{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,9,4]],"date-time":"2025-09-04T13:37:13Z","timestamp":1756993033113},"reference-count":68,"publisher":"Springer Science and Business Media LLC","issue":"4","license":[{"start":{"date-parts":[[2021,2,13]],"date-time":"2021-02-13T00:00:00Z","timestamp":1613174400000},"content-version":"tdm","delay-in-days":0,"URL":"http:\/\/www.springer.com\/tdm"},{"start":{"date-parts":[[2021,2,13]],"date-time":"2021-02-13T00:00:00Z","timestamp":1613174400000},"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":[[2021,4]]},"DOI":"10.1007\/s10115-021-01549-5","type":"journal-article","created":{"date-parts":[[2021,2,14]],"date-time":"2021-02-14T08:30:19Z","timestamp":1613291419000},"page":"845-866","update-policy":"http:\/\/dx.doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":8,"title":["Transfer learning for fine-grained entity typing"],"prefix":"10.1007","volume":"63","author":[{"given":"Feng","family":"Hou","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Ruili","family":"Wang","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yi","family":"Zhou","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2021,2,13]]},"reference":[{"key":"1549_CR1","doi-asserted-by":"crossref","unstructured":"Abhishek A, Anand A, Awekar A (2017) Fine-grained entity type classification by jointly learning representations and label embeddings. In: Proceedings of the 15th conference of the European chapter of the association for computational linguistics: volume 1, Long Papers, pp 797\u2013807. Association for Computational Linguistics, Valencia, Spain. https:\/\/www.aclweb.org\/anthology\/E17-1075","DOI":"10.18653\/v1\/E17-1075"},{"key":"1549_CR2","unstructured":"Bahdanau D, Cho K, Bengio Y (2014) Neural machine translation by jointly learning to align and translate. arXiv preprint arXiv:1409.0473"},{"key":"1549_CR3","doi-asserted-by":"crossref","unstructured":"Baheti A, Ritter A, Li J, Dolan B (2018) Generating more interesting responses in neural conversation models with distributional constraints. In: Proceedings of the 2018 conference on empirical methods in natural language processing. Association for Computational Linguistics, Brussels, Belgium, pp 3970\u20133980. https:\/\/www.aclweb.org\/anthology\/D18-1431","DOI":"10.18653\/v1\/D18-1431"},{"issue":"3","key":"1549_CR4","doi-asserted-by":"publisher","first-page":"1693","DOI":"10.1007\/s10115-019-01337-2","volume":"60","author":"D Banerjee","year":"2019","unstructured":"Banerjee D, Islam K, Xue K, Mei G, Xiao L, Zhang G, Xu R, Lei C, Ji S, Li J (2019) A deep transfer learning approach for improved post-traumatic stress disorder diagnosis. Knowl Inf Syst 60(3):1693\u20131724","journal-title":"Knowl Inf Syst"},{"key":"1549_CR5","first-page":"993","volume":"3","author":"DM Blei","year":"2003","unstructured":"Blei DM, Ng AY, Jordan MI (2003) Latent Dirichlet allocation. J Mach Learn Res 3:993\u20131022","journal-title":"J Mach Learn Res"},{"key":"1549_CR6","doi-asserted-by":"publisher","first-page":"135","DOI":"10.1162\/tacl_a_00051","volume":"5","author":"P Bojanowski","year":"2017","unstructured":"Bojanowski P, Grave E, Joulin A, Mikolov T (2017) Enriching word vectors with subword information. Trans Assoc Comput Linguist 5:135\u2013146","journal-title":"Trans Assoc Comput Linguist"},{"issue":"4","key":"1549_CR7","first-page":"467","volume":"18","author":"PF Brown","year":"1992","unstructured":"Brown PF, Della Pietra VJ, Desouza PV, Lai JC, Mercer RL (1992) Class-based n-gram models of natural language. Comput Linguist 18(4):467\u2013480","journal-title":"Comput Linguist"},{"issue":"1","key":"1549_CR8","doi-asserted-by":"publisher","first-page":"31","DOI":"10.1007\/s10618-010-0175-9","volume":"22","author":"N Silla Carlos","year":"2011","unstructured":"Silla Carlos N, Freitas Alex A (2011) A survey of hierarchical classification across different application domains. Data Min Knowl Disc 22(1):31\u201372","journal-title":"Data Min Knowl Disc"},{"key":"1549_CR9","unstructured":"Clark K, Luong MT, Le QV, Manning CD (2020) ELECTRA: pre-training text encoders as discriminators rather than generators. In: Proceddings of ICLR, pp 1\u201317. Retrieved March 19, 2020, from https:\/\/openreview.net\/pdf?id=r1xMH1BtvB"},{"key":"1549_CR10","doi-asserted-by":"crossref","unstructured":"Collobert R, Weston J (2008) A unified architecture for natural language processing: Deep neural networks with multitask learning. In: Proceedings of the 25th international conference on Machine learning, pp 160\u2013167","DOI":"10.1145\/1390156.1390177"},{"key":"1549_CR11","unstructured":"Daniel G, Nevena L, Kuzman G, Jesse K, David H (2014) Context-dependent fine-grained entity type tagging. arXiv preprint arXiv:1412.1820"},{"key":"1549_CR12","doi-asserted-by":"publisher","first-page":"101","DOI":"10.1613\/jair.1872","volume":"26","author":"H Daume III","year":"2006","unstructured":"Daume H III, Marcu D (2006) Domain adaptation for statistical classifiers. J Artif Intell Res 26:101\u2013126","journal-title":"J Artif Intell Res"},{"issue":"4","key":"1549_CR13","doi-asserted-by":"publisher","first-page":"704","DOI":"10.1109\/TASLP.2019.2892232","volume":"27","author":"D Deng","year":"2019","unstructured":"Deng D, Jing L, Yu J, Sun S, Ng MK (2019) Sentiment lexicon construction with hierarchical supervision topic model. IEEE\/ACM Trans Audio Speech Language Process 27(4):704\u2013718. https:\/\/doi.org\/10.1109\/TASLP.2019.2892232","journal-title":"IEEE\/ACM Trans Audio Speech Language Process"},{"key":"1549_CR14","unstructured":"Dong L, Wei F, Sun H, Zhou M, Xu K (2015) A hybrid neural model for type classification of entity mentions. In: Proceedings of the twenty-fourth international joint conference on artificial intelligence (IJCAI 2015), pp 1243\u20131249"},{"key":"1549_CR15","first-page":"2121","volume":"12","author":"J Duchi","year":"2011","unstructured":"Duchi J, Hazan E, Singer Y (2011) Adaptive subgradient methods for online learning and stochastic optimization. J Mach Learn Res 12:2121\u20132159","journal-title":"J Mach Learn Res"},{"key":"1549_CR16","unstructured":"Ekbal A, Sourjikova E, Frank A, Ponzetto SP (2010) Assessing the challenge of fine-grained named entity recognition and classification. In: Proceedings of the 2010 named entities workshop, pp 93\u2013101"},{"key":"1549_CR17","unstructured":"Eunsol C, Omer L, Yejin C, Luke Z (2018) Ultra-fine entity typing. In: Proceedings of the 56th annual meeting of the association for computational linguistics, pp 87\u201396"},{"key":"1549_CR18","doi-asserted-by":"crossref","unstructured":"Fleischman M, Hovy E (2002) Fine grained classification of named entities. In: COLING 2002: The 19th international conference on computational linguistics, pp 1\u20137. https:\/\/www.aclweb.org\/anthology\/C02-1130","DOI":"10.3115\/1072228.1072358"},{"key":"1549_CR19","unstructured":"Ghaddar A, Langlais P (2018) Transforming Wikipedia into a large-scale fine-grained entity type corpus. In: Proceedings of the eleventh international conference on language resources and evaluation (LREC 2018), pp. 4413\u20134420. European language resources association (ELRA), Miyazaki, Japan. Retrieved April 02, 2019, from https:\/\/www.aclweb.org\/anthology\/L18-1699"},{"key":"1549_CR20","volume-title":"Deep learning","author":"I Goodfellow","year":"2016","unstructured":"Goodfellow I, Bengio Y, Courville A (2016) Deep learning. MIT Press, Cambridge"},{"key":"1549_CR21","unstructured":"Griffiths TL, Steyvers M, Blei DM, Tenenbaum JB (2005) Integrating topics and syntax. In: Advances in neural information processing systems, pp 537\u2013544"},{"key":"1549_CR22","unstructured":"Hailong J, Lei H, Juanzi L, Tiansi D (2018) Attributed and predictive entity embedding for fine-grained entity typing in knowledge bases. In: Proceedings of the 27th international conference on computational linguistics, pp 282\u2013292"},{"key":"1549_CR23","unstructured":"Hinton GE, Srivastava N, Krizhevsky A, Sutskever I, Salakhutdinov RR (2012) Improving neural networks by preventing co-adaptation of feature detectors. arXiv preprint arXiv:1207.0580"},{"issue":"8","key":"1549_CR24","doi-asserted-by":"publisher","first-page":"1735","DOI":"10.1162\/neco.1997.9.8.1735","volume":"9","author":"S Hochreiter","year":"1997","unstructured":"Hochreiter S, Schmidhuber J (1997) Long short-term memory. Neural Comput 9(8):1735\u20131780","journal-title":"Neural Comput"},{"key":"1549_CR25","unstructured":"Jacob D, Ming-Wei C, Kenton L, Kristina T (2018) BERT: pre-training of deep bidirectional transformers for language understanding. arXiv preprint arXiv:1810.04805"},{"key":"1549_CR26","unstructured":"Jeffrey P, Richard S, Christopher DM (2014) GloVe: global vectors for word representation. In: Empirical methods in natural language processing (EMNLP), pp 1532\u20131543"},{"key":"1549_CR27","doi-asserted-by":"publisher","unstructured":"Jin M, Luo X, Zhu H, Zhuo HH (2018) Combining deep learning and topic modeling for review understanding in context-aware recommendation. In: Proceedings of the 2018 conference of the North American chapter of the association for computational linguistics: human language technologies, volume 1 (Long Papers), pp. 1605\u20131614. Association for Computational Linguistics, New Orleans, Louisiana. https:\/\/doi.org\/10.18653\/v1\/N18-1145","DOI":"10.18653\/v1\/N18-1145"},{"key":"1549_CR28","doi-asserted-by":"publisher","first-page":"64","DOI":"10.1162\/tacl_a_00300","volume":"8","author":"M Joshi","year":"2020","unstructured":"Joshi M, Chen D, Liu Y, Weld DS, Zettlemoyer L, Levy O (2020) Spanbert: improving pre-training by representing and predicting spans. Trans Assoc Comput Linguist 8:64\u201377","journal-title":"Trans Assoc Comput Linguist"},{"issue":"1","key":"1549_CR29","doi-asserted-by":"publisher","first-page":"337","DOI":"10.1007\/s10115-019-01395-6","volume":"62","author":"G Keren","year":"2020","unstructured":"Keren G, Sabato S, Schuller B (2020) Analysis of loss functions for fast single-class classification. Knowl Inf Syst 62(1):337\u2013358","journal-title":"Knowl Inf Syst"},{"issue":"2","key":"1549_CR30","doi-asserted-by":"publisher","first-page":"965","DOI":"10.1007\/s10115-018-1316-5","volume":"61","author":"M Liu","year":"2019","unstructured":"Liu M, He M, Wang R, Li S (2019) A new local density and relative distance based spectrum clustering. Knowl Inf Syst 61(2):965\u2013985","journal-title":"Knowl Inf Syst"},{"key":"1549_CR31","doi-asserted-by":"crossref","unstructured":"Ma D, Chen Y, Chang KCC, Du X, Xu C, Chang Y (2018) Leveraging fine-grained Wikipedia categories for entity search. In: Proceedings of the 2018 world wide web conference, pp 1623\u20131632","DOI":"10.1145\/3178876.3186074"},{"key":"1549_CR32","doi-asserted-by":"crossref","unstructured":"Mendes PN, Jakob M, Garc\u00eda-Silva A, Bizer C (2011) DBpedia spotlight: shedding light on the web of documents. In: Proceedings of the 7th international conference on semantic systems, pp. 1\u20138. ACM","DOI":"10.1145\/2063518.2063519"},{"key":"1549_CR33","unstructured":"Minaee S, Kalchbrenner N, Cambria E, Nikzad N, Chenaghlu M, Gao J (2020) Deep learning based text classification: a comprehensive review. arXiv preprint arXiv:2004.03705"},{"key":"1549_CR34","first-page":"1361","volume":"2012","author":"Yosef Mohamed Amir","year":"2012","unstructured":"Amir Yosef Mohamed, Sandro Bauer, Johannes Hoffart, Marc Spaniol, Gerhard Weikum (2012) HYENA: hierarchical type classification for entity names. Proc COLING 2012:1361\u20131370","journal-title":"Proc COLING"},{"key":"1549_CR35","doi-asserted-by":"publisher","unstructured":"Neelakantan A, Chang MW (2015) Inferring missing entity type instances for knowledge base completion: New dataset and methods. In: Proceedings of the 2015 conference of the north american chapter of the association for computational linguistics: human language technologies, pp 515\u2013525. Association for Computational Linguistics, Denver, Colorado. https:\/\/doi.org\/10.3115\/v1\/N15-1054","DOI":"10.3115\/v1\/N15-1054"},{"key":"1549_CR36","unstructured":"Nitish G, Sameer S, Dan R (2017) Entity linking via joint encoding of types, descriptions, and context. In: Proceedings of the conference on empirical methods in natural language processing, pp 2671\u20132680"},{"key":"1549_CR37","unstructured":"Peng X, Denilson B (2018) Neural fine-grained entity type classification with hierarchy-aware loss. In: Proceedings of NAACL-HLT, pp 16\u201325"},{"key":"1549_CR38","doi-asserted-by":"publisher","unstructured":"Peters M, Ammar W, Bhagavatula C, Power R (2017) Semi-supervised sequence tagging with bidirectional language models. In: Proceedings of the 55th annual meeting of the association for computational linguistics (volume 1: long papers), pp 1756\u20131765. Association for Computational Linguistics, Vancouver, Canada. https:\/\/doi.org\/10.18653\/v1\/P17-1161","DOI":"10.18653\/v1\/P17-1161"},{"key":"1549_CR39","doi-asserted-by":"publisher","unstructured":"Rabinovich M, Klein D (2017) Fine-grained entity typing with high-multiplicity assignments. In: Proceedings of the 55th annual meeting of the association for computational linguistics (volume 2: short papers), pp 330\u2013334. Association for Computational Linguistics, Vancouver, Canada. Retrieved April 02, 2019, from https:\/\/doi.org\/10.18653\/v1\/P17-2052","DOI":"10.18653\/v1\/P17-2052"},{"key":"1549_CR40","unstructured":"Radford A, Narasimhan K, Salimans T, Sutskever I (2018) Improving language understanding by generative pre-training pp 1\u201312. Retrieved April 01, 2019, from https:\/\/s3-us-west-2.amazonaws.com\/openai-assets\/research-covers\/languageunsupervised\/language understanding paper.pdf"},{"key":"1549_CR41","unstructured":"Radford W, Curran JR (2013) Joint apposition extraction with syntactic and semantic constraints. In: Proceedings of the 51st annual meeting of the association for computational linguistics (volume 2: short papers), pp 671\u2013677. Association for Computational Linguistics, Sofia, Bulgaria. Retrieved April 02, 2019, from https:\/\/www.aclweb.org\/anthology\/P13-2118"},{"key":"1549_CR42","unstructured":"Rahman A, Ng V (2010) Inducing fine-grained semantic classes via hierarchical and collective classification. In: Proceedings of the 23rd international conference on computational linguistics (Coling 2010), pp 931\u2013939"},{"key":"1549_CR43","unstructured":"Ralph W, Martha P, Mitchell M, Eduard H, Sameer P, Lance R, Nianwen X, Ann T, Jeff K, Michelle F (2013) Ontonotes release 5.0 with OntoNotes DB tool v0.999 beta. In: Linguistic data consortium, pp 1\u201353. Retrieved April 02, 2019, from https:\/\/hdl.handle.net\/11272.1\/AB2\/MKJJ2R"},{"key":"1549_CR44","unstructured":"Recasens M, de\u00a0Marneffe MC, Potts C (2013) The life and death of discourse entities: Identifying singleton mentions. In: Proceedings of the 2013 conference of the North American chapter of the association for computational linguistics: human language technologies, pp 627\u2013633. Association for Computational Linguistics, Atlanta, Georgia. Retrieved April 03, 2019, from https:\/\/www.aclweb.org\/anthology\/N13-1071"},{"key":"1549_CR45","doi-asserted-by":"crossref","unstructured":"Ren X, He W, Qu M, Huang L, Ji H, Han J (2016) Afet: automatic fine-grained entity typing by hierarchical partial-label embedding. In: Proceedings of the 2016 conference on empirical methods in natural language processing, pp 1369\u20131378","DOI":"10.18653\/v1\/D16-1144"},{"key":"1549_CR46","doi-asserted-by":"crossref","unstructured":"Ren X, He W, Qu M, Voss CR, Ji H, Han J (2016) Label noise reduction in entity typing by heterogeneous partial-label embedding. In: Proceedings of the 22nd ACM SIGKDD international conference on Knowledge discovery and data mining, pp 1825\u20131834","DOI":"10.1145\/2939672.2939822"},{"key":"1549_CR47","unstructured":"Sang EF, De\u00a0Meulder F (2003) Introduction to the conll-2003 shared task: Language-independent named entity recognition. arXiv preprint arXiv:cs\/0306050"},{"key":"1549_CR48","unstructured":"Sanjeev K, Ulli W, Hinrich S (2017) End-to-end trainable attentive decoder for hierarchical entity classification. In: Proceedings of European chapter of association for computational linguistics, pp 752\u2013758"},{"key":"1549_CR49","doi-asserted-by":"crossref","unstructured":"Shimaoka S, Stenetorp P, Inui K, Riedel S (2017) Neural architectures for fine-grained entity type classification. In: Proceedings of the 15th Conference of the European chapter of the association for computational linguistics: volume 1, long papers, pp 1271\u20131280. Association for Computational Linguistics, Valencia, Spain. Retrieved April 03, 2019, from https:\/\/www.aclweb.org\/anthology\/E17-1119","DOI":"10.18653\/v1\/E17-1119"},{"issue":"1","key":"1549_CR50","first-page":"1929","volume":"15","author":"N Srivastava","year":"2014","unstructured":"Srivastava N, Hinton G, Krizhevsky A, Sutskever I, Salakhutdinov R (2014) Dropout: a simple way to prevent neural networks from overfitting. J Mach Learn Res 15(1):1929\u20131958","journal-title":"J Mach Learn Res"},{"key":"1549_CR51","doi-asserted-by":"crossref","unstructured":"Suzuki M, Matsuda K, Sekine S, Okazaki N, Inui K (2016) Fine-grained named entity classification with wikipedia article vectors. In: 2016 IEEE\/WIC\/ACM international conference on web intelligence (WI), pp 483\u2013486. IEEE","DOI":"10.1109\/WI.2016.0080"},{"key":"1549_CR52","unstructured":"Tomas M, Greg C, Kai C, Jeffrey D (2013) Efficient estimation of word representations in vector space. In: ICLR workshop, pp 1\u201312"},{"key":"1549_CR53","unstructured":"Tomas M, Ilya S, Kai C, Greg C, Jeffrey D (2013) Distributed representations of words and phrases and their compositionality. In: Advances in neural information processing systems, pp 3111\u20133119"},{"key":"1549_CR54","unstructured":"Vaswani A, Shazeer N, Parmar N, Uszkoreit J, Jones L, Gomez AN, Kaiser \u0141, Polosukhin I (2017) Attention is all you need. In: Advances in neural information processing systems, pp 5998\u20136008"},{"key":"1549_CR55","unstructured":"Wiedemann G, Ruppert E, Jindal R, Biemann C (2018) Transfer learning from lda to bilstm-cnn for offensive language detection in twitter. In: Proceedings of GermEval 2018, 14th conference on natural language processing (KONVENS 2018), pp 85\u201394"},{"key":"1549_CR56","unstructured":"Wu Y, Schuster M, Chen Z, Le QV, Norouzi M, Macherey W, Krikun M, Cao Y, Gao Q, Macherey K, et\u00a0al (2016) Google\u2019s neural machine translation system: Bridging the gap between human and machine translation. arXiv preprint arXiv:1609.08144"},{"key":"1549_CR57","unstructured":"Xiao L, Daniel SW (2012) Fine-grained entity recognition. In: Proceedings of 26th AAAI conference on artificial intelligence, pp 94\u2013100"},{"key":"1549_CR58","doi-asserted-by":"publisher","first-page":"835","DOI":"10.1613\/jair.5601","volume":"61","author":"Yadollah Yaghoobzadeh","year":"2018","unstructured":"Yaghoobzadeh Yadollah, Adel Heike, Schutze Hinrich (2018) Corpus-level fine-grained entity typing. J Artif Intell Res 61:835\u2013862","journal-title":"J Artif Intell Res"},{"key":"1549_CR59","doi-asserted-by":"crossref","unstructured":"Yaghoobzadeh Y, Adel H, Sch\u00fctze H (2017) Noise mitigation for neural entity typing and relation extraction. In: Proceedings of the 15th conference of the European chapter of the association for computational linguistics: volume 1, long papers, pp 1183\u20131194. Association for Computational Linguistics, Valencia, Spain. Retrieved April 03, 2019, from https:\/\/www.aclweb.org\/anthology\/E17-1111","DOI":"10.18653\/v1\/E17-1111"},{"key":"1549_CR60","unstructured":"Yang Z, Dai Z, Yang Y, Carbonell J, Salakhutdinov R, Le QV (2019) Xlnet: Generalized autoregressive pretraining for language understanding. arXiv preprint arXiv:1906.08237"},{"key":"1549_CR61","unstructured":"Yang Z, Salakhutdinov R, Cohen WW (2017) Transfer learning for sequence tagging with hierarchical recurrent networks. In: Proceedings of ICLR, pp 1\u201310"},{"key":"1549_CR62","doi-asserted-by":"publisher","unstructured":"Yogatama D, Gillick D, Lazic N (2015) Embedding methods for fine grained entity type classification. In: Proceedings of the 53rd annual meeting of the association for computational linguistics and the 7th international joint conference on natural language processing (volume 2: short papers), pp 291\u2013296. Association for Computational Linguistics, Beijing, China. https:\/\/doi.org\/10.3115\/v1\/P15-2048","DOI":"10.3115\/v1\/P15-2048"},{"key":"1549_CR63","unstructured":"Yukun M, Erik C, Sa G (2016) Label embedding for zero-shot fine-grained named entity typing. In: Proceedings of the 26th international conference on computational linguistics: technical papers, pp 171\u2013180"},{"issue":"1","key":"1549_CR64","doi-asserted-by":"publisher","first-page":"137","DOI":"10.1007\/s10115-018-1280-0","volume":"61","author":"D Zha","year":"2019","unstructured":"Zha D, Li C (2019) Multi-label dataless text classification with topic modeling. Knowl Inf Syst 61(1):137\u2013160","journal-title":"Knowl Inf Syst"},{"key":"1549_CR65","doi-asserted-by":"publisher","unstructured":"Zhang Z, Han X, Liu Z, Jiang X, Sun M, Liu Q (2019) ERNIE: enhanced language representation with informative entities. In: Proceedings of the 57th annual meeting of the association for computational linguistics, pp 1441\u20131451. Association for Computational Linguistics, Florence, Italy. https:\/\/doi.org\/10.18653\/v1\/P19-1139","DOI":"10.18653\/v1\/P19-1139"},{"issue":"11","key":"1549_CR66","doi-asserted-by":"publisher","first-page":"1664","DOI":"10.1109\/TASLP.2019.2922537","volume":"27","author":"Z Zhang","year":"2019","unstructured":"Zhang Z, Zhao H, Ling K, Li J, Li Z, He S, Fu G (2019) Effective subword segmentation for text comprehension. IEEE\/ACM Trans Audio Speech Language Process 27(11):1664\u20131674","journal-title":"IEEE\/ACM Trans Audio Speech Language Process"},{"key":"1549_CR67","doi-asserted-by":"publisher","unstructured":"Zhao W, Peng H, Eger S, Cambria E, Yang M (2019) Towards scalable and reliable capsule networks for challenging NLP applications. In: Proceedings of the 57th annual meeting of the association for computational linguistics, pp 1549\u20131559. Association for Computational Linguistics, Florence, Italy. https:\/\/doi.org\/10.18653\/v1\/P19-1150","DOI":"10.18653\/v1\/P19-1150"},{"key":"1549_CR68","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1007\/s12559-019-09677-5","volume":"12","author":"X Zhong","year":"2020","unstructured":"Zhong X, Cambria E, Hussain A (2020) Extracting time expressions and named entities with constituent-based tagging schemes. Cognitive Comput 12:1\u201319","journal-title":"Cognitive Comput"}],"container-title":["Knowledge and Information Systems"],"original-title":[],"language":"en","link":[{"URL":"http:\/\/link.springer.com\/content\/pdf\/10.1007\/s10115-021-01549-5.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"http:\/\/link.springer.com\/article\/10.1007\/s10115-021-01549-5\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"http:\/\/link.springer.com\/content\/pdf\/10.1007\/s10115-021-01549-5.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2021,3,28]],"date-time":"2021-03-28T15:05:42Z","timestamp":1616943942000},"score":1,"resource":{"primary":{"URL":"http:\/\/link.springer.com\/10.1007\/s10115-021-01549-5"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2021,2,13]]},"references-count":68,"journal-issue":{"issue":"4","published-print":{"date-parts":[[2021,4]]}},"alternative-id":["1549"],"URL":"https:\/\/doi.org\/10.1007\/s10115-021-01549-5","relation":{},"ISSN":["0219-1377","0219-3116"],"issn-type":[{"value":"0219-1377","type":"print"},{"value":"0219-3116","type":"electronic"}],"subject":[],"published":{"date-parts":[[2021,2,13]]},"assertion":[{"value":"25 July 2020","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"12 January 2021","order":2,"name":"revised","label":"Revised","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"16 January 2021","order":3,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"13 February 2021","order":4,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}}]}}