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Improving Intent Extraction Using Ensemble Neural Network

Luong, Thai Le and Tran, Nhu Thuat and Phan, Xuan Hieu (2019) Improving Intent Extraction Using Ensemble Neural Network. In: The 19th International Symposium on Communications and Information Technologies (ISCIT 2019), 25-27/09/2019, Ho Chi Minh City, Vietnam.

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User intent extraction from social media texts is aimed at identifying user intent keyword and its related information. This topic has attracted a lot of researches since its various applications in online marketing, e-commerce and business services. One of such studies is to model this problem as a sequence labeling task and apply state-of-the-art sequential tagging models such as BiLSTM [12] and BiLSTM-CRFs [12]. In this paper, we take a further step to enhance intent extraction results based on tri-training [23] and ensemble learning [2]. Specifically, we simultaneously use three BiLSTM-CRFs models, each of them is different from others by the type of word embeddings, and apply majority voting scheme over their predicted labels when decoding final labels. Extensive experiments on data from three domains Real Estate, Tourism and Transportation show that our proposed methods enjoy a better performance compared to single model based approach.

Item Type: Conference or Workshop Item (Paper)
Subjects: Information Technology (IT)
Divisions: Faculty of Information Technology (FIT)
Depositing User: A/Prof. Xuan Hieu Phan
Date Deposited: 27 Nov 2019 16:02
Last Modified: 27 Nov 2019 16:02

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