{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,13]],"date-time":"2026-02-13T23:17:57Z","timestamp":1771024677057,"version":"3.50.1"},"publisher-location":"California","reference-count":0,"publisher":"International Joint Conferences on Artificial Intelligence Organization","content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2018,7]]},"abstract":"<jats:p>Traditional text classification algorithms are based on the assumption that data are independent and identically distributed. However, in most non-stationary scenarios, data may change smoothly due to long-term evolution and short-term fluctuation, which raises new challenges to traditional methods. In this paper, we present the first attempt to explore evolutionary neural network models for time-evolving text classification. We first introduce a simple way to extend arbitrary neural networks to evolutionary learning by using a temporal smoothness framework, and then propose a diachronic propagation framework to incorporate the historical impact into currently learned features through diachronic connections. Experiments on real-world news data demonstrate that our approaches greatly and consistently outperform traditional neural network models in both accuracy and stability.<\/jats:p>","DOI":"10.24963\/ijcai.2018\/310","type":"proceedings-article","created":{"date-parts":[[2018,7,5]],"date-time":"2018-07-05T05:49:10Z","timestamp":1530769750000},"page":"2241-2247","source":"Crossref","is-referenced-by-count":31,"title":["Time-evolving Text Classification with Deep Neural Networks"],"prefix":"10.24963","author":[{"given":"Yu","family":"He","sequence":"first","affiliation":[{"name":"Beijing Advanced Innovation Center for Big Data and Brain Computing, Beihang University, China"},{"name":"State Key Laboratory of Software Development Environment, Beihang University, China"}]},{"given":"Jianxin","family":"Li","sequence":"additional","affiliation":[{"name":"Beijing Advanced Innovation Center for Big Data and Brain Computing, Beihang University, China"},{"name":"State Key Laboratory of Software Development Environment, Beihang University, China"}]},{"given":"Yangqiu","family":"Song","sequence":"additional","affiliation":[{"name":"Department of Computer Science and Engineering, HKUST, Hong Kong"}]},{"given":"Mutian","family":"He","sequence":"additional","affiliation":[{"name":"Beijing Advanced Innovation Center for Big Data and Brain Computing, Beihang University, China"},{"name":"State Key Laboratory of Software Development Environment, Beihang University, China"}]},{"given":"Hao","family":"Peng","sequence":"additional","affiliation":[{"name":"Beijing Advanced Innovation Center for Big Data and Brain Computing, Beihang University, China"},{"name":"State Key Laboratory of Software Development Environment, Beihang University, China"}]}],"member":"10584","event":{"name":"Twenty-Seventh International Joint Conference on Artificial Intelligence {IJCAI-18}","theme":"Artificial Intelligence","location":"Stockholm, Sweden","acronym":"IJCAI-2018","number":"27","sponsor":["International Joint Conferences on Artificial Intelligence Organization (IJCAI)"],"start":{"date-parts":[[2018,7,13]]},"end":{"date-parts":[[2018,7,19]]}},"container-title":["Proceedings of the Twenty-Seventh International Joint Conference on Artificial Intelligence"],"original-title":[],"deposited":{"date-parts":[[2018,7,5]],"date-time":"2018-07-05T05:51:38Z","timestamp":1530769898000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.ijcai.org\/proceedings\/2018\/310"}},"subtitle":[],"proceedings-subject":"Artificial Intelligence Research Articles","short-title":[],"issued":{"date-parts":[[2018,7]]},"references-count":0,"URL":"https:\/\/doi.org\/10.24963\/ijcai.2018\/310","relation":{},"subject":[],"published":{"date-parts":[[2018,7]]}}}