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Annually, e-commerce platforms incur substantial financial losses due totrademark infringements, making it crucial to identify and mitigate potentiallegal risks tied to merchant information registered to the platforms. However,the absence of high-quality datasets hampers research in this area. To addressthis gap, our study introduces TMID, a novel dataset to detect trademarkinfringement in merchant registrations. This is a real-world dataset sourceddirectly from Alipay, one of the world's largest e-commerce and digital paymentplatforms. As infringement detection is a legal reasoning task requiring anunderstanding of the contexts and legal rules, we offer a thorough collectionof legal rules and merchant and trademark-related contextual information withannotations from legal experts. We ensure the data quality by performing anextensive statistical analysis. Furthermore, we conduct an empirical study onthis dataset to highlight its value and the key challenges. Through this study,we aim to contribute valuable resources to advance research into legalcompliance related to trademark infringement within the e-commerce sphere. Thedataset is available at https://github.com/emnlpTMID/emnlpTMID.github.io .

TMID: A Comprehensive Real-world Dataset for Trademark Infringement Detection in E-Commerce