DomBERT: Domain-oriented Language Model for Aspect-based Sentiment Analysis
DomBERT: Domain-oriented Language Model for Aspect-based Sentiment Analysis
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DOI：
10.18653/v1/2020.findings-emnlp.156
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发表时间：
2020-04
期刊：
影响因子：
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通讯作者：
Hu Xu;Bing Liu;Lei Shu;Philip S. Yu
中科院分区：
文献类型：
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作者：
Hu Xu;Bing Liu;Lei Shu;Philip S. Yu
本文重点研究由最终任务驱动的面向领域的语言模型，旨在将通用语言模型（例如 ELMo 和 BERT）和特定领域语言理解的世界结合起来。我们提出 DomBERT，它是 BERT 的扩展，可以从域内语料库和相关域语料库中学习。这有助于在资源匮乏的情况下学习领域语言模型。对基于方面的情感分析 (ABSA) 中的各种任务进行了实验，展示了有希望的结果。
This paper focuses on learning domain-oriented language models driven by end tasks, which aims to combine the worlds of both general-purpose language models (such as ELMo and BERT) and domain-specific language understanding. We propose DomBERT, an extension of BERT to learn from both in-domain corpus and relevant domain corpora. This helps in learning domain language models with low-resources. Experiments are conducted on an assortment of tasks in aspect-based sentiment analysis (ABSA), demonstrating promising results.