计算机科学
人工智能
特征(语言学)
机器学习
模式识别(心理学)
融合
语言学
哲学
标识
DOI:10.1016/j.knosys.2021.107525
摘要
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 short-term 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.
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