{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,7]],"date-time":"2026-07-07T07:32:41Z","timestamp":1783409561601,"version":"3.54.6"},"reference-count":50,"publisher":"IOP Publishing","issue":"4","license":[{"start":{"date-parts":[[2021,7,15]],"date-time":"2021-07-15T00:00:00Z","timestamp":1626307200000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/creativecommons.org\/licenses\/by\/4.0"},{"start":{"date-parts":[[2021,7,15]],"date-time":"2021-07-15T00:00:00Z","timestamp":1626307200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/iopscience.iop.org\/info\/page\/text-and-data-mining"}],"funder":[{"DOI":"10.13039\/100006231","name":"Brookhaven National Laboratory","doi-asserted-by":"crossref","award":["Laboratory Directed Research and Development 18-05"],"award-info":[{"award-number":["Laboratory Directed Research and Development 18-05"]}],"id":[{"id":"10.13039\/100006231","id-type":"DOI","asserted-by":"crossref"}]}],"content-domain":{"domain":["iopscience.iop.org"],"crossmark-restriction":false},"short-container-title":["Mach. Learn.: Sci. Technol."],"published-print":{"date-parts":[[2021,12,1]]},"abstract":"<jats:title>Abstract<\/jats:title>\n               <jats:p>Using synchrotron light sources, such as the National Synchrotron Light Source II at Brookhaven National Laboratory, scientists in fields as diverse as physics, biology, and materials science, identify the atomic structure, chemical composition, or other important properties of varied specimens. x-ray spectroscopy from light sources is particularly valuable for materials research with vast information available about reference spectra in the scientific literature. However, as the technique is applicable to many science domains, searching for information about select x-ray spectroscopy spectra is impeded by the sheer number of publications. Moreover, useful information about the context of an experiment or figures presented in papers can be buried among the details, which takes time to assess. This work presents a scientific literature mining system that supports data acquisition, information extraction, and user interaction for referencing x-ray spectra identification and spectral interpretation. The goal is to provide efficient access to useful spectral data to researchers who may spend only a few days at a synchrotron light source. With this system, users browse a classification tree for papers arranged according to x-ray spectroscopic methods, chemical elements, and x-ray absorption spectroscopy edges. Relevant figures are extracted with sentences from the paper that explain them, known as \u2018figure explanatory text.\u2019 Notably, this system focuses on semantic aspects (logical analysis) to find figure explanatory text using deep contextualized word embeddings techniques and contains an interface to obtain labeled data from domain experts that is used to evaluate and improve the model.<\/jats:p>","DOI":"10.1088\/2632-2153\/abf751","type":"journal-article","created":{"date-parts":[[2021,4,14]],"date-time":"2021-04-14T06:18:22Z","timestamp":1618381102000},"page":"045007","update-policy":"https:\/\/doi.org\/10.1088\/crossmark-policy","source":"Crossref","is-referenced-by-count":8,"title":["Advances in scientific literature mining for interpreting materials characterization"],"prefix":"10.1088","volume":"2","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-0153-6646","authenticated-orcid":false,"given":"Gilchan","family":"Park","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-2120-6521","authenticated-orcid":false,"given":"Line","family":"Pouchard","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"266","published-online":{"date-parts":[[2021,7,15]]},"reference":[{"key":"mlstabf751bib1","article-title":"National Synchrotron Light Source II (NSLS-II)"},{"key":"mlstabf751bib2","first-page":"p 6","article-title":"FigSum: automatically generating structured text summaries for figures in biomedical literature","volume":"vol 2009","author":"Agarwal","year":"2009"},{"key":"mlstabf751bib3","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1145\/2094072.2094075","article-title":"Summarizing figures, tables, and algorithms in scientific publications to augment search results","volume":"30","author":"Bhatia","year":"2012","journal-title":"ACM Trans. Inf. Syst."},{"key":"mlstabf751bib4","article-title":"PyMuPDF","author":"Liu"},{"key":"mlstabf751bib5","first-page":"1","article-title":"A benchmark and evaluation for text extraction from pdf","author":"Bast","year":"2017"},{"key":"mlstabf751bib6","first-page":"55","article-title":"Recent trends in deep learning based natural language processing","volume":"13","author":"Young","year":"2018"},{"key":"mlstabf751bib7","doi-asserted-by":"crossref","DOI":"10.18653\/v1\/P19-1452","article-title":"BERT rediscovers the classical NLP pipeline","author":"Tenney","year":"2019"},{"key":"mlstabf751bib8","doi-asserted-by":"publisher","DOI":"10.1038\/sdata.2018.111","article-title":"Auto-generated materials database of Curie and N\u00e9el temperatures via semi-supervised relationship extraction","volume":"5","author":"Court","year":"2018","journal-title":"Sci. Data"},{"key":"mlstabf751bib9","doi-asserted-by":"publisher","first-page":"1894","DOI":"10.1021\/acs.jcim.6b00207","article-title":"ChemDataExtractor: a toolkit for automated extraction of chemical information from the scientific literature","volume":"56","author":"Swain","year":"2016","journal-title":"J. Chem. Inf. Model."},{"key":"mlstabf751bib10","author":"Gormley","year":"2015"},{"key":"mlstabf751bib11","article-title":"X-ray absorption: principles, applications, techniques of EXAFS, SEXAFS, and XANES","author":"Koningsberger","year":"1988"},{"key":"mlstabf751bib12","doi-asserted-by":"publisher","first-page":"241","DOI":"10.1007\/s11120-009-9473-8","article-title":"X-ray absorption spectroscopy","volume":"102","author":"Yano","year":"2009","journal-title":"Photosyn. Res."},{"key":"mlstabf751bib13","doi-asserted-by":"publisher","DOI":"10.1016\/j.radphyschem.2019.108479","article-title":"An open access, integrated XAS data repository at diamond light source","volume":"175","author":"Cibin","year":"2020","journal-title":"Radiat. Phys. Chem."},{"key":"mlstabf751bib14","doi-asserted-by":"publisher","first-page":"869","DOI":"10.1107\/S0909049512036886","article-title":"Towards data format standardization for x-ray absorption spectroscopy","volume":"19","author":"Ravel","year":"2012","journal-title":"J. Synchrotron Radiat."},{"key":"mlstabf751bib15","doi-asserted-by":"publisher","first-page":"717","DOI":"10.1017\/S1431927616000179","article-title":"A complete overhaul of the electron energy-loss spectroscopy and x-ray absorption spectroscopy database: eelsdb.eu","volume":"22","author":"Ewels","year":"2016","journal-title":"Microsc. Microanal."},{"key":"mlstabf751bib16","doi-asserted-by":"publisher","first-page":"967","DOI":"10.1107\/S1600577518006963","article-title":"The challenge of constructing an international XAFS database","volume":"25","author":"Asakura","year":"2018","journal-title":"J. Synchrotron Radiat."},{"key":"mlstabf751bib17","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1038\/sdata.2018.151","article-title":"High-throughput computational x-ray absorption spectroscopy","volume":"5","author":"Mathew","year":"2018","journal-title":"Sci. Data"},{"key":"mlstabf751bib18","first-page":"1","article-title":"Automated generation and ensemble-learned matching of x-ray absorption spectra","volume":"4","author":"Zheng","year":"2018","journal-title":"npj Comput. Mater."},{"key":"mlstabf751bib19","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1038\/s41524-019-0176-1","article-title":"Automated estimation of materials parameter from x-ray absorption and electron energy-loss spectra with similarity measures","volume":"5","author":"Suzuki","year":"2019","journal-title":"npj Computat. Mater."},{"key":"mlstabf751bib20","doi-asserted-by":"publisher","first-page":"10192","DOI":"10.1021\/acscatal.9b03599","article-title":"\u2018Inverting\u2019 x-ray absorption spectra of catalysts by machine learning in search for activity descriptors","volume":"9","author":"Timoshenko","year":"2019","journal-title":"ACS Catal."},{"key":"mlstabf751bib21","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1038\/s41524-017-0056-5","article-title":"Machine learning in materials informatics: recent applications and prospects","volume":"3","author":"Ramprasad","year":"2017","journal-title":"npj Computat. Mater."},{"key":"mlstabf751bib22","doi-asserted-by":"publisher","first-page":"553","DOI":"10.1038\/s41578-020-0215-z","article-title":"Time to kick-start text mining for biomaterials","volume":"5","author":"Hakimi","year":"2020","journal-title":"Nat. Rev. Mater."},{"key":"mlstabf751bib23","doi-asserted-by":"publisher","first-page":"892","DOI":"10.1021\/acscentsci.9b00193","article-title":"A machine learning approach to zeolite synthesis enabled by automatic literature data extraction","volume":"5","author":"Jensen","year":"2019","journal-title":"ACS Central Sci."},{"key":"mlstabf751bib24","doi-asserted-by":"publisher","first-page":"95","DOI":"10.1038\/s41586-019-1335-8","article-title":"Unsupervised word embeddings capture latent knowledge from materials science literature","volume":"571","author":"Tshitoyan","year":"2019","journal-title":"Nature"},{"key":"mlstabf751bib25","doi-asserted-by":"publisher","first-page":"9436","DOI":"10.1021\/acs.chemmater.7b03500","article-title":"Materials synthesis insights from scientific literature via text extraction and machine learning","volume":"29","author":"Kim","year":"2017","journal-title":"Chem. Mater."},{"key":"mlstabf751bib26","first-page":"1","article-title":"Text-mined dataset of inorganic materials synthesis recipes","volume":"6","author":"Kononova","year":"2019","journal-title":"Sci. Data"},{"key":"mlstabf751bib27","first-page":"19","author":"Takeshima","year":"2012"},{"key":"mlstabf751bib28","doi-asserted-by":"publisher","DOI":"10.1371\/journal.pone.0115671","article-title":"Figure-associated text summarization and evaluation","volume":"10","author":"Ramesh","year":"2015","journal-title":"PLoS One"},{"key":"mlstabf751bib29","article-title":"Figure descriptive text extraction using ontological representation","author":"Park","year":"2020"},{"key":"mlstabf751bib30","doi-asserted-by":"publisher","first-page":"1345","DOI":"10.1109\/TKDE.2009.191","article-title":"A survey on transfer learning","volume":"22","author":"Pan","year":"2009","journal-title":"IEEE Trans. Knowl. Data Eng."},{"key":"mlstabf751bib31","doi-asserted-by":"crossref","DOI":"10.18653\/v1\/P19-1337","article-title":"Scalable syntax-aware language models using knowledge distillation","author":"Kuncoro","year":"2019"},{"key":"mlstabf751bib32","doi-asserted-by":"crossref","DOI":"10.18653\/v1\/N19-1112","article-title":"Linguistic knowledge and transferability of contextual representations","author":"Liu","year":"2019"},{"key":"mlstabf751bib33","first-page":"3079","author":"Dai","year":"2015"},{"key":"mlstabf751bib34","article-title":"Can you tell me how to get past sesame street? Sentence-level pretraining beyond language modeling","author":"Wang","year":"2018"},{"key":"mlstabf751bib35","first-page":"359","article-title":"Language modeling teaches you more than translation does: lessons learned through auxiliary syntactic task analysis","author":"Zhang","year":"2018"},{"key":"mlstabf751bib36","article-title":"Bert: pre-training of deep bidirectional transformers for language understanding","author":"Devlin","year":"2018"},{"key":"mlstabf751bib37","first-page":"5998","author":"Vaswani","year":"2017"},{"key":"mlstabf751bib38","first-page":"8968","article-title":"ERNIE 2.0: a continual pre-training framework for language understanding","author":"Sun","year":"2020"},{"key":"mlstabf751bib39","first-page":"5753","author":"Yang","year":"2019"},{"key":"mlstabf751bib40","article-title":"Roberta: a robustly optimized bert pretraining approach","author":"Liu","year":"2019"},{"key":"mlstabf751bib41","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1145\/3357334","article-title":"Textual entailment\u2013based figure summarization for biomedical articles","volume":"16","author":"Saini","year":"2020","journal-title":"ACM Trans. Multimedia Comput. Commun. Appl."},{"key":"mlstabf751bib42","first-page":"1","article-title":"Scientific literature mining for experiment information in materials design","author":"Park","year":"2019"},{"key":"mlstabf751bib43","doi-asserted-by":"crossref","DOI":"10.18653\/v1\/D19-1371","article-title":"SciBERT: a pretrained language model for scientific text","author":"Beltagy","year":"2019"},{"key":"mlstabf751bib44","doi-asserted-by":"crossref","DOI":"10.18653\/v1\/D19-1410","article-title":"Sentence-bert: sentence embeddings using siamese bert-networks","author":"Reimers","year":"2019"},{"key":"mlstabf751bib45","first-page":"632","article-title":"A large annotated corpus for learning natural language inference","author":"Bowman","year":"2015"},{"key":"mlstabf751bib46","first-page":"1112","article-title":"A broad-coverage challenge corpus for sentence understanding through inference","volume":"vol 1","author":"Williams","year":"2018"},{"key":"mlstabf751bib47","first-page":"1","article-title":"SemEval-2017 task 1: semantic textual similarity multilingual and crosslingual focused evaluation","author":"Cer","year":"2017"},{"key":"mlstabf751bib48","doi-asserted-by":"publisher","first-page":"33","DOI":"10.2138\/rmg.2014.78.2","article-title":"Fundamentals of XAFS","volume":"78","author":"Newville","year":"2014","journal-title":"Rev. Mineral. Geochem."},{"key":"mlstabf751bib49","doi-asserted-by":"publisher","first-page":"2756","DOI":"10.3390\/ma12172756","article-title":"Plasma treating mixed metal oxides to improve oxidative performance via defect generation","volume":"12","author":"Horlyck","year":"2019","journal-title":"Materials"},{"key":"mlstabf751bib50","article-title":"Figure associated text summarization and evaluation","author":"Ramesh","year":"2013"}],"container-title":["Machine Learning: Science and Technology"],"original-title":[],"link":[{"URL":"https:\/\/iopscience.iop.org\/article\/10.1088\/2632-2153\/abf751","content-type":"text\/html","content-version":"am","intended-application":"text-mining"},{"URL":"https:\/\/iopscience.iop.org\/article\/10.1088\/2632-2153\/abf751\/pdf","content-type":"application\/pdf","content-version":"am","intended-application":"text-mining"},{"URL":"https:\/\/iopscience.iop.org\/article\/10.1088\/2632-2153\/abf751","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/iopscience.iop.org\/article\/10.1088\/2632-2153\/abf751\/pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/iopscience.iop.org\/article\/10.1088\/2632-2153\/abf751\/pdf","content-type":"application\/pdf","content-version":"am","intended-application":"syndication"},{"URL":"https:\/\/iopscience.iop.org\/article\/10.1088\/2632-2153\/abf751\/pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"syndication"},{"URL":"https:\/\/iopscience.iop.org\/article\/10.1088\/2632-2153\/abf751\/pdf","content-type":"application\/pdf","content-version":"am","intended-application":"similarity-checking"},{"URL":"https:\/\/iopscience.iop.org\/article\/10.1088\/2632-2153\/abf751\/pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2021,12,13]],"date-time":"2021-12-13T16:53:24Z","timestamp":1639414404000},"score":1,"resource":{"primary":{"URL":"https:\/\/iopscience.iop.org\/article\/10.1088\/2632-2153\/abf751"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2021,7,15]]},"references-count":50,"journal-issue":{"issue":"4","published-online":{"date-parts":[[2021,7,15]]},"published-print":{"date-parts":[[2021,12,1]]}},"URL":"https:\/\/doi.org\/10.1088\/2632-2153\/abf751","relation":{},"ISSN":["2632-2153"],"issn-type":[{"value":"2632-2153","type":"electronic"}],"subject":[],"published":{"date-parts":[[2021,7,15]]},"assertion":[{"value":"Advances in scientific literature mining for interpreting materials characterization","name":"article_title","label":"Article Title"},{"value":"Machine Learning: Science and Technology","name":"journal_title","label":"Journal Title"},{"value":"paper","name":"article_type","label":"Article Type"},{"value":"\u00a9 2021 The Author(s). Published by IOP Publishing Ltd","name":"copyright_information","label":"Copyright Information"},{"value":"2020-12-31","name":"date_received","label":"Date Received","group":{"name":"publication_dates","label":"Publication dates"}},{"value":"2021-04-13","name":"date_accepted","label":"Date Accepted","group":{"name":"publication_dates","label":"Publication dates"}},{"value":"2021-07-15","name":"date_epub","label":"Online publication date","group":{"name":"publication_dates","label":"Publication dates"}}]}}