Beyond Accuracy: Robustness Testing of Named Entity Recognition Models with LangTest

Often in Natural Language Processing (NLP), we often judge a model’s success based on how accurate it is. But this approach can be misleading because it doesn’t show how well the model handles real-world language. To truly know if a model is good, we need to test its robustness — how well it deals with different kinds of changes in the text it’s given. This is where Robustness Testing comes in. It’s like giving the model challenges to see if it can handle them. Imagine a ship sailing through rough waters — that’s the kind of test we’re talking about for NLP models. In this blog, we’ll dive into <a href="https://langtest.org/" rel="noopener ugc nofollow" target="_blank">LangTest</a>, a way to go beyond just accuracy and explore how well Named Entity Recognition models can handle the twists and turns of real language out there. In addition, the article delves into the implementation details of the med7 and ner posology models and demonstrates their usage. We showcase the evaluation of model performance using LangTest’s features, conducting tests on both models for Robustness and Accuracy. Finally, we compare the performance of these models, providing valuable insights into their strengths and weaknesses in healthcare NER tasks. <h1>What is Named Entity Recognition?</h1> Named Entity Recognition (NER) is a natural language processing (NLP) technique that involves identifying and classifying named entities within text. Named entities are specific pieces of information such as names of people, places, organizations, dates, times, quantities, monetary values, percentages, and more. NER plays a crucial role in understanding and extracting meaningful information from unstructured text data. <a href="https://medium.com/john-snow-labs/beyond-accuracy-robustness-testing-of-named-entity-recognition-models-with-langtest-fb046ace7eb9">Read More</a>