What does "Low-resource Domains" mean?
Table of Contents
Low-resource domains refer to areas of study or industries where there is not enough data or resources available. This is common in specialized fields like medicine, law, and finance. In these domains, it can be hard to find enough examples or information to train machine learning models effectively.
Challenges
Because there is limited data, it becomes difficult to build accurate systems for tasks such as recognizing specific names or classifying images. Many models rely on having a lot of examples to learn from, so working in low-resource areas presents unique challenges.
Data Augmentation
To tackle these challenges, researchers use data augmentation techniques. These methods create more training examples from existing data. For instance, they might change words in a sentence or replace specific mentions to help improve the model’s learning process.
Applications
Low-resource domains can benefit from various applications, such as named entity recognition (NER) and image classification. By using smart approaches, it’s possible to get better results even when starting with limited information. This means that systems can work effectively in areas that lack a lot of resources.