{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,10,17]],"date-time":"2025-10-17T14:07:14Z","timestamp":1760710034684,"version":"3.37.3"},"reference-count":30,"publisher":"Springer Science and Business Media LLC","issue":"14","license":[{"start":{"date-parts":[[2019,10,28]],"date-time":"2019-10-28T00:00:00Z","timestamp":1572220800000},"content-version":"tdm","delay-in-days":0,"URL":"http:\/\/www.springer.com\/tdm"},{"start":{"date-parts":[[2019,10,28]],"date-time":"2019-10-28T00:00:00Z","timestamp":1572220800000},"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":["Neural Comput &amp; Applic"],"published-print":{"date-parts":[[2020,7]]},"DOI":"10.1007\/s00521-019-04541-x","type":"journal-article","created":{"date-parts":[[2019,10,28]],"date-time":"2019-10-28T19:11:53Z","timestamp":1572289913000},"page":"10087-10108","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":7,"title":["Generating word and document matrix representations for document classification"],"prefix":"10.1007","volume":"32","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-3723-7688","authenticated-orcid":false,"given":"Shun","family":"Guo","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Nianmin","family":"Yao","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2019,10,28]]},"reference":[{"key":"4541_CR1","doi-asserted-by":"publisher","first-page":"146","DOI":"10.1080\/00437956.1954.11659520","volume":"10","author":"ZS Harris","year":"1981","unstructured":"Harris ZS (1981) Distributional structure. Word 10:146\u2013162","journal-title":"Word"},{"issue":"2","key":"4541_CR2","doi-asserted-by":"publisher","first-page":"137","DOI":"10.1007\/s10462-010-9188-4","volume":"35","author":"J Silva","year":"2011","unstructured":"Silva J, Coheur L, Mendes AC, Wichert A (2011) From symbolic to sub-symbolic information in question classification. Artif Intell Rev 35(2):137\u2013154","journal-title":"Artif Intell Rev"},{"key":"4541_CR3","unstructured":"Mikolov T et al (2013) Efficient estimation of word representations in vector space. In: Computer science"},{"key":"4541_CR4","first-page":"1","volume":"99","author":"H Zhang","year":"2018","unstructured":"Zhang H, Wang S, Xu X et al (2018) Tree2Vector: learning a vectorial representation for tree-structured data. IEEE Trans Neural Netw Learn Syst 99:1\u201315","journal-title":"IEEE Trans Neural Netw Learn Syst"},{"key":"4541_CR5","doi-asserted-by":"publisher","first-page":"1873","DOI":"10.1109\/TKDE.2018.2808953","volume":"30","author":"H Zhang","year":"2018","unstructured":"Zhang H, Wang S, Zhao M et al (2018) Locality reconstruction models for book representation. IEEE Trans Knowl Data Eng 30:1873\u20131886","journal-title":"IEEE Trans Knowl Data Eng"},{"key":"4541_CR6","unstructured":"Le QV, Mikolov T (2014) Distributed representations of sentences and documents. In: Computer science"},{"key":"4541_CR7","unstructured":"Chen M (2017) Efficient vector representation for documents through corruption. In: Proceedings of the fifth international conference on learning representations. ICLR"},{"key":"4541_CR8","unstructured":"Socher R, Perelygin A, Wu JY, Chuang J, Manning CD, Ng AY, Potts C (2013) Recursive deep models for semantic compositionality over a sentiment treebank. In: Proceedings of conference on empirical methods in natural language processing. EMNLP, pp 1642"},{"issue":"17","key":"4541_CR9","first-page":"3043","volume":"32","author":"Mesnil","year":"2015","unstructured":"Mesnil et al (2015) Ensemble of generative and discriminative techniques for sentiment analysis of movie reviews. J Lightwave Technol 32(17):3043\u20133060","journal-title":"J Lightwave Technol"},{"key":"4541_CR10","doi-asserted-by":"publisher","first-page":"1625","DOI":"10.1109\/TSMCC.2012.2227112","volume":"43","author":"H Zhang","year":"2013","unstructured":"Zhang H et al (2013) Multidimensional latent semantic analysis using term spatial information. IEEE Trans Cybern 43:1625\u20131640","journal-title":"IEEE Trans Cybern"},{"key":"4541_CR11","unstructured":"Huang EH, Socher R, Manning CD et al (2012) Improving word representations via global context and multiple word prototypes. In: Proceedings of meeting of the Association for Computational Linguistics: long papers"},{"key":"4541_CR12","doi-asserted-by":"crossref","unstructured":"Kim Y (2014). Convolutional neural networks for sentence classification. In: Proceedings of conference on empirical methods in natural language processing. EMNLP, pp 1746\u20131751","DOI":"10.3115\/v1\/D14-1181"},{"key":"4541_CR13","doi-asserted-by":"crossref","unstructured":"Kim Y, Jernite Y, Sontag D et al (2016) Character-aware neural language models. In: Proceedings of the thirtieth AAAI conference on artificial intelligence. AAAI, pp 2741\u20132749","DOI":"10.1609\/aaai.v30i1.10362"},{"key":"4541_CR14","doi-asserted-by":"crossref","unstructured":"Conneau A, Schwenk H et al (2016) Very deep convolutional networks for text classification. In: Proceedings of the 15th conference of the European chapter of the Association for Computational Linguistics, vol 1, long papers","DOI":"10.18653\/v1\/E17-1104"},{"key":"4541_CR15","doi-asserted-by":"crossref","unstructured":"Shen D, Min MR, Li Y et al (2017) Learning context-sensitive convolutional filters for text processing","DOI":"10.18653\/v1\/D18-1210"},{"issue":"8","key":"4541_CR16","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 Computation 9(8):1735\u20131780","journal-title":"Neural Computation"},{"key":"4541_CR17","unstructured":"Zhou C, Sun C, Liu Z, et al (2015) A C-LSTM neural network for text classification. In: Computer science, pp 39\u201344"},{"key":"4541_CR18","doi-asserted-by":"crossref","unstructured":"Ding Z, Xia R, Yu J, et al (2018) Densely connected bidirectional LSTM with applications to sentence classification. In: Natural language processing and chinese computing, pp 278\u2013287","DOI":"10.1007\/978-3-319-99501-4_24"},{"key":"4541_CR19","unstructured":"Pappas N, Popescu-Belis A (2017) Multilingual hierarchical attention networks for document classification"},{"key":"4541_CR20","doi-asserted-by":"crossref","unstructured":"Kumar A, Kawahara D, Kurohashi S (2018) Knowledge-enriched two-layered attention network for sentiment analysis","DOI":"10.18653\/v1\/N18-2041"},{"key":"4541_CR21","doi-asserted-by":"crossref","unstructured":"Zhang T, Huang M, Zhao L (2018) Learning structured representation for text classification via reinforcement learning. In: Proceedings of the thirty-second AAAI conference on artificial intelligence. AAAI","DOI":"10.1609\/aaai.v32i1.12047"},{"key":"4541_CR22","doi-asserted-by":"crossref","unstructured":"Feng J, Huang M, Zhao L et al (2018) Reinforcement learning for relation classification from noisy data. In: Proceedings of the thirty-second AAAI conference on artificial intelligence. AAAI","DOI":"10.1609\/aaai.v32i1.12063"},{"key":"4541_CR23","unstructured":"Miyato T, Dai A M, Goodfellow I (2017) Adversarial training methods for semi-supervised text classification. In: Proceedings of the fifth international conference on learning representations. ICLR"},{"key":"4541_CR24","doi-asserted-by":"crossref","unstructured":"Liu P, Qiu X, Huang X (2017) Adversarial multi-task learning for text classification","DOI":"10.18653\/v1\/P17-1001"},{"key":"4541_CR25","unstructured":"Maas AL, Daly RE, Pham PT, Huang D, Ng AY, Potts C (2011) Learning word vectors for sentiment analysis. Meeting of the Association for Computational Linguistics. ACL, pp 142\u2013150"},{"key":"4541_CR26","unstructured":"Johnson R, Zhang T (2015) Semi-supervised convolutional neural networks for text categorization via region embedding. In: Advances in neural information processing systems, pp 919\u2013927"},{"key":"4541_CR27","unstructured":"Joachims T (1996) A probabilistic analysis of the Rocchio algorithm with TFIDF for text categorization. In: ICML, pp 143\u2013151"},{"key":"4541_CR28","doi-asserted-by":"crossref","unstructured":"Vincent P, Larochelle H, Bengio Y, Manzagol P-A (2008) Extracting and composing robust features with denoising autoencoders. In: Proceedings of the twenty-fifth international conference. ICML, pp 1096\u20131103","DOI":"10.1145\/1390156.1390294"},{"key":"4541_CR29","doi-asserted-by":"crossref","unstructured":"Mikolov T et al (2010) Recurrent neural network based language model. In: Proceedings of the 37th international symposium on computer architecture. ISCA, pp 1045\u20131048","DOI":"10.21437\/Interspeech.2010-343"},{"key":"4541_CR30","first-page":"1871","volume":"9","author":"RE Fan","year":"2008","unstructured":"Fan RE, Chang KW, Hsieh CJ et al (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":"http:\/\/link.springer.com\/content\/pdf\/10.1007\/s00521-019-04541-x.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"http:\/\/link.springer.com\/article\/10.1007\/s00521-019-04541-x\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"http:\/\/link.springer.com\/content\/pdf\/10.1007\/s00521-019-04541-x.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2022,10,2]],"date-time":"2022-10-02T21:17:27Z","timestamp":1664745447000},"score":1,"resource":{"primary":{"URL":"http:\/\/link.springer.com\/10.1007\/s00521-019-04541-x"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2019,10,28]]},"references-count":30,"journal-issue":{"issue":"14","published-print":{"date-parts":[[2020,7]]}},"alternative-id":["4541"],"URL":"https:\/\/doi.org\/10.1007\/s00521-019-04541-x","relation":{},"ISSN":["0941-0643","1433-3058"],"issn-type":[{"type":"print","value":"0941-0643"},{"type":"electronic","value":"1433-3058"}],"subject":[],"published":{"date-parts":[[2019,10,28]]},"assertion":[{"value":"27 March 2019","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"5 October 2019","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"28 October 2019","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"}},{"value":"This article does not contain any studies with human participants or animals performed by any of the authors.","order":3,"name":"Ethics","group":{"name":"EthicsHeading","label":"Human and animal rights"}}]}}