A Drift-Sensitive Distributed LSTM Method for Short Text Stream Classification
Author:
Affiliation:
1. Key Laboratory of Knowledge Engineering with Big Data, Ministry of Education, School of Computer Science and Information Engineering, Hefei University of Technology, Hefei, China
2. Tencent Technology Company, Ltd., Shenzhen, China
Funder
National Key Research and Development Program of China
National Natural Science Foundation of China
Chang Jiang Scholars and Innovative Research Team in the University
Ministry of Education
Publisher
Institute of Electrical and Electronics Engineers (IEEE)
Subject
Information Systems and Management,Information Systems
Link
http://xplorestaging.ieee.org/ielx7/6687317/10017386/09748034.pdf?arnumber=9748034
Reference49 articles.
1. Short text stream clustering via frequent word pairs and reassignment of outliers to clusters;kumar;Proc ACM Symp Document Eng,2020
2. Short Text Stream Clustering via Frequent Word Pairs and Reassignment of Outliers to Clusters
3. Online Biterm Topic Model based short text stream classification using short text expansion and concept drifting detection
4. An lstm approach to short text sentiment classification with word embeddings;wang;Proc 30th Conf Comput Linguistics Speech Process,2018
5. End-to-end Learning for Short Text Expansion
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