AMFF: A new attention-based multi-feature fusion method for intention recognition

Intention recognition is based on a dialog between users to identify their real intentions, which plays a key role in the question answering system. However, the content of a dialog is usually in the form of short text. Due to data sparsity, many current classification models show poor performance on short text. To address this issue, we propose AMFF, an attention-based multi-feature fusion method for intention recognition. In this paper, we enrich short text features by fusing features extracted from frequency-inverse document frequency (TF-IDF), convolutional neural networks (CNNs) and long shortterm memory (LSTM). For the purpose of measuring the important features, we utilize the attention mechanisms to assign weights for the fusion features. Experimental results on the TREC, SST1 and SST2 datasets demonstrate that the proposed AMFF model outperforms traditional machine learning models and typical deep learning models on short text classification. (c) 2021 Elsevier B.V. All rights reserved.

Automated summary

In this paper, we propose an attention-based multi-feature fusion method (AMFF) for intention recognition in short text, which addresses the data sparsity issue. By enriching short text features through the fusion of TF-IDF, CNNs, and LSTM extracted features, as well as using attention mechanisms to measure important features, the experimental results demonstrate that the AMFF model outperforms traditional and typical deep learning models in short text classification.