{"title":"Tackling Structured Knowledge Extraction from Polymer Nanocomposite Literature as an NER/RE Task with seq2seq","authors":"Bingyin Hu, Anqi Lin, L. Catherine Brinson","doi":"10.1007/s40192-024-00363-5","DOIUrl":null,"url":null,"abstract":"<p>There is an urgent need for ready access to published data for advances in materials design, and natural language processing (NLP) techniques offer a promising solution for extracting relevant information from scientific publications. In this paper, we present a domain-specific approach utilizing a Transformer-based model, T5, to automate the generation of sample lists in the field of polymer nanocomposites (PNCs). Leveraging large-scale corpora, we employ advanced NLP techniques including named entity recognition and relation extraction to accurately extract sample codes, compositions, group references, and properties from PNC papers. The T5 model demonstrates competitive performance in relation extraction using a TANL framework and an EM-style input sequence. Furthermore, we explore multi-task learning and joint-entity-relation extraction to enhance efficiency and address deployment concerns. Our proposed methodology, from corpora generation to model training, showcases the potential of structured knowledge extraction from publications in PNC research and beyond.</p>","PeriodicalId":13604,"journal":{"name":"Integrating Materials and Manufacturing Innovation","volume":"28 1","pages":""},"PeriodicalIF":2.4000,"publicationDate":"2024-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Integrating Materials and Manufacturing Innovation","FirstCategoryId":"88","ListUrlMain":"https://doi.org/10.1007/s40192-024-00363-5","RegionNum":3,"RegionCategory":"材料科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q3","JCRName":"ENGINEERING, MANUFACTURING","Score":null,"Total":0}
引用次数: 0
Abstract
There is an urgent need for ready access to published data for advances in materials design, and natural language processing (NLP) techniques offer a promising solution for extracting relevant information from scientific publications. In this paper, we present a domain-specific approach utilizing a Transformer-based model, T5, to automate the generation of sample lists in the field of polymer nanocomposites (PNCs). Leveraging large-scale corpora, we employ advanced NLP techniques including named entity recognition and relation extraction to accurately extract sample codes, compositions, group references, and properties from PNC papers. The T5 model demonstrates competitive performance in relation extraction using a TANL framework and an EM-style input sequence. Furthermore, we explore multi-task learning and joint-entity-relation extraction to enhance efficiency and address deployment concerns. Our proposed methodology, from corpora generation to model training, showcases the potential of structured knowledge extraction from publications in PNC research and beyond.
期刊介绍:
The journal will publish: Research that supports building a model-based definition of materials and processes that is compatible with model-based engineering design processes and multidisciplinary design optimization; Descriptions of novel experimental or computational tools or data analysis techniques, and their application, that are to be used for ICME; Best practices in verification and validation of computational tools, sensitivity analysis, uncertainty quantification, and data management, as well as standards and protocols for software integration and exchange of data; In-depth descriptions of data, databases, and database tools; Detailed case studies on efforts, and their impact, that integrate experiment and computation to solve an enduring engineering problem in materials and manufacturing.