Yu Wu
ABSTRACT
Aiming at the problem that the traditional extraction method caused by the diversification of weapon attributes has a large amount of work to construct the label of weapon attributes, in this paper, we propose a weapon attribute value extraction method based on bidirectional long-term and short-term memory network (Bi-LSTM) and attention mechanism. The method first uses the Bi-LSTM model to extract the features of the input text and attribute names. Then, the attention mechanism focuses on the relations between words and attributes in the sentence. Afterward, the global BIO tag marks the position of the attribute values in the sentence. In this way, the method can reduce the workload during the corpus preparation period to improve the generalization ability of the model so that it can extract different weapon attribute data. Compared with Bi-LSTM, Bi-LSTM_CRF, and OpenTag from the experimental results, the F1 values of the proposed model on the weapon domain attribute dataset are increased by about 6.9%, 5.7%, and 2.5%, respectively.
- Jain M, Bhattacharya S, Jain H, Learning cross-task attribute-attribute similarity for multi-task attribute-value extraction[C]//Proceedings of The 4th Workshop on e-Commerce and NLP. 2021: 79-87.Google Scholar
- Embar V, Kan A, Sisman B, DiffXtract: Joint Discriminative Product Attribute-Value Extraction[C]//2021 IEEE International Conference on Big Knowledge (ICBK). IEEE, 2021: 271-280.Google Scholar
- Zheng G, Mukherjee S, Dong X L, Opentag: Open attribute value extraction from product profiles[C]//Proceedings of the 24th ACM SIGKDD international conference on knowledge discovery & data mining. 2018: 1049-1058.Google Scholar
- Wang Q, Yang L, Kanagal B, Learning to Extract Attribute Value from Product via Question Answering: A Multi-task Approach[C]//Proceedings of the 26th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining. New York, NY, USA: Association for Computing Machinery, 2020: 47-55.Google Scholar
- Roy K, Goyal P, Pandey M. Attribute value generation from product title using language models[C]//Proceedings of The 4th Workshop on e-Commerce and NLP. 2021: 13-17.Google Scholar
- Yan J, Zalmout N, Liang Y, AdaTag: Multi-Attribute Value Extraction from Product Profiles with Adaptive Decoding[C]//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). Association for Computational Linguistics, 2021: 4694-4705.Google Scholar
- Xu H, Wang W, Mao X, Scaling up Open Tagging from Tens to Thousands: Comprehension Empowered Attribute Value Extraction from Product Title[C]//Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics. Association for Computational Linguistics, 2019: 5214-5223.Google Scholar
- Qi G, Gao H, Wu T. Research progress of knowledge graph[J]. Information Engineering, 2017,3(1): 4-25.Google Scholar
- Luo H, Li T, Liu B, Improving Aspect Term Extraction with Bidirectional Dependency Tree Representation[J]. IEEE/ACM Transactions on Audio, Speech, and Language Processing, 2019: 1201-1212.Google Scholar
- Li C, Zhao Z, Li C, Product attribute extraction method based on dependency relation embedding and conditional random field [J]. Data analysis and knowledge discovery, 2020,4(5): 54-65.Google Scholar
- Zhao G, Zhang T, Wang C, Applications of BERT Based Sequence Tagging Models on Chinese Medical Text Attributes Extraction[J]. arXiv preprint arXiv:2008.09740, 2020.Google Scholar
- Feng A, Liu J, Jiang H, Attribute value extraction method based on machine reading comprehension model and crowdsourcing verification [J]. Computer Engineering, 2021 (5): 97-193.Google Scholar
- Liu Y, Zhang S, Song R, Knowledge-guided Open Attribute Value Extraction with Reinforcement Learning[C]//Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP). Association for Computational Linguistics, 2020: 8595-8604.Google Scholar
- Huang Z, Xu W, Yu K. Bidirectional LSTM-CRF Models for Sequence Tagging[J]. arXiv preprint arXiv:1508.01991, 2015.Google Scholar
Recommendations
Fusing Attribute Type Features for Attribute Value Extraction from Product via Question Answering
MLNLP '22: Proceedings of the 2022 5th International Conference on Machine Learning and Natural Language ProcessingExtracting 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 ...
Simultaneous Product Attribute Name and Value Extraction from Web Pages
WI-IAT '09: Proceedings of the 2009 IEEE/WIC/ACM International Joint Conference on Web Intelligence and Intelligent Agent Technology - Volume 03Much work has been done in the area of template independent web data extraction. However, these approaches deal with the attribute value extraction and annotation either in separate phases or constrained to a predefined set of attributes which is highly ...
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 ...
Comments