{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,10,16]],"date-time":"2025-10-16T06:59:34Z","timestamp":1760597974962,"version":"3.37.3"},"reference-count":39,"publisher":"Springer Science and Business Media LLC","issue":"8","license":[{"start":{"date-parts":[[2020,8,8]],"date-time":"2020-08-08T00:00:00Z","timestamp":1596844800000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springer.com\/tdm"},{"start":{"date-parts":[[2020,8,8]],"date-time":"2020-08-08T00:00:00Z","timestamp":1596844800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springer.com\/tdm"}],"funder":[{"DOI":"10.13039\/501100004829","name":"Department of Science and Technology of Sichuan Province","doi-asserted-by":"publisher","award":["No. 2017GZDZX0002"],"award-info":[{"award-number":["No. 2017GZDZX0002"]}],"id":[{"id":"10.13039\/501100004829","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Neural Comput & Applic"],"published-print":{"date-parts":[[2021,4]]},"DOI":"10.1007\/s00521-020-05198-7","type":"journal-article","created":{"date-parts":[[2020,8,8]],"date-time":"2020-08-08T08:03:30Z","timestamp":1596873810000},"page":"3757-3768","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":7,"title":["Learning Chinese word representation better by cascade morphological n-gram"],"prefix":"10.1007","volume":"33","author":[{"given":"Zongyang","family":"Xiong","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-6174-3877","authenticated-orcid":false,"given":"Ke","family":"Qin","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Haobo","family":"Yang","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Guangchun","family":"Luo","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2020,8,8]]},"reference":[{"issue":"6088","key":"5198_CR1","doi-asserted-by":"publisher","first-page":"533","DOI":"10.1038\/323533a0","volume":"323","author":"DE Rumelhart","year":"1986","unstructured":"Rumelhart DE, Hinton GE, Williams RJ (1986) Learning representations by back-propagating errors. Nature 323(6088):533","journal-title":"Nature"},{"key":"5198_CR2","doi-asserted-by":"crossref","unstructured":"Kim Y (2014) Convolutional neural networks for sentence classification. arXiv preprint arXiv:14085882","DOI":"10.3115\/v1\/D14-1181"},{"issue":"1","key":"5198_CR3","doi-asserted-by":"publisher","first-page":"513","DOI":"10.1007\/s00521-016-2351-3","volume":"28","author":"Z Li","year":"2017","unstructured":"Li Z, Lu W, Sun Z, Xing W (2017) A parallel feature selection method study for text classification. Neural Comput Appl 28(1):513\u2013524","journal-title":"Neural Comput Appl"},{"issue":"8","key":"5198_CR4","doi-asserted-by":"publisher","first-page":"2315","DOI":"10.1007\/s00521-015-2069-7","volume":"27","author":"R Shams","year":"2016","unstructured":"Shams R, Mercer RE (2016) Supervised classification of spam emails with natural language stylometry. Neural Comput Appl 27(8):2315\u20132331","journal-title":"Neural Comput Appl"},{"key":"5198_CR5","doi-asserted-by":"crossref","unstructured":"Liu X, Gao J, He X, Deng L, Duh K, Wang YY (2015) Representation learning using multi-task deep neural networks for semantic classification and information retrieval. In: The 2015 conference of the North American chapter of the association for computational linguistics\u2014human language technologies","DOI":"10.3115\/v1\/N15-1092"},{"key":"5198_CR6","doi-asserted-by":"crossref","unstructured":"Zhou G, He T, Zhao J, Hu P (2015) Learning continuous word embedding with metadata for question retrieval in community question answering. In: Proceedings of the 53rd annual meeting of the association for computational linguistics and the 7th international joint conference on natural language processing (Volume 1: Long Papers), vol\u00a01, pp 250\u2013259","DOI":"10.3115\/v1\/P15-1025"},{"key":"5198_CR7","unstructured":"Sutskever I, Vinyals O, Le QV (2014) Sequence to sequence learning with neural networks. In: Advances in neural information processing systems, pp 3104\u20133112"},{"issue":"2","key":"5198_CR8","doi-asserted-by":"publisher","first-page":"616","DOI":"10.1109\/TII.2016.2601521","volume":"13","author":"H Zhang","year":"2016","unstructured":"Zhang H, Li J, Ji Y, Yue H (2016) Understanding subtitles by character-level sequence-to-sequence learning. IEEE Trans Ind Inf 13(2):616\u2013624","journal-title":"IEEE Trans Ind Inf"},{"key":"5198_CR9","unstructured":"Huang EH, Socher R, Manning CD, Ng AY (2012) Improving word representations via global context and multiple word prototypes. In: Proceedings of the 50th annual meeting of the association for computational linguistics: long papers-volume 1, association for computational linguistics, pp 873\u2013882"},{"key":"5198_CR10","doi-asserted-by":"crossref","unstructured":"Levy O, Goldberg Y (2014) Dependency-based word embeddings. In: Proceedings of the 52nd annual meeting of the association for computational linguistics (short papers), vol\u00a02, pp 302\u2013308","DOI":"10.3115\/v1\/P14-2050"},{"key":"5198_CR11","doi-asserted-by":"crossref","unstructured":"Yang Y, Eisenstein J (2015) Unsupervised multi-domain adaptation with feature embeddings. In: Proceedings of the 2015 conference of the North American chapter of the association for computational linguistics: human language technologies, pp 672\u2013682","DOI":"10.3115\/v1\/N15-1069"},{"key":"5198_CR12","unstructured":"Mikolov T, Sutskever I, Chen K, Corrado GS, Dean J (2013) Distributed representations of words and phrases and their compositionality. In: Advances in neural information processing systems, pp 3111\u20133119"},{"key":"5198_CR13","unstructured":"Mikolov T, Yih Wt, Zweig G (2013) Linguistic regularities in continuous space word representations. In: Proceedings of the 2013 Conference of the North American Chapter of the association for computational linguistics: human language technologies, pp 746\u2013751"},{"key":"5198_CR14","doi-asserted-by":"crossref","unstructured":"Pennington J, Socher R, Manning C (2014) Glove: global vectors for word representation. In: Proceedings of the 2014 conference on empirical methods in natural language processing (EMNLP), pp 1532\u20131543","DOI":"10.3115\/v1\/D14-1162"},{"key":"5198_CR15","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:180205365","DOI":"10.18653\/v1\/N18-1202"},{"key":"5198_CR16","unstructured":"Radford A, Narasimhan K, Salimans T, Sutskever I (2018) Improving language understanding by generative pre-training. https:\/\/s3-us-west-2.amazonaws.com\/openai-assets\/research-covers\/language-unsupervised\/language_understanding_paper.pdf"},{"key":"5198_CR17","unstructured":"Devlin J, Chang MW, Lee K, Toutanova K (2018) Bert: Pre-training of deep bidirectional transformers for language understanding. arXiv preprint arXiv:181004805"},{"key":"5198_CR18","unstructured":"Luong T, Socher R, Manning C (2013) Better word representations with recursive neural networks for morphology. In: Proceedings of the seventeenth conference on computational natural language learning, pp 104\u2013113"},{"key":"5198_CR19","unstructured":"Qiu S, Cui Q, Bian J, Gao B, Liu TY (2014) Co-learning of word representations and morpheme representations. In: Proceedings of COLING 2014, the 25th international conference on computational linguistics: technical papers, pp 141\u2013150"},{"key":"5198_CR20","doi-asserted-by":"crossref","unstructured":"Sun F, Guo J, Lan Y, Xu J, Cheng X (2016) Inside out: two jointly predictive models for word representations and phrase representations. In: AAAI, pp 2821\u20132827","DOI":"10.1609\/aaai.v30i1.10338"},{"key":"5198_CR21","doi-asserted-by":"crossref","unstructured":"Wieting J, Bansal M, Gimpel K, Livescu K (2016) Charagram: embedding words and sentences via character n-grams. arXiv preprint arXiv:160702789","DOI":"10.18653\/v1\/D16-1157"},{"key":"5198_CR22","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"},{"key":"5198_CR23","doi-asserted-by":"crossref","unstructured":"Cao S, Lu W (2017) Improving word embeddings with convolutional feature learning and subword information. In: AAAI, pp 3144\u20133151","DOI":"10.1609\/aaai.v31i1.10993"},{"key":"5198_CR24","doi-asserted-by":"crossref","unstructured":"Sun Y, Lin L, Yang N, Ji Z, Wang X (2014) Radical-enhanced Chinese character embedding. In: International conference on neural information processing, Springer, Berlin, pp 279\u2013286","DOI":"10.1007\/978-3-319-12640-1_34"},{"key":"5198_CR25","doi-asserted-by":"crossref","unstructured":"Li Y, Li W, Sun F, Li S (2015) Component-enhanced Chinese character embeddings. arXiv preprint arXiv:150806669","DOI":"10.18653\/v1\/D15-1098"},{"key":"5198_CR26","unstructured":"Chen X, Xu L, Liu Z, Sun M, Luan HB (2015) Joint learning of character and word embeddings. In: IJCAI, pp 1236\u20131242"},{"key":"5198_CR27","doi-asserted-by":"crossref","unstructured":"Xu J, Liu J, Zhang L, Li Z, Chen H (2016) Improve Chinese word embeddings by exploiting internal structure. In: Proceedings of the 2016 conference of the North American chapter of the association for computational linguistics: human language technologies, pp 1041\u20131050","DOI":"10.18653\/v1\/N16-1119"},{"key":"5198_CR28","doi-asserted-by":"crossref","unstructured":"Yin R, Wang Q, Li P, Li R, Wang B (2016) Multi-granularity Chinese word embedding. In: Proceedings of the 2016 conference on empirical methods in natural language processing, pp 981\u2013986","DOI":"10.18653\/v1\/D16-1100"},{"key":"5198_CR29","doi-asserted-by":"crossref","unstructured":"Yu J, Jian X, Xin H, Song Y (2017) Joint embeddings of Chinese words, characters, and fine-grained subcharacter components. In: Proceedings of the 2017 conference on empirical methods in natural language processing, pp 286\u2013291","DOI":"10.18653\/v1\/D17-1027"},{"key":"5198_CR30","doi-asserted-by":"crossref","unstructured":"Cao S, Lu W, Zhou J, Li X (2018) cw2vec: Learning Chinese word embeddings with stroke n-gram information. In: Thirty-second AAAI conference on artificial intelligence","DOI":"10.1609\/aaai.v32i1.12029"},{"key":"5198_CR31","doi-asserted-by":"crossref","unstructured":"Zhang Z, Zhao H, Ling K, Li J, Li Z, He S, Fu G (2019) Effective subword segmentation for text comprehension. In: IEEE\/ACM transactions on audio, speech and language processing (TASLP), vol 27(11), pp 1664\u20131674","DOI":"10.1109\/TASLP.2019.2922537"},{"key":"5198_CR32","doi-asserted-by":"crossref","unstructured":"Chen Z, Hu K (2018) Radical enhanced Chinese word embedding. In: Chinese computational linguistics and natural language processing based on naturally annotated big data. Springer, Berlin, pp 3\u201311","DOI":"10.1007\/978-3-030-01716-3_1"},{"key":"5198_CR33","doi-asserted-by":"crossref","unstructured":"Su Tr, Lee Hy (2017) Learning Chinese word representations from glyphs of characters. In: Proceedings of the 2017 conference on empirical methods in natural language processing, pp 264\u2013273","DOI":"10.18653\/v1\/D17-1025"},{"key":"5198_CR34","unstructured":"Wu W, Meng Y, Han Q, Li M, Li X, Mei J, Nie P, Sun X, Li J (2019) Glyce: glyph-vectors for Chinese character representations. arXiv preprint arXiv:190110125"},{"key":"5198_CR35","first-page":"307","volume":"13","author":"MU Gutmann","year":"2012","unstructured":"Gutmann MU, Hyv\u00e4rinen A (2012) Noise-contrastive estimation of unnormalized statistical models, with applications to natural image statistics. J Mach Learn Res 13:307\u2013361","journal-title":"J Mach Learn Res"},{"key":"5198_CR36","unstructured":"Sun Y, Wang S, Li Y, Feng S, Chen X, Zhang H, Tian X, Zhu D, Tian H, Wu H (2019) Ernie: enhanced representation through knowledge integration. arXiv preprint arXiv:190409223"},{"key":"5198_CR37","unstructured":"Jin P, Wu Y (2012) Semeval-2012 task 4: evaluating Chinese word similarity. In: Proceedings of the first joint conference on lexical and computational semantics-volume 1: proceedings of the main conference and the shared task, and Volume 2: proceedings of the sixth international workshop on semantic evaluation, association for computational linguistics, pp 374\u2013377"},{"issue":"1","key":"5198_CR38","doi-asserted-by":"publisher","first-page":"72","DOI":"10.2307\/1412159","volume":"15","author":"C Spearman","year":"1904","unstructured":"Spearman C (1904) The proof and measurement of association between two things. Am J Psychol 15(1):72\u2013101","journal-title":"Am J Psychol"},{"key":"5198_CR39","first-page":"1871","volume":"9","author":"RE Fan","year":"2008","unstructured":"Fan RE, Chang KW, Hsieh CJ, Wang XR, Lin CJ (2008) LIBLINEAR: a library for large linear classification. J Mach Learn Res 9:1871\u20131874","journal-title":"J Mach Learn Res"}],"container-title":["Neural Computing and Applications"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s00521-020-05198-7.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s00521-020-05198-7\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s00521-020-05198-7.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2022,11,6]],"date-time":"2022-11-06T07:50:05Z","timestamp":1667721005000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s00521-020-05198-7"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2020,8,8]]},"references-count":39,"journal-issue":{"issue":"8","published-print":{"date-parts":[[2021,4]]}},"alternative-id":["5198"],"URL":"https:\/\/doi.org\/10.1007\/s00521-020-05198-7","relation":{},"ISSN":["0941-0643","1433-3058"],"issn-type":[{"type":"print","value":"0941-0643"},{"type":"electronic","value":"1433-3058"}],"subject":[],"published":{"date-parts":[[2020,8,8]]},"assertion":[{"value":"7 December 2019","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"11 July 2020","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"8 August 2020","order":3,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Compliance with ethical standards"}},{"value":"The authors declare that they have no conflict of interest.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Conflict of interest"}}]}}