隐式方面提取的弱监督学习方法

IF 2.4 Q3 COMPUTER SCIENCE, INFORMATION SYSTEMS
Aye Aye Mar, Kiyoaki Shirai, Natthawut Kertkeidkachorn
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引用次数: 0

摘要

基于方面的情感分析(ABSA)是一种从客户评论中提取产品方面并识别其极性的过程。以往的研究大多集中在外显方面,而内隐方面尚未受到重视。本文提出了一种新的弱监督隐式方面提取方法,该方法是将句子分类到预定义的隐式方面类别中。使用隐式方面标记的数据集从未标记的句子自动构建,如下所示。首先,通过从未标记的句子中提取显式方面来获得显式句子,而不包含显式方面的句子则作为隐式句子的候选者保留。其次,对具有相同方面的显式和隐含句子进行聚类合并。第三,将显式句的方面与隐式句的方面标签分配给同一簇中的隐式句。然后,使用构建的数据集对BERT模型进行微调,以进行隐式方面提取。实验结果表明,我们的方法在手机评论和PC评论上分别达到82%和84%的准确率,比基线提高了20和21个百分点。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Weakly Supervised Learning Approach for Implicit Aspect Extraction
Aspect-based sentiment analysis (ABSA) is a process to extract an aspect of a product from a customer review and identify its polarity. Most previous studies of ABSA focused on explicit aspects, but implicit aspects have not yet been the subject of much attention. This paper proposes a novel weakly supervised method for implicit aspect extraction, which is a task to classify a sentence into a pre-defined implicit aspect category. A dataset labeled with implicit aspects is automatically constructed from unlabeled sentences as follows. First, explicit sentences are obtained by extracting explicit aspects from unlabeled sentences, while sentences that do not contain explicit aspects are preserved as candidates of implicit sentences. Second, clustering is performed to merge the explicit and implicit sentences that share the same aspect. Third, the aspect of the explicit sentence is assigned to the implicit sentences in the same cluster as the implicit aspect label. Then, the BERT model is fine-tuned for implicit aspect extraction using the constructed dataset. The results of the experiments show that our method achieves 82% and 84% accuracy for mobile phone and PC reviews, respectively, which are 20 and 21 percentage points higher than the baseline.
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来源期刊
Information (Switzerland)
Information (Switzerland) Computer Science-Information Systems
CiteScore
6.90
自引率
0.00%
发文量
515
审稿时长
11 weeks
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