{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,5,14]],"date-time":"2025-05-14T02:42:02Z","timestamp":1747190522189,"version":"3.40.5"},"reference-count":32,"publisher":"Wiley","license":[{"start":{"date-parts":[[2021,5,17]],"date-time":"2021-05-17T00:00:00Z","timestamp":1621209600000},"content-version":"unspecified","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Tsinghua University Humanities and Social Sciences Revitalization Project","award":["2019THZWJC38","20202000291","62073284","61603331","2019YFB1406300"],"award-info":[{"award-number":["2019THZWJC38","20202000291","62073284","61603331","2019YFB1406300"]}]},{"name":"Project of Baidu Netcom Technology Co. Ltd. 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Keyword (as <jats:inline-formula>\n                     <e:math xmlns:e=\"http:\/\/www.w3.org\/1998\/Math\/MathML\" id=\"M4\">\n                        <e:mi>n<\/e:mi>\n                        <e:mo>=<\/e:mo>\n                        <e:mn>1<\/e:mn>\n                     <\/e:math>\n                  <\/jats:inline-formula>, 1-gram is the keyword) extraction models are generally carried out from two aspects, the feature extraction and the model design. By summarizing the advantages and disadvantages of existing models, we propose a novel key <jats:inline-formula>\n                     <g:math xmlns:g=\"http:\/\/www.w3.org\/1998\/Math\/MathML\" id=\"M5\">\n                        <g:mi>n<\/g:mi>\n                     <\/g:math>\n                  <\/jats:inline-formula>-gram extraction model \u201cattentive <jats:inline-formula>\n                     <i:math xmlns:i=\"http:\/\/www.w3.org\/1998\/Math\/MathML\" id=\"M6\">\n                        <i:mi>n<\/i:mi>\n                     <\/i:math>\n                  <\/jats:inline-formula>-gram network\u201d (ANN) based on the attention mechanism and multilayer perceptron, in which the attention mechanism scores each <jats:inline-formula>\n                     <k:math xmlns:k=\"http:\/\/www.w3.org\/1998\/Math\/MathML\" id=\"M7\">\n                        <k:mi>n<\/k:mi>\n                     <\/k:math>\n                  <\/jats:inline-formula>-gram in a sentence by mining the internal semantic relationship between words, and their importance is given by the scores. Experimental results on the real corpus show that the key <jats:inline-formula>\n                     <m:math xmlns:m=\"http:\/\/www.w3.org\/1998\/Math\/MathML\" id=\"M8\">\n                        <m:mi>n<\/m:mi>\n                     <\/m:math>\n                  <\/jats:inline-formula>-gram extracted from our model can distinguish a novel, news, and text book very well; the accuracy of our model is significantly higher than the baseline model. Also, we conduct experiments on key <jats:inline-formula>\n                     <o:math xmlns:o=\"http:\/\/www.w3.org\/1998\/Math\/MathML\" id=\"M9\">\n                        <o:mi>n<\/o:mi>\n                     <\/o:math>\n                  <\/jats:inline-formula>-grams extracted from these registers, which turned out to be well clustered. Furthermore, we make some statistical analyses of the results of key <jats:inline-formula>\n                     <q:math xmlns:q=\"http:\/\/www.w3.org\/1998\/Math\/MathML\" id=\"M10\">\n                        <q:mi>n<\/q:mi>\n                     <\/q:math>\n                  <\/jats:inline-formula>-gram extraction. We find that the key <jats:inline-formula>\n                     <s:math xmlns:s=\"http:\/\/www.w3.org\/1998\/Math\/MathML\" id=\"M11\">\n                        <s:mi>n<\/s:mi>\n                     <\/s:math>\n                  <\/jats:inline-formula>-grams extracted by our model are very explanatory in linguistics.<\/jats:p>","DOI":"10.1155\/2021\/5264090","type":"journal-article","created":{"date-parts":[[2021,5,18]],"date-time":"2021-05-18T17:51:43Z","timestamp":1621360303000},"page":"1-16","source":"Crossref","is-referenced-by-count":1,"title":["Key <a:math xmlns:a=\"http:\/\/www.w3.org\/1998\/Math\/MathML\" id=\"M1\">\n                     <a:mi>n<\/a:mi>\n                  <\/a:math>-Gram Extractions and Analyses of Different Registers Based on Attention Network"],"prefix":"10.1155","volume":"2021","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-0722-3042","authenticated-orcid":true,"given":"Haiyan","family":"Wu","sequence":"first","affiliation":[{"name":"Zhejiang University of Finance and Economics, Hangzhou 310018, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-1230-6395","authenticated-orcid":true,"given":"Ying","family":"Liu","sequence":"additional","affiliation":[{"name":"School of Humanities, Tsinghua University, Beijing 100084, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-1524-7132","authenticated-orcid":true,"given":"Shaoyun","family":"Shi","sequence":"additional","affiliation":[{"name":"Department of Computer Science and Technology, Institute for Artificial Intelligence, Tsinghua University, Beijing 100084, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Qingfeng","family":"Wu","sequence":"additional","affiliation":[{"name":"Chengdu Polytechnic, Chengdu 610095, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-8694-3717","authenticated-orcid":true,"given":"Yunlong","family":"Huang","sequence":"additional","affiliation":[{"name":"Beijing Normal University, Beijing 100875, China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"311","reference":[{"issue":"2","key":"1","first-page":"7","article-title":"Russian register of patients with chronic myeloid leukemia","volume":"52","author":"A. 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