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E-commerce platforms require structured product data in the form of attribute-value pairs to offer features such as faceted product search or attribute-based product comparison.
Ghani, R., Probst, K., Liu, Y., et al.: Text mining for product attribute extraction. In: ACM SIGKDD Explorations Newsletter. vol. 8, pp. 41–48 (2006)
2006
Earlier work this paper cites.
Wong, Y.W., Widdows, D., Lokovic, T., et al.: Scalable Attribute-Value Extraction from Semi-structured Text. In: ICDMW. pp. 302–307 (2009)
2009
Earlier work this paper cites.
Zhang, L., Zhu, M., Huang, W.: A Framework for an Ontology-based E-commerce Product Information Retrieval System. JCP 4
2009
Earlier work this paper cites.
Putthividhya, D., Hu, J.: Bootstrapped Named Entity Recognition for Product Attribute Extraction. In: EMNLP. pp. 1557–1567 (2011)
2011
Earlier work this paper cites.
Vandic, D., van Dam, J.W., Frasincar, F.: Faceted product search powered by the Semantic Web. Decision Support Systems 53
2012
Earlier work this paper cites.
Ren, Z., He, X., Yin, D., et al.: Information Discovery in E-commerce: Half-day SIGIR 2018 Tutorial. In: SIGIR. pp. 1379–1382 (2018)
2018
Earlier work this paper cites.
Zheng, G., Mukherjee, S., Dong, X.L., et al.: OpenTag: Open Attribute Value Extraction from Product Profiles. In: SIGKDD. pp. 1049–1058 (2018)
2018
Earlier work this paper cites.
Devlin, J., Chang, M.W., Lee, K., et al.: BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding. In: NAACL. pp. 4171–4186 (2019)
2019
Earlier work this paper cites.
Xu, H., Wang, W., Mao, X., et al.: Scaling up Open Tagging from Tens to Thousands: Comprehension Empowered Attribute Value Extraction from Product Title. In: ACL. pp. 5214–5223 (2019)
2019
Earlier work this paper cites.
Brown, T., Mann, B., Ryder, N., et al.: Language Models are Few-Shot Learners. In: NeurIPS. vol. 33, pp. 1877–1901 (2020)
2020
Earlier work this paper cites.
Wang, Q., Yang, L., Kanagal, B., et al.: Learning to Extract Attribute Value from Product via Question Answering: A Multi-task Approach. In: SIGKDD. pp. 47–55 (2020)
2020
Earlier work this paper cites.
Zhu, T., Wang, Y., Li, H., et al.: Multimodal Joint Attribute Prediction and Value Extraction for E-commerce Product. In: EMNLP. pp. 2129–2139 (2020)
2020
Cited alongside, same era.
Yan, J., Zalmout, N., Liang, Y., et al.: AdaTag: Multi-Attribute Value Extraction from Product Profiles with Adaptive Decoding. In: ACL|IJCNLP. pp. 4694–4705 (2021)
2021
Cited alongside, same era.
Ouyang, L., Wu, J., Jiang, X.: Training language models to follow instructions with human feedback. In: NeurIPS. vol. 35, pp. 27730–27744 (2022)
2022
Cited alongside, same era.
Shinzato, K., Yoshinaga, N., Xia, Y., et al.: Simple and Effective Knowledge-Driven Query Expansion for QA-Based Product Attribute Extraction. In: ACL. pp. 227–234 (2022)
2022
Cited alongside, same era.
Wang, Q., Yang, L., Wang, J., et al.: SMARTAVE: Structured Multimodal Transformer for Product Attribute Value Extraction. In: EMNLP. pp. 263 – 276 (2022)
Khorashadizadeh, H., Mihindukulasooriya, N., Tiwari, S., et al.: Exploring In-Context Learning Capabilities of Foundation Models for Generating Knowledge Graphs from Text. In: TEXT2KG | BiKE. vol. 3447, pp. 132–153 (2023)
2023
Closest in time.
OpenAI: GPT-4 Technical Report (2023), arXiv:2303.08774 [cs]
2023
Closest in time.
Parekh, T., Hsu, I.H., Huang, K.H., et al.: GENEVA: Benchmarking Generalizability for Event Argument Extraction with Hundreds of Event Types and Argument Roles. In: ACL. pp. 3664–3686 (2023)
2023
Closest in time.
Wang, X., Li, S., Ji, H.: Code4Struct: Code Generation for Few-Shot Event Structure Prediction. In: ACL. vol. 1, pp. 3640–3663 (2023)
2023
Closest in time.
Yang, L., Wang, Q., Wang, J., et al.: MixPAVE: Mix-Prompt Tuning for Few-shot Product Attribute Value Extraction. In: ACL. pp. 9978–9991 (2023)
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2022
Cited alongside, same era.
Wei, J., Tay, Y., Bommasani, R., et al.: Emergent Abilities of Large Language Models. TMLR (2022)
2022
Cited alongside, same era.
Yang, L., Wang, Q., Yu, Z., et al.: MAVE: A Product Dataset for Multi-source Attribute Value Extraction. In: WSDM. pp. 1256–1265 (2022)
2022
Cited alongside, same era.
Zhang, X., Zhang, C., Li, X., et al.: OA-Mine: Open-World Attribute Mining for E-Commerce Products with Weak Supervision. In: WWW. pp. 3153–3161 (2022)
2022
Cited alongside, same era.
Chen, W.T., Shinzato, K., Yoshinaga, N., et al.: Does Named Entity Recognition Truly Not Scale Up to Real-world Product Attribute Extraction? In: EMNLP. pp. 152–159 (2023)
2023
Cited alongside, same era.
Goel, A., Gueta, A., Gilon, O., et al.: LLMs Accelerate Annotation for Medical Information Extraction. In: ML4H. pp. 82–100 (2023)
2023
Cited alongside, same era.
2023
Closest in time.
Zamfirescu-Pereira, J., Wong, R.Y., Hartmann, B., et al.: Why Johnny Can’t Prompt: How Non-AI Experts Try (and Fail) to Design LLM Prompts. In: CHI. pp. 1–21 (2023)
2023
Closest in time.
Brinkmann, A., Baumann, N., Bizer, C.: Using LLMs for the Extraction and Normalization of Product Attribute Values. In: ADBIS. pp. 217–230 (2024)
2024
Closest in time.
Dubey, A., Jauhri, A., Pandey, A., et al.: The Llama 3 Herd of Models (2024), arXiv:2407.21783 [cs]
2024
Closest in time.
2024
Closest in time.