基于预训练语言模型的商品属性抽取

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Abstract Attribute extraction is a key step of constructing a knowledge graph. In this paper, the task of attribute extraction is converted into a sequence labeling problem. Due to a lack of labeling data in product attribute extraction, we use the distant supervision to automatically label multiple source texts related to e-commerce. In order to accurately evaluate the performance of the system, we construct a manually annotated test set, and finally obtain a new data set for product attribute extraction in multi-domains. Based on the newly constructed data set, we carried out intra-domain and cross-domain attribute extraction for a variety of pre-trained language models. The experimental results show that the pre-trained language models can better improve the extraction performance. Among them, ELECTRA performs the best in attribute extraction in in-domain experiments, and BERT performs the best in cross-domain experiments. we also find that adding a small amount of target domain annotation data can effectively improve the performance cross-domain attribute extraction and enhance the domain adaptability of the model.

Key words： attribute extraction distant supervision pre-trained language model domain adaptation

Received: 29 January 2021

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	Articles by authors
	ZHANG Shiqi
	MA Jin
	ZHOU Xiabing
	JIA Hao
	CHEN Wenliang
	ZHANG Min

Cite this article:

ZHANG Shiqi,MA Jin,ZHOU Xiabing, et al. Pre-trained Language Models for Product Attribute Extraction[J]. , 2022, 36(1): 56-64.

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http://jcip.cipsc.org.cn/EN/ OR http://jcip.cipsc.org.cn/EN/Y2022/V36/I1/56

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