Chinese Short Text Classification with Mutual-Attention Convolutional Neural Networks

Some text classification methods don’t work well on short texts due to the data sparsity. What’s more, they don’t fully exploit context-relevant knowledge. In order to tackle these problems, we propose a neural network to incorporate context-relevant knowledge into a convolutional neural network for short text classification. Our model consists of two modules. The first module utilizes two layers to extract concept and context features respectively and then employs an attention layer to extract those context-relevant concepts. The second module utilizes a convolutional neural network to extract high-level features from the word and the contextrelevant concept features. The experimental results on three datasets show that our proposed model outperforms the stateof-the-art models.

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Character-Level Attention Convolutional Neural Networks for Short-Text Classification

Human Centered Computing - Lecture Notes in Computer Science ◽

10.1007/978-3-030-37429-7_57 ◽

2019 ◽

pp. 560-567

Author(s):

Feiyang Yin ◽

Zhilin Yao ◽

Jia Liu

Keyword(s):

Neural Networks ◽

Convolutional Neural Networks ◽

Text Classification ◽

Short Text

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Best Practices of Convolutional Neural Networks for Question Classification

Applied Sciences ◽

10.3390/app10144710 ◽

2020 ◽

Vol 10 (14) ◽

pp. 4710 ◽

Cited By ~ 5

Author(s):

Marco Pota ◽

Massimo Esposito ◽

Giuseppe De Pietro ◽

Hamido Fujita

Keyword(s):

Neural Networks ◽

Best Practices ◽

Convolutional Neural Networks ◽

Text Classification ◽

Subjective Experience ◽

Question Answering ◽

Statistical Significance ◽

Short Text ◽

Question Classification ◽

Optimal Settings

Question Classification (QC) is of primary importance in question answering systems, since it enables extraction of the correct answer type. State-of-the-art solutions for short text classification obtained remarkable results by Convolutional Neural Networks (CNNs). However, implementing such models requires choices, usually based on subjective experience, or on rare works comparing different settings for general text classification, while peculiar solutions should be individuated for QC task, depending on language and on dataset size. Therefore, this work aims at suggesting best practices for QC using CNNs. Different datasets were employed: (i) A multilingual set of labelled questions to evaluate the dependence of optimal settings on language; (ii) a large, widely used dataset for validation and comparison. Numerous experiments were executed, to perform a multivariate analysis, for evaluating statistical significance and influence on QC performance of all the factors (regarding text representation, architectural characteristics, and learning hyperparameters) and some of their interactions, and for finding the most appropriate strategies for QC. Results show the influence of CNN settings on performance. Optimal settings were found depending on language. Tests on different data validated the optimization performed, and confirmed the transferability of the best settings. Comparisons to configurations suggested by previous works highlight the best classification accuracy by those optimized here. These findings can suggest the best choices to configure a CNN for QC.

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Sequential Short-Text Classification with Recurrent and Convolutional Neural Networks

10.18653/v1/n16-1062 ◽

2016 ◽

Cited By ~ 83

Author(s):

Ji Young Lee ◽

Franck Dernoncourt

Keyword(s):

Neural Networks ◽

Convolutional Neural Networks ◽

Text Classification ◽

Short Text

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Combining Knowledge with Deep Convolutional Neural Networks for Short Text Classification

Proceedings of the Twenty-Sixth International Joint Conference on Artificial Intelligence ◽

10.24963/ijcai.2017/406 ◽

2017 ◽

Cited By ~ 69

Author(s):

Jin Wang ◽

Zhongyuan Wang ◽

Dawei Zhang ◽

Jun Yan

Keyword(s):

Neural Networks ◽

Knowledge Base ◽

Convolutional Neural Networks ◽

Text Classification ◽

State Of The Art ◽

Experimental Results ◽

Text Representation ◽

Deep Convolutional Neural Networks ◽

Short Text ◽

Fine Grained

Text classification is a fundamental task in NLP applications. Most existing work relied on either explicit or implicit text representation to address this problem. While these techniques work well for sentences, they can not easily be applied to short text because of its shortness and sparsity. In this paper, we propose a framework based on convolutional neural networks that combines explicit and implicit representations of short text for classification. We first conceptualize a short text as a set of relevant concepts using a large taxonomy knowledge base. We then obtain the embedding of short text by coalescing the words and relevant concepts on top of pre-trained word vectors. We further incorporate character level features into our model to capture fine-grained subword information. Experimental results on five commonly used datasets show that our proposed method significantly outperforms state-of-the-art methods.

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