{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,9,16]],"date-time":"2026-09-16T07:56:17Z","timestamp":1789545377669,"version":"build-2803163510"},"reference-count":9,"publisher":"Wiley","license":[{"start":{"date-parts":[[2017,1,1]],"date-time":"2017-01-01T00:00:00Z","timestamp":1483228800000},"content-version":"unspecified","delay-in-days":0,"URL":"http:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Computational Intelligence and Neuroscience"],"published-print":{"date-parts":[[2017]]},"abstract":"<jats:p>We use both reinforcement learning and deep learning to simultaneously extract entities and relations from unstructured texts. For reinforcement learning, we model the task as a two-step decision process. Deep learning is used to automatically capture the most important information from unstructured texts, which represent the state in the decision process. By designing the reward function per step, our proposed method can pass the information of entity extraction to relation extraction and obtain feedback in order to extract entities and relations simultaneously. Firstly, we use bidirectional LSTM to model the context information, which realizes preliminary entity extraction. On the basis of the extraction results, attention based method can represent the sentences that include target entity pair to generate the initial state in the decision process. Then we use Tree-LSTM to represent relation mentions to generate the transition state in the decision process. Finally, we employ <mml:math xmlns:mml=\"http:\/\/www.w3.org\/1998\/Math\/MathML\" id=\"M1\"><mml:mrow><mml:mi>Q<\/mml:mi><\/mml:mrow><\/mml:math>-Learning algorithm to get control policy <mml:math xmlns:mml=\"http:\/\/www.w3.org\/1998\/Math\/MathML\" id=\"M2\"><mml:mrow><mml:mi>\u03c0<\/mml:mi><\/mml:mrow><\/mml:math> in the two-step decision process. Experiments on ACE2005 demonstrate that our method attains better performance than the state-of-the-art method and gets a 2.4% increase in recall-score.<\/jats:p>","DOI":"10.1155\/2017\/7643065","type":"journal-article","created":{"date-parts":[[2017,8,14]],"date-time":"2017-08-14T17:02:58Z","timestamp":1502730178000},"page":"1-11","source":"Crossref","is-referenced-by-count":17,"title":["Joint Extraction of Entities and Relations Using Reinforcement Learning and Deep Learning"],"prefix":"10.1155","volume":"2017","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-1882-1005","authenticated-orcid":true,"given":"Yuntian","family":"Feng","sequence":"first","affiliation":[{"name":"Institute of Command Information System, PLA University of Science and Technology, Nanjing, Jiangsu 210007, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Hongjun","family":"Zhang","sequence":"additional","affiliation":[{"name":"Institute of Command Information System, PLA University of Science and Technology, Nanjing, Jiangsu 210007, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-0880-1227","authenticated-orcid":true,"given":"Wenning","family":"Hao","sequence":"additional","affiliation":[{"name":"Institute of Command Information System, PLA University of Science and Technology, Nanjing, Jiangsu 210007, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Gang","family":"Chen","sequence":"additional","affiliation":[{"name":"Institute of Command Information System, PLA University of Science and Technology, Nanjing, Jiangsu 210007, China"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"311","reference":[{"key":"5","doi-asserted-by":"publisher","DOI":"10.1162\/neco.1997.9.8.1735"},{"key":"6","first-page":"380","volume":"2","year":"2007","journal-title":"Journal of Machine Learning Research"},{"key":"10","first-page":"2773","year":"2015","journal-title":"Advances in Neural Information Processing Systems"},{"key":"14","first-page":"2121","volume":"12","year":"2011","journal-title":"Journal of Machine Learning Research (JMLR)"},{"key":"15","doi-asserted-by":"publisher","DOI":"10.1007\/BF00992698"},{"issue":"2","key":"16","volume":"4","year":"2012","journal-title":"Coursera: Neural Networks for Machine Learning"},{"key":"27","doi-asserted-by":"publisher","DOI":"10.3115\/v1\/W15-1506"},{"key":"28","doi-asserted-by":"publisher","DOI":"10.1155\/2011\/869348"},{"key":"29","volume":"2014","year":"2014","journal-title":"Computational Intelligence and Neuroscience"}],"container-title":["Computational Intelligence and Neuroscience"],"original-title":[],"language":"en","link":[{"URL":"http:\/\/downloads.hindawi.com\/journals\/cin\/2017\/7643065.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"http:\/\/downloads.hindawi.com\/journals\/cin\/2017\/7643065.xml","content-type":"application\/xml","content-version":"vor","intended-application":"text-mining"},{"URL":"http:\/\/downloads.hindawi.com\/journals\/cin\/2017\/7643065.pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2017,8,14]],"date-time":"2017-08-14T17:02:58Z","timestamp":1502730178000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.hindawi.com\/journals\/cin\/2017\/7643065\/"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2017]]},"references-count":9,"alternative-id":["7643065","7643065"],"URL":"https:\/\/doi.org\/10.1155\/2017\/7643065","relation":{},"ISSN":["1687-5265","1687-5273"],"issn-type":[{"value":"1687-5265","type":"print"},{"value":"1687-5273","type":"electronic"}],"subject":[],"published":{"date-parts":[[2017]]}}}