacific Chapter of the Association for Computational Linguistics and the 12th International Joint Conference on Natural Language Processing: System Demonstrations</title>
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            <namePart type="given">Wray</namePart>
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    <abstract>The published materials science literature contains abundant description information about synthesis procedures that can help discover new material areas, deepen the study of materials synthesis, and accelerate its automated planning. Nevertheless, this information is expressed in unstructured text, and manually processing and assimilating useful information is expensive and time-consuming for researchers. To address this challenge, we develop a Machine Learning-based procedural information extraction and knowledge management system (PIEKM) that extracts procedural information recipe steps, figures, and tables from materials science articles, and provides information retrieval capability and the statistics visualization functionality. Our system aims to help researchers to gain insights and quickly understand the connections among massive data. Moreover, we demonstrate that the machine learning-based system performs well in low-resource scenarios (i.e., limited annotated data) for domain adaption.</abstract>
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        <url>https://aclanthology.org/2022.aacl-demo.7</url>
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%0 Conference Proceedings
%T PIEKM: ML-based Procedural Information Extraction and Knowledge Management System for Materials Science Literature
%A Yang, Huichen
%A Aguirre, Carlos
%A Hsu, William
%Y Buntine, Wray
%Y Liakata, Maria
%S Proceedings of the 2nd Conference of the Asia-Pacific Chapter of the Association for Computational Linguistics and the 12th International Joint Conference on Natural Language Processing: System Demonstrations
%D 2022
%8 November
%I Association for Computational Linguistics
%C Taipei, Taiwan
%F yang-2022-piekm
%X The published materials science literature contains abundant description information about synthesis procedures that can help discover new material areas, deepen the study of materials synthesis, and accelerate its automated planning. Nevertheless, this information is expressed in unstructured text, and manually processing and assimilating useful information is expensive and time-consuming for researchers. To address this challenge, we develop a Machine Learning-based procedural information extraction and knowledge management system (PIEKM) that extracts procedural information recipe steps, figures, and tables from materials science articles, and provides information retrieval capability and the statistics visualization functionality. Our system aims to help researchers to gain insights and quickly understand the connections among massive data. Moreover, we demonstrate that the machine learning-based system performs well in low-resource scenarios (i.e., limited annotated data) for domain adaption.
%U https://aclanthology.org/2022.aacl-demo.7
%P 57-62

Markdown (Informal)

[PIEKM: ML-based Procedural Information Extraction and Knowledge Management System for Materials Science Literature](https://aclanthology.org/2022.aacl-demo.7) (Yang et al., AACL-IJCNLP 2022)

• PIEKM: ML-based Procedural Information Extraction and Knowledge Management System for Materials Science Literature (Yang et al., AACL-IJCNLP 2022)

ACL

• Huichen Yang, Carlos Aguirre, and William Hsu. 2022. PIEKM: ML-based Procedural Information Extraction and Knowledge Management System for Materials Science Literature. In Proceedings of the 2nd Conference of the Asia-Pacific Chapter of the Association for Computational Linguistics and the 12th International Joint Conference on Natural Language Processing: System Demonstrations, pages 57–62, Taipei, Taiwan. Association for Computational Linguistics.