TMID: A Comprehensive Real-world Dataset for Trademark Infringement
  Detection in E-Commerce
Tongxin Hu Zhuang Li Xin Jin Lizhen Qu Xin Zhang
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 .