基于高斯分布引导位置相关性权重的情感分类

Gaussian Distribution Guided Position Relevance Weight for Sentiment Classification

针对当前情感分类方法通常忽略不同单词之间相对位置特征,导致模型难以学习到单词的最佳位置表示.为了解决这一问题,提出一种基于高斯分布引导位置相关性权重的情感分类算法.首先,计算每个单词与其他单词之间的位置相关性;其次,利用改进的高斯分布函数对位置相关性进行建模,并将其结果与单词的特征向量相乘,以生成单词的位置感知表示;最后,将算法集成到传统模型中以验证其有效性.实验结果表明,所提方法较传统模型获得了更高的准确率,在域内、域外和对抗评估指标上分别提高了2.98%、5.02%和10.55%,表明其具有较好的实用价值.

The current sentiment classification methods often ignore the relative positional features between different words,which makes it difficult for the model to learn the best positional representation of words.To solve this problem,a sentiment classification algorithm based on Gaussian distribution guided position relevance weight is proposed.First,the positional relevance between each word and other words is calculated.Second,the positional relevance is modeled by using an improved Gaussian distribution function,and the results are multiplied with the feature vectors of the words to generate a positional-aware representation of the words.Finally,the algorithm is integrated into the traditional model to verify its effectiveness.The experimental results show that the proposed method obtains higher accuracy than the traditional model,with improvements of 2.98%,5.02%,and 10.55%in terms of in-domain,out-of-domain,and adversarial evaluation metrics,respectively,indicating its excellent practical value.