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23 May 2022 A multi-level information extraction model for end-to-end aspect-based sentiment analysis
Jiale Wang, Wensheng Sun
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Proceedings Volume 12254, International Conference on Electronic Information Technology (EIT 2022); 1225427 (2022) https://doi.org/10.1117/12.2638580
Event: International Conference on Electronic Information Technology (EIT 2022), 2022, Chengdu, China
Abstract
Aspect-based sentiment analysis mainly processes natural language and generates its aspect term and corresponding sentiment. Previous studies focused on individual subtasks, did not make full use of large-scale corpus and dig out the semantic information of sentences, and did not consider the different contributions of different words in aspect-based sentiment analysis. In this paper, a unified sequence labeling BIO model is used, and the two subtasks of aspect word extraction and sentiment analysis are fused. By sharing a unified BERT model, and using the auxiliary training of domain-related documents, the attention is focused on aspect words and emotional words. Through experiments, a better effect was finally achieved.
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Jiale Wang and Wensheng Sun "A multi-level information extraction model for end-to-end aspect-based sentiment analysis", Proc. SPIE 12254, International Conference on Electronic Information Technology (EIT 2022), 1225427 (23 May 2022); https://doi.org/10.1117/12.2638580
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KEYWORDS
Data modeling
Analytical research
Data hiding
Lithium
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Transformers
Networks
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