Named Entity Recognition Testing

We provide the data and code for NER testing and NER repairing of TIN, which are in the directory and TIN_Test and TIN_Repair, respectively.

Environment

python == 3.7
pytorch == 1.7.1
Transformers == 3.3.0
nltk
stanfordcorenlp

Get start

We provide tutorial for users about the usage of NER testing and NER repairing, which are in the corresponding directory, TIN_Test/README.md, TIN_Repair/README.md.

Performance of our NER repair Toolkit:

Our Toolkit can reduce 42.6% of the NER errors on AWS NER system, and reduce 50.6% of the NER errors on Azure NER system. Examples of using TIN to detect the NER errors and then fix them are shown as below:

Citation

🔭:If you use any tools or datasets in this project, please kindly cite the following paper:

• Boxi Yu, Yiyan Hu, Qiuyang Mang, Wenhan Hu, Pinjia He. [ESEC/FSE'23] ACM Joint Meeting on European Software Engineering Conference and Symposium on the Foundations of Software Engineering (ESEC/FSE)*, 2023.

Feedback

Should you have any questions, please post to the issue page, or email Boxi Yu via [email protected].