Does Named Entity Recognition Truly Not Scale Up to Real-world Product Attribute Extraction? (2023.emnlp-industry)
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| Challenge: | scalability of attribute-value extraction (AVE) task is key for a large number of products . a question-answering (QA)-based approach is better for AVE, but requires a larger number of classes to be scalable. |
| Approach: | They propose a question-answering-based approach that additionally inputs the target attribute as a query to extract its values. |
| Outcome: | The proposed approach outperforms a classical approach on real-word e-commerce datasets in accuracy and speed. |
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| Challenge: | Named entity recognition (NER) is a foundational task for a variety of applications like question answering and machine translation. |
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Yupeng Zhang, Shensi Wang, Peiguang Li, Guanting Dong, Sirui Wang, Yunsen Xian, Zhoujun Li, Hongzhi Zhang
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| Challenge: | Existing models for attribute value extraction struggle for parameter efficiency and reliability due to data contamination and catastrophic forgetting. |
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NERetrieve: Dataset for Next Generation Named Entity Recognition and Retrieval (2023.findings-emnlp)
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| Challenge: | Named Entity Recognition (NER) is a widely adopted NLP task . authors present three variants of NER task, with dataset to support them . |
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