{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,9]],"date-time":"2026-06-09T14:51:39Z","timestamp":1781016699784,"version":"3.54.1"},"reference-count":45,"publisher":"Springer Science and Business Media LLC","issue":"3","license":[{"start":{"date-parts":[[2021,1,15]],"date-time":"2021-01-15T00:00:00Z","timestamp":1610668800000},"content-version":"tdm","delay-in-days":0,"URL":"http:\/\/www.springer.com\/tdm"},{"start":{"date-parts":[[2021,1,15]],"date-time":"2021-01-15T00:00:00Z","timestamp":1610668800000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/www.springer.com\/tdm"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Knowl Inf Syst"],"published-print":{"date-parts":[[2021,3]]},"DOI":"10.1007\/s10115-020-01532-6","type":"journal-article","created":{"date-parts":[[2021,1,15]],"date-time":"2021-01-15T20:06:09Z","timestamp":1610741169000},"page":"695-715","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":38,"title":["Auto-labelling entities in low-resource text: a geological case study"],"prefix":"10.1007","volume":"63","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-4958-6762","authenticated-orcid":false,"given":"Majigsuren","family":"Enkhsaikhan","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Wei","family":"Liu","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Eun-Jung","family":"Holden","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Paul","family":"Duuring","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2021,1,15]]},"reference":[{"key":"1532_CR1","unstructured":"Akbik A, Blythe D, Vollgraf R ( 2018) Contextual string embeddings for sequence labeling. In: Proceedings of the 27th international conference on computational linguistics. pp.\u00a01638\u20131649"},{"key":"1532_CR2","volume-title":"Natural language processing with Python: analyzing text with the natural language toolkit","author":"S Bird","year":"2009","unstructured":"Bird S, Klein E, Loper E (2009) Natural language processing with Python: analyzing text with the natural language toolkit. O\u2019Reilly Media Inc., Sebastopol"},{"key":"1532_CR3","doi-asserted-by":"crossref","unstructured":"Blum A, Mitchell T (1998) Combining labeled and unlabeled data with co-training. In: Proceedings of the eleventh annual conference on Computational learning theory\u2019. ACM 92\u2013100","DOI":"10.1145\/279943.279962"},{"key":"1532_CR4","unstructured":"Chiticariu L, Li Y, Reiss F (2013) Rule-based information extraction is dead! long live rule-based information extraction systems. In: Proceedings of the 2013 conference on empirical methods in natural language processing, pp.\u00a0827\u2013832"},{"key":"1532_CR5","doi-asserted-by":"publisher","first-page":"357","DOI":"10.1162\/tacl_a_00104","volume":"4","author":"JP Chiu","year":"2016","unstructured":"Chiu JP, Nichols E (2016) Named entity recognition with bidirectional lstm-cnns. Trans Assoc Comput Linguist 4:357\u2013370","journal-title":"Trans Assoc Comput Linguist"},{"key":"1532_CR6","unstructured":"Devlin J, Chang M.-W, Lee K, Toutanova K (2018) Bert: Pre-training of deep bidirectional transformers for language understanding. arXiv preprint arXiv:1810.04805"},{"key":"1532_CR7","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1016\/j.jbi.2013.12.006","volume":"47","author":"RI Do\u011fan","year":"2014","unstructured":"Do\u011fan RI, Leaman R, Lu Z (2014) Ncbi disease corpus: a resource for disease name recognition and concept normalization. J Biomed Inf 47:1\u201310","journal-title":"J Biomed Inf"},{"key":"1532_CR8","doi-asserted-by":"crossref","unstructured":"Enkhsaikhan M, Liu W, Holden E.-J, Duuring P (2018) Towards geological knowledge discovery using vector-based semantic similarity. In: International conference on advanced data mining and applications. Springer, pp.\u00a0224\u2013237","DOI":"10.1007\/978-3-030-05090-0_20"},{"key":"1532_CR9","doi-asserted-by":"crossref","unstructured":"Feng X, Feng X, Qin B, Feng Z, Liu T (2018) Improving low resource named entity recognition using cross-lingual knowledge transfer. In: Proceedings of the 27th international joint conference on artificial intelligence. AAAI Press, pp.\u00a04071\u20134077","DOI":"10.24963\/ijcai.2018\/566"},{"key":"1532_CR10","doi-asserted-by":"crossref","unstructured":"Finkel JR, Grenager T, Manning C (2005) Incorporating non-local information into information extraction systems by Gibbs sampling. In: Proceedings of the 43rd annual meeting on association for computational linguistics. Association for Computational Linguistics 363\u2013370","DOI":"10.3115\/1219840.1219885"},{"key":"1532_CR11","unstructured":"Fries J, Wu S, Ratner A, R\u00e9 C (2017) Swellshark: a generative model for biomedical named entity recognition without labeled data. arXiv preprint arXiv:1704.06360"},{"key":"1532_CR12","doi-asserted-by":"crossref","unstructured":"Gardner M, Grus J, Neumann M, Tafjord O, Dasigi P, Liu NF, Peters M, Schmitz M, Zettlemoyer LS (2017) Allennlp: a deep semantic natural language processing platform","DOI":"10.18653\/v1\/W18-2501"},{"issue":"10","key":"1532_CR13","doi-asserted-by":"publisher","first-page":"2451","DOI":"10.1162\/089976600300015015","volume":"12","author":"FA Gers","year":"2000","unstructured":"Gers FA, Schmidhuber JA, Cummins FA (2000) Learning to forget: continual prediction with lstm. Neural Comput. 12(10):2451\u20132471. https:\/\/doi.org\/10.1162\/089976600300015015","journal-title":"Neural Comput."},{"key":"1532_CR14","doi-asserted-by":"crossref","unstructured":"Graves A, Mohamed A-R, Hinton G (2013) Speech recognition with deep recurrent neural networks. In: 2013 IEEE international conference on acoustics, speech and signal processing\u2019. IEEE 6645\u20136649","DOI":"10.1109\/ICASSP.2013.6638947"},{"key":"1532_CR15","unstructured":"Guillaume L, Miguel B, Sandeep S, Kazuya K, Chris D (2016) Neural architectures for named entity recognition. In: Proceedings of NAACL-HLT"},{"key":"1532_CR16","unstructured":"Honnibal M ( 2017) \u2018Spacy\u2019. https:\/\/explosion.ai\/blog\/introducing-spacy"},{"key":"1532_CR17","unstructured":"Huang Z, Xu W , Yu K ( 2015) Bidirectional lstm-crf models for sequence tagging. arXiv preprint arXiv:1508.01991"},{"key":"1532_CR18","unstructured":"Kuru O, Can OA , Yuret D (2016) Charner: character-level named entity recognition. In: Proceedings of COLING 2016, the 26th international conference on computational linguistics: Technical Papers\u2019, pp.\u00a0911\u2013921"},{"key":"1532_CR19","unstructured":"Lafferty J, McCallum A , Pereira FC ( 2001) Conditional random fields: probabilistic models for segmenting and labeling sequence data"},{"key":"1532_CR20","unstructured":"Li J, Sun A, Han J , Li C ( 2018) A survey on deep learning for named entity recognition. arXiv preprint arXiv:1812.09449"},{"key":"1532_CR21","doi-asserted-by":"crossref","unstructured":"Li J, Sun Y, Johnson RJ, Sciaky D, Wei C-H, Leaman R, Davis AP, Mattingly CJ, Wiegers TC, Lu Z (2016) Biocreative v cdr task corpus: a resource for chemical disease relation extraction. Database","DOI":"10.1093\/database\/baw068"},{"key":"1532_CR22","doi-asserted-by":"crossref","unstructured":"Ma X, Hovy E ( 2016) End-to-end sequence labeling via bi-directional lstm-cnns-crf. In: Proceedings of the 54th annual meeting of the association for computational linguistics (Volume 1: Long Papers), Vol.\u00a01, pp.\u00a01064\u20131074","DOI":"10.18653\/v1\/P16-1101"},{"issue":"1","key":"1532_CR23","doi-asserted-by":"publisher","first-page":"3","DOI":"10.1075\/li.30.1.03nad","volume":"30","author":"D Nadeau","year":"2007","unstructured":"Nadeau D, Sekine S (2007) A survey of named entity recognition and classification. Lingvisticae Investigationes 30(1):3\u201326","journal-title":"Lingvisticae Investigationes"},{"key":"1532_CR24","doi-asserted-by":"crossref","unstructured":"Peters ME, Neumann M, Iyyer M, Gardner M, Clark C, Lee K , Zettlemoyer L ( 2018) Deep contextualized word representations. arXiv preprint arXiv:1802.05365","DOI":"10.18653\/v1\/N18-1202"},{"key":"1532_CR25","doi-asserted-by":"crossref","unstructured":"Qu L, Ferraro G, Zhou L, Hou W, Baldwin T ( 2016) Named entity recognition for novel types by transfer learning. In: Proceedings of the 2016 conference on empirical methods in natural language processing. pp.\u00a0899\u2013905","DOI":"10.18653\/v1\/D16-1087"},{"key":"1532_CR26","unstructured":"Ramshaw LA, Marcus MP (1995) Text chunking using transformation-based learning. CoRR arxiv: cmp-lg\/9505040"},{"key":"1532_CR27","unstructured":"Ramshaw LA, Marcus MP (1999) Text chunking using transformation-based learning. In: Natural language processing using very large corpora. Springer, pp. 157\u2013176"},{"key":"1532_CR28","unstructured":"Sang EFTK , De\u00a0Meulder F ( 2003) Introduction to the conll-2003 shared task:language-independent named entity recognition, CoNLL-2003"},{"issue":"17\u201318","key":"1532_CR29","doi-asserted-by":"publisher","first-page":"816","DOI":"10.1016\/j.drudis.2008.06.001","volume":"13","author":"I Segura-Bedmar","year":"2008","unstructured":"Segura-Bedmar I, Mart\u00ednez P, Segura-Bedmar M (2008) Drug name recognition and classification in biomedical texts: a case study outlining approaches underpinning automated systems. Drug Discov Today 13(17\u201318):816\u2013823","journal-title":"Drug Discov Today"},{"key":"1532_CR30","doi-asserted-by":"crossref","unstructured":"Shang J, Liu L, Gu X, Ren X, Ren T , Han J (2018) Learning named entity tagger using domain-specific dictionary. In: Proceedings of the 2018 conference on empirical methods in natural language processing. pp.\u00a02054\u20132064","DOI":"10.18653\/v1\/D18-1230"},{"key":"1532_CR31","doi-asserted-by":"publisher","first-page":"52286","DOI":"10.1109\/ACCESS.2018.2870203","volume":"6","author":"L Shi","year":"2018","unstructured":"Shi L, Jianping C, Jie X (2018) Prospecting information extraction by text mining based on convolutional neural networks-a case study of the lala copper deposit, china. IEEE Access 6:52286\u201352297","journal-title":"IEEE Access"},{"key":"1532_CR32","first-page":"8887","volume":"975","author":"N Sobhana","year":"2010","unstructured":"Sobhana N, Mitra P, Ghosh S (2010) Conditional random field based named entity recognition in geological text. Int J Comput Appl 975:8887","journal-title":"Int J Comput Appl"},{"key":"1532_CR33","doi-asserted-by":"crossref","unstructured":"Stewart M, Liu W, Cardell-Oliver R ( 2019) Redcoat: a collaborative annotation tool for hierarchical entity typing. In: Proceedings of the 2019 Conference on empirical methods in natural language processing and the 9th international joint conference on natural language processing (EMNLP-IJCNLP): system demonstrations. pp.\u00a0193\u2013198","DOI":"10.18653\/v1\/D19-3033"},{"key":"1532_CR34","doi-asserted-by":"crossref","unstructured":"Varma P, R\u00e9 C (2018) Snuba: automating weak supervision to label training data. Proceedings of the VLDB Endowment 12(3):223\u2013236","DOI":"10.14778\/3291264.3291268"},{"key":"1532_CR35","doi-asserted-by":"publisher","first-page":"112","DOI":"10.1016\/j.cageo.2017.12.007","volume":"112","author":"C Wang","year":"2018","unstructured":"Wang C, Ma X, Chen J, Chen J (2018) Information extraction and knowledge graph construction from geoscience literature. Comput Geosci 112:112\u2013120","journal-title":"Comput Geosci"},{"key":"1532_CR36","unstructured":"Wang R, Liu W, McDonald C ( 2016) Featureless domain-specific term extraction with minimal labelled data. In: Proceedings of the Australasian language technology association workshop 2016. pp.\u00a0103\u2013112"},{"key":"1532_CR37","doi-asserted-by":"crossref","unstructured":"Wang X, Zhang Y, Li Q, Ren X, Shang J, Han J ( 2019) Distantly supervised biomedical named entity recognition with dictionary expansion. In: 2019 IEEE International conference on bioinformatics and biomedicine (BIBM), IEEE, pp.\u00a0496\u2013503","DOI":"10.1109\/BIBM47256.2019.8983212"},{"issue":"10","key":"1532_CR38","doi-asserted-by":"publisher","first-page":"1745","DOI":"10.1093\/bioinformatics\/bty869","volume":"35","author":"X Wang","year":"2019","unstructured":"Wang X, Zhang Y, Ren X, Zhang Y, Zitnik M, Shang J, Langlotz C, Han J (2019) Cross-type biomedical named entity recognition with deep multi-task learning. Bioinformatics 35(10):1745\u20131752","journal-title":"Bioinformatics"},{"key":"1532_CR39","volume-title":"Ontonotes release 5.0 ldc2013t19","author":"R Weischedel","year":"2013","unstructured":"Weischedel R, Palmer M, Marcus M, Hovy E, Pradhan S, Ramshaw L, Xue N, Taylor A, Kaufman J, Franchini M et al (2013) Ontonotes release 5.0 ldc2013t19. Linguistic Data Consortium, Philadelphia"},{"key":"1532_CR40","doi-asserted-by":"crossref","unstructured":"Yadav V, Sharp R, Bethard S (2018) Deep affix features improve neural named entity recognizers. In: Proceedings of the seventh joint conference on lexical and computational semantics. pp.\u00a0167\u2013172","DOI":"10.18653\/v1\/S18-2021"},{"key":"1532_CR41","doi-asserted-by":"crossref","unstructured":"Yang LC, Tan IK, Selvaretnam B, Howg EK , Kar LH ( 2019) Text: traffic entity extraction from twitter. In: Proceedings of the 2019 5th international conference on computing and data engineering. pp.\u00a053\u201359","DOI":"10.1145\/3330530.3330547"},{"key":"1532_CR42","unstructured":"Yang Z, Salakhutdinov R, Cohen WW ( 2017) Transfer learning for sequence tagging with hierarchical recurrent networks. arXiv preprint arXiv:1703.06345"},{"key":"1532_CR43","doi-asserted-by":"crossref","unstructured":"Zhang B, Pan X, Wang T, Vaswani A, Ji H, Knight K, Marcu D (2016) Name tagging for low-resource incident languages based on expectation-driven learning. In: Proceedings of the 2016 conference of the North American chapter of the association for computational linguistics: human language technologies\u2019, pp.\u00a0249\u2013259","DOI":"10.18653\/v1\/N16-1029"},{"key":"1532_CR44","doi-asserted-by":"crossref","unstructured":"Zhang C, Govindaraju V, Borchardt J, Foltz T, R\u00e9 C, Peters S (2013) Geodeepdive: statistical inference using familiar data-processing languages. In: Proceedings of the 2013 ACM SIGMOD international conference on management of data. ACM, pp.\u00a0993\u2013996","DOI":"10.1145\/2463676.2463680"},{"key":"1532_CR45","doi-asserted-by":"crossref","unstructured":"Zhu Y, Zhou W, Xu Y, Liu J, Tan Y (2017) (2017) Intelligent learning for knowledge graph towards geological data. Scientific Programming","DOI":"10.1155\/2017\/5072427"}],"container-title":["Knowledge and Information Systems"],"original-title":[],"language":"en","link":[{"URL":"http:\/\/link.springer.com\/content\/pdf\/10.1007\/s10115-020-01532-6.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"http:\/\/link.springer.com\/article\/10.1007\/s10115-020-01532-6\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"http:\/\/link.springer.com\/content\/pdf\/10.1007\/s10115-020-01532-6.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2021,3,8]],"date-time":"2021-03-08T10:16:15Z","timestamp":1615198575000},"score":1,"resource":{"primary":{"URL":"http:\/\/link.springer.com\/10.1007\/s10115-020-01532-6"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2021,1,15]]},"references-count":45,"journal-issue":{"issue":"3","published-print":{"date-parts":[[2021,3]]}},"alternative-id":["1532"],"URL":"https:\/\/doi.org\/10.1007\/s10115-020-01532-6","relation":{},"ISSN":["0219-1377","0219-3116"],"issn-type":[{"value":"0219-1377","type":"print"},{"value":"0219-3116","type":"electronic"}],"subject":[],"published":{"date-parts":[[2021,1,15]]},"assertion":[{"value":"10 September 2019","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"11 November 2020","order":2,"name":"revised","label":"Revised","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"15 November 2020","order":3,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"15 January 2021","order":4,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}}]}}