Chinese E-commerce NER Using RoBERTa-wmm under the Machine Reading Comprehension Paradigm
DOI:
https://doi.org/10.54097/fcis.v5i2.12817Keywords:
Deep Learning, Machine Reading Comprehension, Named Entity RecognitionAbstract
In practical applications within the e-commerce domain, there is often a requirement to identify product entities and their corresponding brand entities, based on their descriptions. However, there has been a relatively limited focus on studies addressing the Named Entities Recognition in the e-commerce domain. we crawled data from e-commerce websites and transformed them into a Named Entity Recognition dataset, which is suitable for Machine Reading Comprehension. Since the questions Machine Reading Comprehension contain a priori semantic information about the types of the entities, we propose a model that uses the MRC modeling paradigm to solve the task of recognizing brand entities as well as commodity entities in the e-commerce domain. The model encodes the contexts and the corresponding questions using the RoBERTa-wwm model, and then further extracts the semantic information of the contexts using an attention network. We utilize SoftMax as the decoding layer to get the head index and tail index of the entity, and finally use the matching module to get the entity index. Through experiments on two e-commerce datasets, the results show that the new method can significantly improve the recognition effect of Chinese NER in e-commerce domain.
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Diefenbach D, Lopez V, Singh K, et al. Core techniques of question answering systems over knowledge bases: a survey [J]. Knowledge and Information systems, 2018, 55(3): 529-569.
Dandapat S, Way A. Improved named entity recognition using machine translation-based cross-lingual information[J]. Computacióny Sistemas, 2016, 20(3): 495-504.
Cheng Q, Liu J, Qu X, et al. HacRED: A large-scale relation extraction dataset toward hard cases in practical applications [C]. Findings of the Association for Computational Linguistics: ACL-IJCNLP 2021, 2021: 2819-2831.
Martins P H, Marinho Z, Martins A F T. Joint learning of named entity recognition and entity linking[C]. Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics. student Research Workshop, 2019: 190-196.
Levy O, Seo M, Choi E, et al. Zero-Shot Relation Extraction via Reading Comprehension[C]. Proceedings of the 21st Conference on Computational Natural Language Learning. 2017: 333-342.
McCann B, Keskar N S, Xiong C, et al. The natural language decathlon: multitask learning as question answering[J]. arXiv preprint arXiv:1806.08730, 2018. [Online]. Available: https:// arxiv. org/pdf/1806.08730.pdf
Li X, Yin F, Sun Z, et al. Entity-Relation Extraction as Multi-Turn Question Answering[C]. Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics. 2019: 1340-1350.
Fei Y, Xu X. GFMRC: A Machine Reading Comprehension Model for Named Entity Recognition[J]. Pattern Recognition Letters, 2023.
Li X, Feng J, Meng Y, et al. A Unified MRC Framework for Named Entity Recognition[C]. Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics. 2020: 5849-5859.
Chen P, Wang J, Lin H, et al. Knowledge Adaptive Multi-way Matching Network for Biomedical Named Entity Recognition via Machine Reading Comprehension[J]. IEEE Transactions on Computational Biology and Bioinformatics, 2023.
Zhang Y, Zhang H. FinBERT-MRC: Financial Named Entity Recognition Using BERT Under the Machine Reading Comprehension Paradigm[J]. Neural Processing Letters, 2023: 1-21.
Ding R, Xie P, Zhang X, et al. A neural multi-digraph model for Chinese NER with gazetteers[C]. Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics. 2019:1462-1467.
Rajpurkar P, Zhang J, Lopyrev K, et al. SQuAD: 100,000+ Questions for Machine Comprehension of Text[C]. Proceedings of the 2016 Conference on Empirical Methods in Natural Language Processing. 2016: 2383-2392.
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