Fusing Attribute Type Features for Attribute Value Extraction from Product via Question Answering
Authors: 
Miaobo Hu, Jun Xiao, Yunfei Liu, Weihu Guo, Xipeng FanAuthors Info & Claims
Miaobo Hu, Jun Xiao, Yunfei Liu, Weihu Guo, Xipeng FanAuthors Info & ClaimsPages 179 - 184
Abstract
Extracting attribute values from product titles is a crucial e-commerce task. Previous attribute extraction method was insufficient because the exist dataset lacked attribute type information. To overcome these obstacles and promote product attribute research, we first improve the AliExpress dataset with appending entity type information to better facilitate research on product attribute value extraction. Then, we propose an Attribute Type Attention via Question Answering (ATAQA) approach to fully utilize the attribute type information for attribute value extraction, which captures semantic interaction of attribute and attribute type with a sentence and get the vectors containing attributes, attribute types and sentence features. We combine the attribute type vector and the attribute vector to get the processed vector, and then we use the self-attention layer connect the attribute vector and the processed vector, finally export a sentence-attribute-comprehension representation to the CRF for final tagging. The proposed approach outperforms previous best methods by a significant margin, as shown by the experimental results. Our Data is available at https://github.com/wandugu/AE-improved.
References
[1]
Ahuja, A., Rao, N., Katariya, S., Subbian, K. and Reddy, C.K. 2020. Language-agnostic representation learning for product search on e-commerce platforms. (2020), 7–15.
[2]
Ai, Q., Zhang, Y., Bi, K., Chen, X. and Croft, W.B. 2017. Learning a hierarchical embedding model for personalized product search. (2017), 645–654.
[3]
Carmel, D., Lewin-Eytan, L. and Maarek, Y. 2018. Product question answering using customer generated content-research challenges. (2018), 1349–1350.
[4]
Chen, K., Feng, L., Chen, Q., Chen, G. and Shou, L. 2019. EXACT: Attributed Entity Extraction By Annotating Texts. Proceedings of the 42nd International ACM SIGIR Conference on Research and Development in Information Retrieval (Paris France, Jul. 2019), 1349–1352.
[5]
Chiticariu, L., Krishnamurthy, R., Li, Y., Reiss, F. and Vaithyanathan, S. 2010. Domain adaptation of rule-based annotators for named-entity recognition tasks. (2010), 1002–1012.
[6]
Dirie, A.-H.A. 2017. Extracting diverse attribute-value information from product catalog text via transfer learning. (2017).
[7]
Gao, S., Ren, Z., Zhao, Y., Zhao, D., Yin, D. and Yan, R. 2019. Product-aware answer generation in e-commerce question-answering. (2019), 429–437.
[8]
Ghani, R., Probst, K., Liu, Y., Krema, M. and Fano, A. 2006. Text mining for product attribute extraction. ACM SIGKDD Explorations Newsletter. 8, 1 (Jun. 2006), 41–48.
[9]
Gopalakrishnan, V., Iyengar, S.P., Madaan, A., Rastogi, R. and Sengamedu, S. 2012. Matching product titles using web-based enrichment. (2012), 605–614.
[10]
Hochreiter, S. and Schmidhuber, J. 1997. Long short-term memory. Neural computation. 9, 8 (1997), 1735–1780.
[11]
Huang, Z., Xu, W. and Yu, K. 2015. Bidirectional LSTM-CRF models for sequence tagging. arXiv preprint arXiv:1508.01991. (2015).
[12]
Ishwari, K., Aneeze, A., Sudheesan, S., Karunaratne, H., Nugaliyadde, A. and Mallawarrachchi, Y. 2019. Advances in natural language question answering: A review. arXiv preprint arXiv:1904.05276. (2019).
[13]
IV, R.L.L., Humeau, S. and Singh, S. 2017. Multimodal Attribute Extraction. (2017).
[14]
Jain, M., Bhattacharya, S., Jain, H., Shaik, K. and Chelliah, M. 2021. Learning cross-task attribute-attribute similarity for multi-task attribute-value extraction. (2021), 79–87.
[15]
Karamanolakis, G., Ma, J. and Dong, X. 2020. TXtract: Taxonomy-Aware Knowledge Extraction for Thousands of Product Categories. (2020).
[16]
Kenton, J.D.M.-W.C. and Toutanova, L.K. 2019. BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding. (2019), 4171–4186.
[17]
Kodra, L. and Meçe, E.K. 2017. Question answering systems: A review on present developments, challenges and trends. International Journal of Advanced Computer Science and Applications. 8, 9 (2017).
[18]
Kozareva, Z., Li, Q., Zhai, K. and Guo, W. 2016. Recognizing salient entities in shopping queries. (2016), 107–111.
[19]
Le, T.-H. and Lauw, H.W. 2021. Explainable recommendation with comparative constraints on product aspects. (2021), 967–975.
[20]
Li, J., Fei, H., Liu, J., Wu, S., Zhang, M., Teng, C., Ji, D. and Li, F. 2021. Unified Named Entity Recognition as Word-Word Relation Classification. arXiv.
[21]
Lin, R., He, X., Feng, J., Zalmout, N., Liang, Y., Xiong, L. and Dong, X. 2021. PAM: Understanding Product Images in Cross Product Category Attribute Extraction. (2021).
[22]
Liu, Y., Zhang, S., Song, R., Feng, S. and Xiao, Y. 2020. Knowledge-guided Open Attribute Value Extraction with Reinforcement Learning. Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP) (Online, 2020), 8595–8604.
[23]
Martinez-Gomez, P., Papachristoudis, G., Blauvelt, J., Rachlin, E. and Simhon, S. 2021. Enhancement and analysis of tars few-shot learning model for product attribute extraction from unstructured text. (2021).
[24]
Meng, Z., Liang, S., Fang, J. and Xiao, T. 2019. Semi-supervisedly co-embedding attributed networks. Advances in neural information processing systems. 32, (2019).
[25]
Mikheev, A., Moens, M. and Grover, C. 1999. Named entity recognition without gazetteers. (1999), 1–8.
[26]
Mikolov, T., Sutskever, I., Chen, K., Corrado, G.S. and Dean, J. 2013. Distributed representations of words and phrases and their compositionality. Advances in neural information processing systems. 26, (2013).
[27]
Nadeau, D. and Sekine, S. 2007. A survey of named entity recognition and classification. Lingvisticae Investigationes. 30, 1 (2007), 3–26.
[28]
Pan, G., Yao, Y., Tong, H., Xu, F. and Lu, J. 2021. Unsupervised attributed network embedding via cross fusion. (2021), 797–805.
[29]
Probst, K., Ghani, R., Krema, M., Fano, A.E. and Liu, Y. 2007. Semi-Supervised Learning of Attribute-Value Pairs from Product Descriptions. (2007), 2838–2843.
[30]
Rajpurkar, P., Zhang, J., Lopyrev, K. and Liang, P. 2016. SQuAD: 100, 000+ Questions for Machine Comprehension of Text. (2016).
[31]
Rezk, M., Alemany, L.A., Nio, L. and Zhang, T. 2019. Accurate Product Attribute Extraction on the Field. (2019).
[32]
Roy, K., Goyal, P. and Pandey, M. 2021. Attribute Value Generation from Product Title using Language Models. (2021).
[33]
Shinzato, K. and Sekine, S. 2013. Unsupervised extraction of attributes and their values from product description. (2013), 1339–1347.
[34]
Sun, C., Liu, H., Liu, M., Ren, Z., Gan, T. and Nie, L. 2020. LARA: attribute-to-feature adversarial learning for new-item recommendation. (2020), 582–590.
[35]
Vandic, D., Van Dam, J.-W. and Frasincar, F. 2012. Faceted product search powered by the semantic web. Decision Support Systems. 53, 3 (2012), 425–437.
[36]
Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A.N., Kaiser, Ł. and Polosukhin, I. 2017. Attention is all you need. Advances in neural information processing systems. 30, (2017).
[37]
Wang, Q., Yang, L., Kanagal, B., Sanghai, S., Sivakumar, D., Shu, B., Yu, Z. and Elsas, J. 2020. Learning to Extract Attribute Value from Product via Question Answering: A Multi-task Approach. Proceedings of the 26th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining (Virtual Event CA USA, Aug. 2020), 47–55.
[38]
Xu, H., Wang, W., Mao, X., Jiang, X. and Lan, M. 2019. Scaling up Open Tagging from Tens to Thousands: Comprehension Empowered Attribute Value Extraction from Product Title. Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics (Florence, Italy, 2019), 5214–5223.
[39]
Yan, J., Zalmout, N., Liang, Y., Grant, C., Ren, X. and Dong, X.L. 2021. AdaTag: Multi-Attribute Value Extraction from Product Profiles with Adaptive Decoding. Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers) (Online, 2021), 4694–4705.
[40]
Yang, L., Wang, Q., Yu, Z., Kulkarni, A., Sanghai, S., Shu, B., Elsas, J. and Kanagal, B. 2021. MAVE: A Product Dataset for Multi-source Attribute Value Extraction. arXiv.
[41]
Zhao, J., Guan, Z. and Sun, H. 2019. Riker: Mining rich keyword representations for interpretable product question answering. (2019), 1389–1398.
[42]
Zheng, G., Mukherjee, S., Dong, X.L. and Li, F. 2018. OpenTag: Open Attribute Value Extraction from Product Profiles [Deep Learning, Active Learning, Named Entity Recognition]. Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining (Jul. 2018), 1049–1058.
[43]
Zhu, T., Wang, Y., Li, H., Wu, Y., He, X. and Zhou, B. 2020. Multimodal Joint Attribute Prediction and Value Extraction for E-commerce Product. Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP) (Online, 2020), 2129–2139.
Index Terms
- Fusing Attribute Type Features for Attribute Value Extraction from Product via Question Answering
Index terms have been assigned to the content through auto-classification.
Recommendations
MAVE: A Product Dataset for Multi-source Attribute Value Extraction
WSDM '22: Proceedings of the Fifteenth ACM International Conference on Web Search and Data MiningAttribute value extraction refers to the task of identifying values of an attribute of interest from product information. Product attribute values are essential in many e-commerce scenarios, such as customer service robots, product ranking, retrieval ...
Learning to Extract Attribute Value from Product via Question Answering: A Multi-task Approach
KDD '20: Proceedings of the 26th ACM SIGKDD International Conference on Knowledge Discovery & Data MiningAttribute value extraction refers to the task of identifying values of an attribute of interest from product information. It is an important research topic which has been widely studied in e-Commerce and relation learning. There are two main limitations ...
CAVE: Correcting Attribute Values in E-commerce Profiles
CIKM '22: Proceedings of the 31st ACM International Conference on Information & Knowledge ManagementAttribute value extraction from product profiles is essential for many applications such as product retrieval, comparison, and recommendation. While existing techniques focus mainly on the extraction task, none of them deals with the problem of ...
Comments
Information & Contributors
Information
Published In
December 2022
406 pages
ISBN:9781450399067
DOI:10.1145/3578741
Copyright � 2022 Owner/Author.
This work is licensed under a Creative Commons Attribution-NonCommercial International 4.0 License.
Publisher
Association for Computing Machinery
New York, NY, United States
Publication History
Published: 06 March 2023
Check for updates
Author Tags
Qualifiers
- Research-article
- Research
- Refereed limited
Conference
MLNLP 2022
MLNLP 2022: 2022 5th International Conference on Machine Learning and Natural Language Processing
December 23 - 25, 2022
Sanya, China
Contributors
Other Metrics
Bibliometrics & Citations
Bibliometrics
Article Metrics
- 0Total Citations
- 246Total Downloads
- Downloads (Last 12 months)176
- Downloads (Last 6 weeks)20
Reflects downloads up to 17 Oct 2024
Other Metrics
Citations
Cited By
View allView Options
View options
View or Download as a PDF file.
PDFeReader
View online with eReader.
eReaderHTML Format
View this article in HTML Format.
HTML FormatGet Access
Login options
Check if you have access through your login credentials or your institution to get full access on this article.
Sign in