融合多粒度动态语义表征的文本分类模型
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淮阴工学院 计算机与软件工程学院

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TP391; TQ072

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国家重点研发计划(2018YFB1004904);江苏省六大人才高峰资助项目(XYDXXJS-011);江苏省333工程资助项目(BRA2016454);江苏省教育厅重大研究项目(18KJA520001);淮阴工学院研究生科技创新计划项目:HGYK202121


Incorporating Multi-Granularity Dynamic Semantic Representation with Text Classification Model
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Huaiyin Institute of Technology

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    摘要:

    在对化工领域类文本进行分类任务时,由于文本的专业性以及复杂多样性,仅仅依靠现有的词向量表征方式,很难对其中的专业术语以及其他化工领域内相关字词的语义进行充分表征,从而导致分类任务的准确率不高。本文提出一种融合多粒度动态语义表征的文本分类模型,首先在词嵌入层使用动态词向量表征语义信息并引入对抗扰动,使得词向量具有更好的表征能力,然后利用多头注意力机制进行词向量权重分配,获得带有关键语义信息的文本表示,最后使用提出的多尺度残差收缩深层金字塔形的卷积神经网络与混合注意力胶囊双向LSTM网络模型分别提取不同粒度的文本表示,融合后对得到的最终文本表示进行分类。实验结果表明,相比于现有模型,所提出的模型使用不同词向量表示时,在化工领域文本数据集上F1-score最高可达84.62%,提升了0.38~5.58个百分点;在公开中文数据集THUCNews和谭松波酒店评论数据集ChnSentiCorp上进行模型泛化性能评估,模型也有较好表现。

    Abstract:

    In the classification task of the fairly specialized chemical domain texts, it is hard to wholly characterize the spe-cialized phrases and some other phrases in the domain just by way of relying on the current word vector representation due to the specialized and complex diversity of the texts, which leads to the low accuracy of the classification task for chemical domain texts. To tackle these problems, the text classification model incorporating multi-granularity dy-namic semantic representations was proposed. Firstly, the adversarial perturbation was introduced into the word embedding layer of the model to enhance the ability of dynamic word vectors to represent the semantics. Then the word vector weights were redistributed by a multi-headed attention mechanism to obtain a better textual represen-tation of key semantic information. Finally, text representations of different granularities were extracted through the proposed multi-scale residual shrinkage deep pyramidal convolutional neural network (MSRS-DPCNN) and hybrid attention capsule bidirectional LSTM (HAC-BiLSTM) network model, fusing them and classifying the result of the fusion. Compared with existing models, the experimental results show that the proposed model achieves an F1-score of up to 84.62% on the chemical domain text dataset when using different word vector representations, an im-provement of 0.38 ~ 5.58 percentage points; The model also had better performance when it was evaluated for model generalization performance on the publicly available Chinese dataset THUCNews and the Tan Songbo hotel review dataset ChnSentiCorp.

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  • 收稿日期:2022-01-12
  • 最后修改日期:2022-04-18
  • 录用日期:2022-04-25
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