基于防退化策略的多通道闭环BiLSTM在文本分类中的应用研究

双向长短时记忆(BiLSTM)及其变体能够处理可变长度序列,由于文本的复杂语义信息和文本数据嵌入维度的高维性,BiLSTM表现出低层次网络学习能力较弱,通过叠加网络层学习高层次的特征表示,容易出现网络退化问题.为解决这些问题,提出一种闭环BiLSTM模块用于丰富每一层网络结构隐状态的语义信息表示,同时采用残差连接和增强稀疏表示策略来优化模块,稀疏化隐状态特征向量减缓网络退化问题;最后利用加权融合的多通道词嵌入,将语义信息和情感信息在低维张量下实现融合来丰富输入层的文本表示.对情感分类和问题分类的数据集进行了实验验证,实验表明,提出模型在捕捉文本的情感信息表达上具有出色的性能,具有较好的分类精度和鲁棒性.

Multi-channel closed-loop BiLSTM with anti-degradation strategy for text classification

Bidirectional long short-term memory(BiLSTM) and its variants can handle variable length sequences. Due to the complex semantic information of text and the high dimension of embedded text data, BiLSTM shows a weak ability of low-level network learning. And the problem of network degradation is easy to occur when learning high-level feature representation using overlay network layer. In order to solve these problems, this paper proposed a closed-loop BiLSTM module to enrich the semantic representation of hidden state in each layer of network structure. Meanwhile, it used residual connection and enhanced sparse representation strategy to optimize the module, and sparsed the feature vectors of hidden state to alleviate network degradation. Finally, this paper adopted the weighted fusion of multi-channel word embedding and realized the fusion of semantic information and emotional information under the low-dimensional tensor to enrich the text representation of the input layer. This paper verified the datasets of emotional classification and problem classification by experiments. The results show that the proposed model has excellent performance, good classification accuracy and robustness in capturing the expression of text''s emotion information.