Multi-domain Aspect Extraction Based on Deep and Lifelong Learning

被引:1
|
作者
Lopez, Dionis [1 ,2 ]
Arco, Leticia [2 ,3 ]
机构
[1] Univ Oriente, Fac Engn Telecommun Informat & Biomed, Dept Informat, Ave Amer S-N, Santiago De Cuba 90900, Cuba
[2] Univ Cent Marta Abreu Las Villas, Fac Math Phys & Comp Sci, Dept Comp Sci, Carretera Camajuani Km 5 1-2, Santa Clara 54830, Cuba
[3] Vrije Univ Brussel, Dept Comp Sci, AI Lab, Pl Laan 9, B-1050 Brussels, Belgium
来源
PROGRESS IN PATTERN RECOGNITION, IMAGE ANALYSIS, COMPUTER VISION, AND APPLICATIONS (CIARP 2019) | 2019年 / 11896卷
关键词
Opinion mining; Aspect extraction; Deep learning; Lifelong learning;
D O I
10.1007/978-3-030-33904-3_52
中图分类号
TP18 [人工智能理论];
学科分类号
081104 ; 0812 ; 0835 ; 1405 ;
摘要
Opinions concerning features or aspects of people, entities, products or services are some of the most important textual information. Several methods try to solve the aspect extraction task needed in sentiment analysis by using Deep Learning techniques in specific domains. However, catastrophic forgetting appears when these methods are used to learn aspects of multi-domains. In this paper, we propose a new approach to achieve aspect extraction in multi-domains based on Deep and Lifelong Learning techniques. Our proposal reduces catastrophic forgetting and improves one of the principal state-of-the-art results.
引用
收藏
页码:556 / 565
页数:10
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