@inproceedings{cf3c9ea8492b4eddb399a1b1923af878,
title = "BI-CARU Feature Extraction for Semantic Analysis",
abstract = "Semantic analysis is an important part of the NLP task that enables computers to understand humans. In this paper, we present the feature extraction method of our results by applying a bidirectional RNN network with CARU units and filter the feature for each domain aspect word with major information extraction. We also developed a weighted layer to represent the combination of hidden states generated by two CARU layers. Finally, the output states are concatenated to an advanced self-weighting layer to obtain more accuracy. The experimental results show that the proposed method can slightly improve the accuracy and has great potential for improvement and analysis for any neural network using RNN architecture.",
keywords = "BI-RNN, CARU, NLP, Self-Weighting, Semantic Analysis",
author = "Chan, {Ka Hou} and Im, {Sio Kei}",
note = "Publisher Copyright: {\textcopyright} 2022 IEEE.; 5th International Conference on Information and Communications Technology, ICOIACT 2022 ; Conference date: 24-08-2022 Through 25-08-2022",
year = "2022",
doi = "10.1109/ICOIACT55506.2022.9971849",
language = "English",
series = "ICOIACT 2022 - 5th International Conference on Information and Communications Technology: A New Way to Make AI Useful for Everyone in the New Normal Era, Proceeding",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
pages = "183--187",
booktitle = "ICOIACT 2022 - 5th International Conference on Information and Communications Technology",
address = "United States",
}