TMID: A Comprehensive Real-world Dataset for Trademark Infringement Detection in E-Commerce
This addresses the problem of financial losses from trademark infringements for e-commerce platforms by providing a foundational dataset, though it is incremental as it focuses on data creation rather than novel detection methods.
The study tackled the lack of high-quality datasets for trademark infringement detection in e-commerce by introducing TMID, a real-world dataset sourced from Alipay, which includes legal rules and expert annotations to support research in this area.
Annually, e-commerce platforms incur substantial financial losses due to trademark infringements, making it crucial to identify and mitigate potential legal risks tied to merchant information registered to the platforms. However, the absence of high-quality datasets hampers research in this area. To address this gap, our study introduces TMID, a novel dataset to detect trademark infringement in merchant registrations. This is a real-world dataset sourced directly from Alipay, one of the world's largest e-commerce and digital payment platforms. As infringement detection is a legal reasoning task requiring an understanding of the contexts and legal rules, we offer a thorough collection of legal rules and merchant and trademark-related contextual information with annotations from legal experts. We ensure the data quality by performing an extensive statistical analysis. Furthermore, we conduct an empirical study on this dataset to highlight its value and the key challenges. Through this study, we aim to contribute valuable resources to advance research into legal compliance related to trademark infringement within the e-commerce sphere. The dataset is available at https://github.com/emnlpTMID/emnlpTMID.github.io .