A Drift-Sensitive Distributed LSTM Method for Short Text Stream Classification-Reference-Cited by-同舟云学术

A Drift-Sensitive Distributed LSTM Method for Short Text Stream Classification

Published:2023-02-01 Issue:1 Volume:9 Page:341-357
ISSN:2332-7790
Container-title:IEEE Transactions on Big Data
language:
Short-container-title:IEEE Trans. Big Data

Author:

Li Peipei¹^ORCID,Liu Yingying¹,Hu Yang²,Zhang Yuhong¹^ORCID,Hu Xuegang¹^ORCID,Yu Kui¹^ORCID

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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