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Named entity recognition (NER) systems have seen rapid progress in recentyears due to the development of deep neural networks. These systems are widelyused in various natural language processing applications, such as informationextraction, question answering, and sentiment analysis. However, the complexityand intractability of deep neural networks can make NER systems unreliable incertain circumstances, resulting in incorrect predictions. For example, NERsystems may misidentify female names as chemicals or fail to recognize thenames of minority groups, leading to user dissatisfaction. To tackle thisproblem, we introduce TIN, a novel, widely applicable approach forautomatically testing and repairing various NER systems. The key idea forautomated testing is that the NER predictions of the same named entities undersimilar contexts should be identical. The core idea for automated repairing isthat similar named entities should have the same NER prediction under the samecontext. We use TIN to test two SOTA NER models and two commercial NER APIs,i.e., Azure NER and AWS NER. We manually verify 784 of the suspicious issuesreported by TIN and find that 702 are erroneous issues, leading to highprecision (85.0%-93.4%) across four categories of NER errors: omission,over-labeling, incorrect category, and range error. For automated repairing,TIN achieves a high error reduction rate (26.8%-50.6%) over the four systemsunder test, which successfully repairs 1,056 out of the 1,877 reported NERerrors.

Automated Testing and Improvement of Named Entity Recognition Systems