一种结合词性及注意力的句子情感分类方法

A Sentence Sentiment Classification Method with POS and Attention

针对目前各种基于长短期记忆网络LSTM的句子情感分类方法没有考虑词的词性信息这一问题,将词性与自注意力机制相结合,提出一种面向句子情感分类的神经网络模型PALSTM(Pos and Attention-based LSTM).首先,结合预训练词向量和词性标注工具分别给出句子中词的语义词向量和词性词向量表示,并作为LSTM的输入用于学习词在内容和词性方面的长期依赖关系,有效地弥补了一般LSTM单纯依赖预训练词向量中词的共现信息的不足;接着,利用自注意力机制学习句子中词的位置信息和权重向量,并构造句子的最终语义表示;最后由多层感知器进行分类和输出.实验结果表明,PALSTM在公开语料库Movie Reviews、Internet Movie Database和Stanford Sentiment Treebank二元分类及五元情感上的准确率均比一般的LSTM和注意力LSTM模型有一定的提升.

Aiming at the problem that most of existing LSTM-based methods for sentence sentiment classification don t take into account the part-of-speech (POS) information of words,a neural network model,PALSTM,which combines POS and self-attention mechanism,was proposed and applied to sentence sentiment classification.Firstly,PALSTM used pre-trained word vectors and POS tagging tool to give the semantic and POS word vector representations of words in the sentences,and then took them as the inputs of a LSTM so as to capture the long-term dependence of words on content and part-of-speech,which effectively compensates for the common LSTM networks relying solely on the co-occurrence information of words in pre-trained word vectors.Secondly,the self-attention mechanism was used to learn the position information about words in the sentences and build the corresponding position weight matrix,which yields the final semantic representations of sentences.Finally,the results was classified and outputted via a multi-layer perceptron.The experiments show that PALSTM outperforms common LSTM and attentional LSTM models on some open corpus,i.e.Movie Reviews,Internet Movie Database,Stanford Sentiment Treebank binary and fine-grained classification.