文章基本信息

标题：Aspect Term Extraction Based on MFE-CRF
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作者：Yanmin Xiang ; Hongye He ; Jin Zheng 等
期刊名称：Information
电子版ISSN：2078-2489
出版年度：2018
卷号：9
期号：8
页码：198
DOI：10.3390/info9080198
语种：English
出版社：MDPI Publishing
摘要：This paper is focused on aspect term extraction in aspect-based sentiment analysis (ABSA), which is one of the hot spots in natural language processing (NLP). This paper proposes MFE-CRF that introduces Multi-Feature Embedding (MFE) clustering based on the Conditional Random Field (CRF) model to improve the effect of aspect term extraction in ABSA. First, Multi-Feature Embedding (MFE) is proposed to improve the text representation and capture more semantic information from text. Then the authors use kmeans++ algorithm to obtain MFE and word clustering to enrich the position features of CRF. Finally, the clustering classes of MFE and word embedding are set as the additional position features to train the model of CRF for aspect term extraction. The experiments on SemEval datasets validate the effectiveness of this model. The results of different models indicate that MFE-CRF can greatly improve the Recall rate of CRF model. Additionally, the Precision rate also is increased obviously when the semantics of text is complex.
关键词：sentiment analysis; aspect term extraction; MFE; CRF; kmeans++ algorithm sentiment analysis ; aspect term extraction ; MFE ; CRF ; kmeans++ algorithm