文献类型: 外文期刊
作者: Mao, Dianhui 1 ; Liu, Yiming 1 ; Li, Ruixuan 1 ; Chen, Junhua 2 ; Hao, Yuanrong 3 ; Wu, Jianwei 4 ;
作者机构: 1.Beijing Technol & Business Univ, Beijing Key Lab Big Data Technol Food Safety, Beijing 100048, Peoples R China
2.Sub Inst Standardizat Theory & Strategy, China Natl Inst Standardizat CNIS, Beijing 100088, Peoples R China
3.Minist Justice Peoples Republ China, Law Promot Ctr, Beijing 100020, Peoples R China
4.Beijing Acad Agr & Forestry Sci, Res Ctr Informat Technol, Beijing 100097, Peoples R China
5.Beijing PAIDE Sci & Technol Dev Co Ltd, Beijing 100097, Peoples R China
关键词: Event Extraction; Ontology construction; Knowledge Graph; Food e -commerce live streaming
期刊名称:ELECTRONIC COMMERCE RESEARCH AND APPLICATIONS ( 影响因子:5.9; 五年影响因子:6.9 )
ISSN: 1567-4223
年卷期: 2024 年 66 卷
页码:
收录情况: SCI
摘要: In the evolving landscape of food e-commerce live streaming, the profusion of textual data, marked by an excess of promotional vernacular and unstructured formats, presents a formidable challenge for event extraction. Addressing these hurdles, we introduce a tailored ontology-based method alongside FMLEE (Food Marketing Live Event Extraction), a joint event extraction algorithm. This approach simplifies the event identification process through meticulous segmentation and the development of an ontology comprising 5 event categories and 19 argument roles. By integrating context-aware embeddings derived from pre-trained language models and applying an adversarial learning tactic, our methodology not only bolsters the robustness of our model but also significantly refines its accuracy in discerning relevant events within the scarce-resource milieu of food live streaming promotions. The effectiveness of the FMLEE model is validated by its achievement of an F1 score of 73.05%, with the inclusion of adversarial learning contributing to a 2.61% enhancement in performance. This evidences our novel contribution to the domain, offering robust technical support for the optimal exploitation of information within the sphere of food live streaming promotions. Simultaneously, this aids in the investigation of innovative applications for consumer engagement within marketing strategies and the smart regulation of marketing activities.
- 相关文献
作者其他论文 更多>>
-
Computer Vision-Based Measurement Techniques for Livestock Body Dimension and Weight: A Review
作者:Ma, Weihong;Qi, Xiangyu;Sun, Yi;Gao, Ronghua;Ding, Luyu;Wang, Rong;Peng, Cheng;Zhang, Jun;Wu, Jianwei;Xu, Zhankang;Li, Mingyu;Huang, Shudong;Li, Qifeng;Qi, Xiangyu;Zhao, Hongyan;Huang, Shudong
关键词:3D reconstruction; stressless body dimension measurement; visual weight estimation; precision livestock farming
-
Maize seed fraud detection based on hyperspectral imaging and one-class learning
作者:Zhang, Liu;Wei, Yaoguang;Liu, Jincun;An, Dong;Zhang, Liu;Wei, Yaoguang;Liu, Jincun;An, Dong;Zhang, Liu;Wei, Yaoguang;Liu, Jincun;An, Dong;Zhang, Liu;Wei, Yaoguang;Liu, Jincun;An, Dong;Wu, Jianwei;Wu, Jianwei
关键词:Fraud detection; Maize seeds; Hyperspectral imaging; One -class learning; Deep learning
-
A hyperspectral band selection method based on sparse band attention network for maize seed variety identification
作者:Zhang, Liu;Wei, Yaoguang;Liu, Jincun;An, Dong;Zhang, Liu;Wei, Yaoguang;Liu, Jincun;An, Dong;Zhang, Liu;Wei, Yaoguang;Liu, Jincun;An, Dong;Zhang, Liu;Wei, Yaoguang;Liu, Jincun;An, Dong;Wu, Jianwei;Wu, Jianwei
关键词:Hyperspectral imaging; Band selection; Attention mechanism; Deep learning; Seed variety identification
-
Maize seed variety identification using hyperspectral imaging and self-supervised learning: A two-stage training approach without spectral preprocessing
作者:Zhang, Liu;Zhang, Shubin;Liu, Jincun;Wei, Yaoguang;An, Dong;Zhang, Liu;Zhang, Shubin;Liu, Jincun;Wei, Yaoguang;An, Dong;Zhang, Liu;Zhang, Shubin;Liu, Jincun;Wei, Yaoguang;An, Dong;Zhang, Liu;Zhang, Shubin;Liu, Jincun;Wei, Yaoguang;An, Dong;Wu, Jianwei;Wu, Jianwei
关键词:Seed classification; Hyperspectral imaging; Self-supervised learning; Deep learning; Spectral analysis
-
Using filter pruning-based deep learning algorithm for the real-time fruit freshness detection with edge processors
作者:Mao, DianHui;Zhang, DengHui;Sun, Hao;Mao, DianHui;Wu, JianWei;Wu, JianWei;Chen, JunHua
关键词:PP-YOLO Tiny; Ultra Lightweight; FPGM algorithm; Real-time detection; Fruit
-
Open set maize seed variety classification using hyperspectral imaging coupled with a dual deep SVDD-based incremental learning framework
作者:Zhang, Liu;Huang, Jinze;Wei, Yaoguang;Liu, Jincun;An, Dong;Zhang, Liu;Huang, Jinze;Wei, Yaoguang;Liu, Jincun;An, Dong;Zhang, Liu;Huang, Jinze;Wei, Yaoguang;Liu, Jincun;An, Dong;Zhang, Liu;Huang, Jinze;Wei, Yaoguang;Liu, Jincun;An, Dong;Wu, Jianwei;Wu, Jianwei
关键词:Seed classification; Hyperspectral imaging; Open set recognition; Incremental learning; Deep learning
-
Non-destructive and in-situ detection of shrimp freshness using mid-infrared fiber-optic evanescent wave spectroscopy
作者:Zhou, Yunhai;Jiao, Leizi;Zhang, Yunhe;Dong, Daming;Zhou, Yunhai;Jiao, Leizi;Zhu, Qingzhen;Dong, Daming;Wu, Jianwei
关键词:Fiber-optic evanescent wave spectroscopy; Shrimp; Freshness; Non-destructive; In -situ