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Title: Sub-word attention mechanism and ensemble learning-based semantic annotation for heterogeneous networks

Authors: Liang Zhang; Zhaobin Liu; Jinxiang Li; Gang Liu; Yuanfeng Yang; Yi Jin; Xu Zhang

Addresses: School of Computer Engineering, Suzhou Vocational University, No. 106, Zhineng Str., International Education Park, Suzhou 215104, China ' School of Computer Engineering, Suzhou Vocational University, No. 106, Zhineng Str., International Education Park, Suzhou 215104, China ' School of Computer Engineering, Suzhou Vocational University, No. 106, Zhineng Str., International Education Park, Suzhou 215104, China ' School of Computer Engineering, Suzhou Vocational University, No. 106, Zhineng Str., International Education Park, Suzhou 215104, China ' School of Computer Engineering, Suzhou Vocational University, No. 106, Zhineng Str., International Education Park, Suzhou 215104, China ' School of Computer Engineering, Suzhou Vocational University, No. 106, Zhineng Str., International Education Park, Suzhou 215104, China ' School of Computer Engineering, Suzhou Vocational University, No. 106, Zhineng Str., International Education Park, Suzhou 215104, China

Abstract: The sensing device and wireless sensor networks (WSN) can provide information to the application of Internet of Things (IoT), but data from different types of devices present significant polyphyly and heterogeneity, which poses challenges to the collaboration and interaction of information resources in IoT application and service. Especially when the device uses Chinese characters for information representation and there is an Out-of-Vocabulary (OOV) problem, it will make this work more challenging. This paper introduces an ensemble learning model based on sub-word attention mechanism and bidirectional long short-term memory model (SWAT-Bi-LSTM) which can provide an internal structural attention ability of Chinese characters. The boosting strategy and gradient boosting decision tree (GDBT) integration scheme is adopted to complete the final integrated output. The experimental results show that the proposed method can effectively improve the accuracy of sentiment analysis, and the integrated learning model can further improve the accuracy and stability.

Keywords: sentiment analysis; sub-word units; Bi-LSTM; ensemble learning; WSN; wireless sensor networks.

DOI: 10.1504/IJWMC.2020.104773

International Journal of Wireless and Mobile Computing, 2020 Vol.18 No.1, pp.51 - 58

Received: 06 Jul 2019
Accepted: 07 Aug 2019

Published online: 28 Jan 2020 *

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