{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,25]],"date-time":"2026-06-25T12:07:04Z","timestamp":1782389224530,"version":"3.54.5"},"reference-count":14,"publisher":"SAGE Publications","issue":"1","license":[{"start":{"date-parts":[[2019,10,24]],"date-time":"2019-10-24T00:00:00Z","timestamp":1571875200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/journals.sagepub.com\/page\/policies\/text-and-data-mining-license"}],"content-domain":{"domain":["journals.sagepub.com"],"crossmark-restriction":true},"short-container-title":["Journal of Intelligent &amp; Fuzzy Systems"],"published-print":{"date-parts":[[2020,1,9]]},"abstract":"<jats:p>\u00a0Language Model is used to describe and calculate the probability of a reasonable sentence occurrence in natural language. In practical applications, language model as the core of natural language processing is often used in machine translation, information indexing, voice recognition, context processing such as sentiment recognition and other tasks. We will discuss advantages and weaknesses of traditional statistical language models and neural Network Language Models such as CBOW and Skip-gram. Keeping in view the traditional statistical language model and neural network model, we will try to put forward the word vector model based on part of speech and sentiment information (PSWV-model) in order to use more natural language information such as word order features, part of speech features, and sentiment polarity information under the framework of Mikolov\u2019s model. And finally we will present our deliberations on some advantages of PSWV model and other models including CBOW and Skip-Gram, CDNV in the NLP tasks including named entities recognition and sentiment polarity analysis.<\/jats:p>","DOI":"10.3233\/jifs-179417","type":"journal-article","created":{"date-parts":[[2019,10,25]],"date-time":"2019-10-25T09:12:40Z","timestamp":1571994760000},"page":"427-440","update-policy":"https:\/\/doi.org\/10.1177\/sage-journals-update-policy","source":"Crossref","is-referenced-by-count":4,"title":["The deep learning word vector model using part of speech and sentiment information"],"prefix":"10.1177","volume":"38","author":[{"given":"Dongping","family":"Wei","sequence":"first","affiliation":[{"name":"School of Mathematics and Statistics, Yunnan University, Kunming, Yunnan, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Niansheng","family":"Tang","sequence":"additional","affiliation":[{"name":"School of Mathematics and Statistics, Yunnan University, Kunming, Yunnan, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Tianli","family":"lei","sequence":"additional","affiliation":[{"name":"Department of Math and Physics, Shenzhen Polytechnic, Shenzhen, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Shouwen","family":"Wen","sequence":"additional","affiliation":[{"name":"Management College, Shenzhen Polytechnic, Shenzhen, China"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"179","published-online":{"date-parts":[[2019,10,24]]},"reference":[{"key":"e_1_3_2_2_2","doi-asserted-by":"publisher","DOI":"10.3390\/a9020041"},{"key":"e_1_3_2_3_2","doi-asserted-by":"publisher","DOI":"10.1109\/TKDE.2015.2489653"},{"key":"e_1_3_2_4_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.procs.2014.05.296"},{"key":"e_1_3_2_5_2","doi-asserted-by":"publisher","DOI":"10.1126\/science.1127647"},{"issue":"2","key":"e_1_3_2_6_2","first-page":"1137","article-title":"A neural probabilistic language","volume":"3","author":"Bengio Y.","year":"2003","unstructured":"BengioY., DucharmeR., VincentP., et al., A neural probabilistic language, Journal of Machine Learning Research 3(2) (2003), 1137\u20131155.","journal-title":"Journal of Machine Learning Research"},{"key":"e_1_3_2_7_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.neunet.2014.08.005"},{"key":"e_1_3_2_8_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.jbi.2017.12.006"},{"issue":"171","key":"e_1_3_2_9_2","first-page":"1108","article-title":"A novel word embedding learning model using the dissociation between nouns and verbs","volume":"2016","author":"Baotian H.","year":"2016","unstructured":"BaotianH., BuzhouT. and etc, A novel word embedding learning model using the dissociation between nouns and verbs, Neurocomputing 2016(171) (2016), 1108\u20131117.","journal-title":"Neurocomputing"},{"key":"e_1_3_2_10_2","first-page":"142","article-title":"Learning word vectors for sentiment analysis","volume":"2011","author":"Maas A.L.","year":"2011","unstructured":"MaasA.L., DalyR.E., PhamP.T., et al., Learning word vectors for sentiment analysis, Proceedings ComTonal Linguistics of the 49th Annual Meeting of the Association for Human Language Technologies-Volume 1, Association for Computational Linguistics 2011 (2011), 142\u2013150.","journal-title":"Proceedings ComTonal Linguistics of the 49th Annual Meeting of the Association for Human Language Technologies-Volume 1, Association for Computational Linguistics"},{"issue":"5","key":"e_1_3_2_11_2","first-page":"214","article-title":"Emotional Modeling in an E-learning System Based on OCC Theory","volume":"37","author":"XiangJie Q.","year":"2010","unstructured":"XiangJieQ., ZhiliangW. and WansenW., Emotional Modeling in an E-learning System Based on OCC Theory, Computer Science 37(5) (2010), 214\u2013218.","journal-title":"Computer Science"},{"key":"e_1_3_2_12_2","doi-asserted-by":"publisher","DOI":"10.1007\/s12559-015-9319-y"},{"key":"e_1_3_2_13_2","first-page":"2493","article-title":"Natural Language Processing (almost) from Scratch","volume":"12","author":"Collobert R.","year":"2011","unstructured":"CollobertR., WestonJ., BottouL., et al., Natural Language Processing (almost) from Scratch, Journal of Machine Learning Research 12 (2011), 2493\u20132537.","journal-title":"Journal of Machine Learning Research"},{"issue":"1","key":"e_1_3_2_14_2","first-page":"58","article-title":"Learning a product of experts with elitist lasso","volume":"34","author":"Wang M.","year":"2013","unstructured":"WangM. and ManningC.D., Learning a product of experts with elitist lasso, Newdesign.aclweb.org 34(1) (2013), 58\u201365.","journal-title":"Newdesign.aclweb.org"},{"key":"e_1_3_2_15_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.neunet.2005.06.042"}],"container-title":["Journal of Intelligent &amp; 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