{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,14]],"date-time":"2026-03-14T18:07:04Z","timestamp":1773511624093,"version":"3.50.1"},"publisher-location":"New York, NY, USA","reference-count":29,"publisher":"ACM","license":[{"start":{"date-parts":[[2024,12,13]],"date-time":"2024-12-13T00:00:00Z","timestamp":1734048000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.acm.org\/publications\/policies\/copyright_policy#Background"}],"content-domain":{"domain":["dl.acm.org"],"crossmark-restriction":true},"short-container-title":[],"published-print":{"date-parts":[[2024,12,13]]},"DOI":"10.1145\/3711542.3711593","type":"proceedings-article","created":{"date-parts":[[2025,4,14]],"date-time":"2025-04-14T04:46:56Z","timestamp":1744606016000},"page":"286-291","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":1,"title":["Named Entity Recognition from Materials Science Patent Documents Using Clue Word Tags"],"prefix":"10.1145","author":[{"ORCID":"https:\/\/orcid.org\/0009-0001-8950-7822","authenticated-orcid":false,"given":"Toshihiko","family":"Sakai","sequence":"first","affiliation":[{"name":"Department of Advanced Information Technology, Graduate School of Information Science and Electrical Engineering, Kyushu University, Fukuoka, Japan"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-8838-0156","authenticated-orcid":false,"given":"Nobuhiko","family":"Chiwata","sequence":"additional","affiliation":[{"name":"Global Research &amp; Innovative Technology Center, Proterial, Ltd., Kumagaya, Japan"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-7462-8074","authenticated-orcid":false,"given":"Tsunenori","family":"Mine","sequence":"additional","affiliation":[{"name":"Department of Advanced Information Technology, Faculty of Information Science and Electrical Engineering, Kyushu University, Fukuoka, Japan"}]}],"member":"320","published-online":{"date-parts":[[2025,4,13]]},"reference":[{"key":"e_1_3_3_2_2_2","doi-asserted-by":"crossref","unstructured":"Chen C Zuo Y Ye W Li X Deng Z and Ong SP. 2020. A Critical Review of Machine Learning of Energy Materials. Advanced Energy Materials 10 8 (2020) 1903242.","DOI":"10.1002\/aenm.201903242"},{"key":"e_1_3_3_2_3_2","doi-asserted-by":"crossref","unstructured":"Draxl C and Scheffler M. 2018. NOMAD: The FAIR concept for big data-driven materials science. MRS Bulletin 43 9 (2018) 676\u2013682.","DOI":"10.1557\/mrs.2018.208"},{"key":"e_1_3_3_2_4_2","doi-asserted-by":"crossref","unstructured":"Court CJ and Cole JM. 2020. Magnetic and superconducting phase diagrams and transition temperatures predicted using text mining and machine learning. npj Computational Materials 6 1 (2020) 18.","DOI":"10.1038\/s41524-020-0287-8"},{"key":"e_1_3_3_2_5_2","doi-asserted-by":"crossref","first-page":"72","DOI":"10.18653\/v1\/W19-1909","volume-title":"Proceedings of the 2nd Clinical Natural Language Processing Workshop","author":"E Alsentzer","year":"2019","unstructured":"Alsentzer E, Murphy J, Boag W, Weng W, Jindi D, Naumann T, and McDermott M. 2019. Publicly Available Clinical BERT Embeddings. In Proceedings of the 2nd Clinical Natural Language Processing Workshop. Association for Computational Linguistics, 72\u201378."},{"key":"e_1_3_3_2_6_2","doi-asserted-by":"crossref","unstructured":"Olivetti E\u00a0A Cole J\u00a0M Kim E Kononova O Ceder G Han T\u00a0Y-J and Hiszpanski A\u00a0M. 2020. Data-driven materials research enabled by natural language processing and information extraction. Applied Physics Reviews 7 4 (2020) 41317.","DOI":"10.1063\/5.0021106"},{"key":"e_1_3_3_2_7_2","first-page":"173","volume-title":"Proceedings of the Ninth Conference on European Chapter of the Association for Computational Linguistics","author":"E\u00a0F Tjong Kim\u00a0Sang","year":"1999","unstructured":"Tjong Kim\u00a0Sang E\u00a0F and Veenstra J. 1999. Representing Text Chunks. In Proceedings of the Ninth Conference on European Chapter of the Association for Computational Linguistics. 173\u2013179."},{"key":"e_1_3_3_2_8_2","first-page":"319","volume-title":"Proceedings of the Joint European Conference on Machine Learning and Knowledge Discovery in Databases","author":"F Kuniyoshi","year":"2021","unstructured":"Kuniyoshi F, Ozawa J, and Miwa M. 2021. Analyzing research trends in inorganic materials literature using nlp. In Proceedings of the Joint European Conference on Machine Learning and Knowledge Discovery in Databases. 319\u2013334."},{"key":"e_1_3_3_2_9_2","unstructured":"Souza F Nogueira R\u00a0F and Lotufo R\u00a0d A. 2019. Portuguese Named Entity Recognition using BERT-CRF. arXiv preprint arXiv:https:\/\/arXiv.org\/abs\/1909.10649 (2019). arXiv:https:\/\/arXiv.org\/abs\/1909.10649"},{"key":"e_1_3_3_2_10_2","unstructured":"Center for Research and Development Strategy. [n. d.]. Process Science platform for Innovation in Materials Creation Technologies\u3000- Process Informatics -. https:\/\/www.jst.go.jp\/crds\/report\/CRDS-FY2021-SP-01.html"},{"key":"e_1_3_3_2_11_2","doi-asserted-by":"publisher","unstructured":"Pooja H and Prabhudev P. 2024. Integrated Deep Learning with Attention Layer Based Approach for Precise Biomedical Named Entity Recognition. Journal of Advances in Information Technology 15 (01 2024) 704\u2013713. 10.12720\/jait.15.6.704-713","DOI":"10.12720\/jait.15.6.704-713"},{"key":"e_1_3_3_2_12_2","unstructured":"Sakaji H Nonaka H Sakai H and Masuyama S. 2010. Cross-bootstrapping: an automatic extraction method of solution-effect expressions from patent documents Vol.\u00a093. The IEICE transactions on information and systems 742\u2013755."},{"key":"e_1_3_3_2_13_2","doi-asserted-by":"crossref","first-page":"546","DOI":"10.18653\/v1\/S17-2091","volume-title":"Proceedings of the 11th International Workshop on Semantic Evaluation (SemEval-2017)","author":"I Augenstein","year":"2017","unstructured":"Augenstein I, Das M, Riedel S, Vikraman L, and McCallum A. 2017. SemEVal 2017 Task 10: ScienceIE - Extracting Keyphrases and Relations from Scientific Publications. In Proceedings of the 11th International Workshop on Semantic Evaluation (SemEval-2017). 546\u2013555."},{"key":"e_1_3_3_2_14_2","doi-asserted-by":"crossref","unstructured":"Li J Sun A Han J and Li C. 2022. A Survey on Deep Learning for Named Entity Recognition. IEEE Transactions on Knowledge and Data Engineering 34 1 (2022) 50\u201370.","DOI":"10.1109\/TKDE.2020.2981314"},{"key":"e_1_3_3_2_15_2","doi-asserted-by":"crossref","unstructured":"Wei J Chu X Sun X-Y Xu K Deng H-X Chen J Wei Z and Lei M. 2019. Machine learning in materials science. InfoMat 1 3 (2019) 338\u2013358.","DOI":"10.1002\/inf2.12028"},{"key":"e_1_3_3_2_16_2","series-title":"(ICML \u201901)","first-page":"282","volume-title":"Proceedings of the Eighteenth International Conference on Machine Learning","author":"J\u00a0D Lafferty","year":"2001","unstructured":"Lafferty J\u00a0D, McCallum A, and Pereira F\u00a0C\u00a0N. 2001. Conditional Random Fields: Probabilistic Models for Segmenting and Labeling Sequence Data. In Proceedings of the Eighteenth International Conference on Machine Learning(ICML \u201901). 282\u2013289."},{"key":"e_1_3_3_2_17_2","doi-asserted-by":"crossref","first-page":"147","DOI":"10.3115\/1596374.1596399","volume-title":"Proceedings of the Thirteenth Conference on Computational Natural Language Learning (CoNLL-2009)","author":"L Ratinov","year":"2009","unstructured":"Ratinov L and Roth D. 2009. Design Challenges and Misconceptions in Named Entity Recognition. In Proceedings of the Thirteenth Conference on Computational Natural Language Learning (CoNLL-2009). 147\u2013155."},{"key":"e_1_3_3_2_18_2","doi-asserted-by":"crossref","unstructured":"Weston L Tshitoyan V Dagdelen J Kononova O Trewartha A Persson K\u00a0A Ceder G and Jain A. 2019. Named Entity Recognition and Normalization Applied to Large-Scale Information Extraction from the Materials Science Literature. Journal of Chemical Information and Modeling 59 9 (2019) 3692\u20133702.","DOI":"10.1021\/acs.jcim.9b00470"},{"key":"e_1_3_3_2_19_2","volume-title":"Materials Development and Utilization Utilizing Materials Informatics (2nd ed.)","author":"LTD. Technical Information Institute\u00a0CO.","year":"2019","unstructured":"Technical Information Institute\u00a0CO. LTD.2019. Materials Development and Utilization Utilizing Materials Informatics (2nd ed.). Technical Information Institute CO. LTD.https:\/\/ci.nii.ac.jp\/ncid\/BB29059582"},{"key":"e_1_3_3_2_20_2","unstructured":"Polak M\u00a0P Modi S Latosinska A Zhang J Wang C-W Wang S Hazra A\u00a0D and Morgan D. 2023. Flexible Model-Agnostic Method for Materials Data Extraction from Text Using General Purpose Language Models. arXiv preprint arXiv:https:\/\/arXiv.org\/abs\/2302.04914 (2023)."},{"key":"e_1_3_3_2_21_2","doi-asserted-by":"crossref","unstructured":"Etzioni O Cafarella M Downey D Popescu A-M Shaked T Soderland S Weld D and Yates A. 2005. Unsupervised named-entity extraction from the Web: An experimental study. Artificial Intelligence 165 1 (06 2005) 91\u2013134.","DOI":"10.1016\/j.artint.2005.03.001"},{"key":"e_1_3_3_2_22_2","doi-asserted-by":"crossref","unstructured":"Kononova O He T Huo H Trewartha A Olivetti E\u00a0A and Ceder G. 2021. Opportunities and challenges of text mining in materials research. iScience 24 3 (2021) 102155.","DOI":"10.1016\/j.isci.2021.102155"},{"key":"e_1_3_3_2_23_2","first-page":"113","volume-title":"Proceedings of the 21st International Conference on Computational Linguistics and 44th Annual Meeting of the Association for Computational Linguistics","author":"P Pantel","year":"2006","unstructured":"Pantel P and Pennacchiotti M. 2006. Espresso: Leveraging Generic Patterns for Automatically Harvesting Semantic Relations. In Proceedings of the 21st International Conference on Computational Linguistics and 44th Annual Meeting of the Association for Computational Linguistics. 113\u2013120."},{"key":"e_1_3_3_2_24_2","unstructured":"Kurokawa R Iwasaki T Itobe R and Okada Y. 2022. Materials Development Utilizing Materials Informatics Technology: Chemicals Informatics Contributing to Environmental Management(in Japanese). Hitachi Review 104 2 (03 2022) 249\u2013254."},{"key":"e_1_3_3_2_25_2","first-page":"1818","volume-title":"Proceedings of the Third International Conference on Language Resources and Evaluation (LREC\u201902)","author":"S Sekine","year":"2002","unstructured":"Sekine S, Sudo K, and Nobata C. 2002. Extended Named Entity Hierarchy. In Proceedings of the Third International Conference on Language Resources and Evaluation (LREC\u201902). 1818\u20131824."},{"key":"e_1_3_3_2_26_2","doi-asserted-by":"crossref","first-page":"767","DOI":"10.1109\/iThings-GreenCom-CPSCom-SmartData-Cybermatics50389.2020.00132","volume-title":"2020 International Conferences on Internet of Things (iThings) and IEEE Green Computing and Communications (GreenCom) and IEEE Cyber, Physical and Social Computing (CPSCom) and IEEE Smart Data (SmartData) and IEEE Congress on Cybermatics (Cybermatics)","author":"T Chen","year":"2020","unstructured":"Chen T, Luo M, Fu H, Chen D, Hu Q, and Deng N. 2020. Application of NER and Association Rules to Traditional Chinese Medicine Patent Mining. In 2020 International Conferences on Internet of Things (iThings) and IEEE Green Computing and Communications (GreenCom) and IEEE Cyber, Physical and Social Computing (CPSCom) and IEEE Smart Data (SmartData) and IEEE Congress on Cybermatics (Cybermatics). 767\u2013772."},{"key":"e_1_3_3_2_27_2","doi-asserted-by":"crossref","unstructured":"Tshitoyan V Dagdelen J Weston L Dunn A Rong Z Kononova O Persson K Ceder G and Jain A. 2019. Unsupervised word embeddings capture latent knowledge from materials science literature. Nature 571 (07 2019) 95\u201398.","DOI":"10.1038\/s41586-019-1335-8"},{"key":"e_1_3_3_2_28_2","unstructured":"Hu Y Ameer I Zuo X Peng X Zhou Y Li Z Li Y Li J Jiang X and Xu H. 2023. Zero-shot clinical entity recognition using chatgpt. arXiv preprint arXiv:https:\/\/arXiv.org\/abs\/2303.16416 (2023)."},{"key":"e_1_3_3_2_29_2","unstructured":"Huang Z Xu W and Yu K. 2015. Bidirectional LSTM-CRF Models for Sequence Tagging. arXiv preprint arXiv:https:\/\/arXiv.org\/abs\/1508.01991 (2015). arXiv:https:\/\/arXiv.org\/abs\/1508.01991"},{"key":"e_1_3_3_2_30_2","first-page":"892","volume-title":"ACS Central Science","author":"Z Jensen","year":"2019","unstructured":"Jensen Z, Kim E, Kwon S\u00a0H, Gani T\u00a0Z\u00a0H, Roman-Leshkov Y, Moliner M, Corma A, and Olivetti E. 2019. A machine learning approach to zeolite synthesis enabled by automatic literature data extraction. In ACS Central Science. 892\u2013899."}],"event":{"name":"NLPIR 2024: 2024 8th International Conference on Natural Language Processing and Information Retrieval","location":"Okayama Japan","acronym":"NLPIR 2024"},"container-title":["Proceedings of the 2024 8th International Conference on Natural Language Processing and Information Retrieval"],"original-title":[],"link":[{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3711542.3711593","content-type":"unspecified","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/dl.acm.org\/doi\/pdf\/10.1145\/3711542.3711593","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,6,19]],"date-time":"2025-06-19T01:18:29Z","timestamp":1750295909000},"score":1,"resource":{"primary":{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3711542.3711593"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,12,13]]},"references-count":29,"alternative-id":["10.1145\/3711542.3711593","10.1145\/3711542"],"URL":"https:\/\/doi.org\/10.1145\/3711542.3711593","relation":{},"subject":[],"published":{"date-parts":[[2024,12,13]]},"assertion":[{"value":"2025-04-13","order":3,"name":"published","label":"Published","group":{"name":"publication_history","label":"Publication History"}}]}}