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

arXiv: https://arxiv.org/abs/2312.05103 · HTML: https://mlsanity.com/paper/2312.05103/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 9 of its 20 references that are in the map.
- First posted on arXiv 8 Dec 2023.

## Builds on (most cited first, 9 of 9)

- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [LoRA: Low-Rank Adaptation of Large Language Models](https://mlsanity.com/paper/2106.09685/) (2021)
- [Recent Advances in Convolutional Neural Networks](https://mlsanity.com/paper/1512.07108/) (2015)
- [REFINER: Reasoning Feedback on Intermediate Representations](https://mlsanity.com/paper/2304.01904/) (2023)
- [Large Language Models Can be Lazy Learners: Analyze Shortcuts in In-Context Learning](https://mlsanity.com/paper/2305.17256/) (2023)
- [Natural Language Processing in the Legal Domain](https://mlsanity.com/paper/2302.12039/) (2023)
- [LogoDet-3K: A Large-Scale Image Dataset for Logo Detection](https://mlsanity.com/paper/2008.05359/) (2020)
- [FoodLogoDet-1500: A Dataset for Large-Scale Food Logo Detection via Multi-Scale Feature Decoupling Network](https://mlsanity.com/paper/2108.04644/) (2021)
- [Component-based Attention for Large-scale Trademark Retrieval](https://mlsanity.com/paper/1811.02746/) (2018)

## Built on by (most cited first, 1 of 1)

- [A Survey on the Integration of Generative AI for Critical Thinking in Mobile Networks](https://mlsanity.com/paper/2404.06946/) (2024)

## Abstract (arXiv)

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 .