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To address the challenge, an unsupervised learning approach to discover intents and automatically produce the labels from a collection of unlabeled utterances in the context of the banking domain is proposed. The proposed approach focuses on improving both architectures of DEC and IDEC by combining the Jensen\u2013Shannon (JS) divergence to simultaneously learn feature representations and cluster assignments, and the Second-order Clipped Stochastic Optimization (Sophia). Then, a set of intent labels for each cluster is generated by using a dependency parser in the second stage. Experimental results showed that the proposed approach is capable of generating meaningful intent labels and short text clustering with high performance. <\/jats:p>","DOI":"10.1142\/s175289092440004x","type":"journal-article","created":{"date-parts":[[2024,2,28]],"date-time":"2024-02-28T15:25:41Z","timestamp":1709133941000},"source":"Crossref","is-referenced-by-count":0,"title":["Customer Intent Mining from Service Inquiries with Newly Improved Deep Embedded Clustering"],"prefix":"10.1142","volume":"17","author":[{"given":"Nguyen Q. K.","family":"Ha","sequence":"first","affiliation":[{"name":"Department of Information Technology, College of Technology and Design, University of Economics Ho Chi Minh City, Ho Chi Minh City 700000, Vietnam"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Nguyen T. 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