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Firstly, text corpus is preprocessed by word segmentation and stop words processing and uses word embedding to form the matrix of word vectors. Then, local semantic features are extracted through convolution operation, and deep context semantic features are extracted through RDBiGRU. Finally, the learned features are activated by softmax function and the recognition results are output. The novelty of work is that we integrate residual structure into recurrent neural network and combine these methods and field of application. The simulation results show that this method improves precision and recall of Chinese emergency event recognition, and the <jats:italic>F<\/jats:italic>-value is better than other methods.<\/jats:p>","DOI":"10.1155\/2020\/7090918","type":"journal-article","created":{"date-parts":[[2020,5,21]],"date-time":"2020-05-21T23:38:05Z","timestamp":1590104285000},"page":"1-12","source":"Crossref","is-referenced-by-count":5,"title":["Chinese Emergency Event Recognition Using Conv-RDBiGRU Model"],"prefix":"10.1155","volume":"2020","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-5162-8349","authenticated-orcid":true,"given":"Haoran","family":"Yin","sequence":"first","affiliation":[{"name":"College of Police Information Engineering & Cyber Security, People\u2019s Public Security University of China, Beijing, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-9318-714X","authenticated-orcid":true,"given":"Jinxuan","family":"Cao","sequence":"additional","affiliation":[{"name":"College of Police 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