属性抽取是细粒度情感分析的子任务之一,其目标是从评论文本中抽取用户所评价的属性。在特定领域中,某些属性可能会频繁出现在不同的评论文本中,称之为高频属性。高频属性具有较高的领域表征能力,易被监督学习模型感知。相对地,低频属性出现频率低,可供训练的样本总量较为稀疏,使得神经网络模型难以充分学习相应的语言现象,从而使测试阶段的低频属性抽取难度较高。由于低频属性经常与高频属性同时出现在局部文字片段之中,该文根据这一特点,提出一种融合高频属性信息的属性抽取方法: 跟踪和记录模型识别的高频属性,使用卷积神经网络和注意力机制编码高频属性的上下文信息,并通过门控机制融入其他词项的表示学习过程中,辅助低频属性的识别。该文在国际语义评测大会2014和2016提供的笔记本电脑及餐馆领域数据集上进行了实验,相比于基线模型,该文方法在这两个英文数据集上F1值分别提升了2.33和1.44个百分点,并且总体性能高于现有前沿技术。
Abstract
Aspect extraction is one subtask of fine-grained sentiment analysis, which aims to extract the aspects that users express opinions on comments. Appearing in various comments, high-frequency aspects have strong domain representation ability and are easy to be perceived by the supervised learning model. We propose an aspect extraction method that integrates high-frequency aspects information. We track and record the high-frequency aspects recognized by model, encode the context information of high-frequency aspects by convolutional neural network and attention mechanism, and integrate the information into the representation learning process through the gated mechanism. Experiments on two benchmark datasets: Laptop of SemEval-14 and Restaurant of SemEval-16 demonstrate 2.33% and 1.44% improvement, respectively, compared with the baseline models.
关键词
属性抽取 /
高频属性 /
门控机制
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Key words
aspect extraction /
high-frequency aspects /
gated mechanism
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参考文献
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基金
国家自然科学基金(61672367,61751206,62076174)
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