The process of data annotation is a fundamental challenge for artificial intelligence, as it is well-known that neural networks require a large number of accurately labeled training examples to learn effectively. This bottleneck limits the applicability of deep learning to many domains. In order to address this challenge, a number of new approaches have been proposed to automate or reduce the cost of data annotation.
One approach is to make use of existing annotated data sources. For example, the ImageNet dataset contains over 14 million annotated images, which can be employed to train deep neural networks for image classification. Similarly, there are a number of datasets for natural language processing tasks such as part-of-speech tagging and named entity recognition. Although these datasets are not usually as large as those for computer vision tasks, they can still be used to train models that achieve state-of-the-art performance on various tasks.
Another approach is to use weakly supervised methods to learn from data that is not fully annotated. For example,Bootstrapping for Data Annotation (BDA) is a technique that can be used to automatically generate training data for text classification tasks. BDA consists of two steps: first, a small number of seed instances are manually annotated; second, a classifier is trained on the seed data and then used to label a large number of unannotated instances. The generated labels are then used to train a second classifier, which is used to label even more unannotated instances, and so on. This process can be repeated until the classifier reaches the desired accuracy level.
A third approach is to make use of active learning, which is a technique that can be employed to minimize the amount of manual annotation required. Active learning algorithms select the instances that are most informative for the task at hand and present them to the annotator for labeling. The idea is that, by carefully selecting which instances to annotate, it is possible to achieve the same performance as if all instances were annotated, but with far less effort. Active learning has been shown to be particularly effective for tasks such as image classification, where a small number of labels can have a large impact on performance.
Finally, it is worth noting that there are a number of commercial services that offer data annotation services. These companies typically have a large team of annotators who can label data quickly and accurately. Although these services might be expensive, they can be a good option for companies that need to annotate a large amount of data.
References:
http://www.image-net.org/
https://nlp.stanford.edu/projects/glove/
https://machinelearningmastery.com/gentle-introduction-bootstrapping-machine-learning/
https://en.wikipedia.org/wiki/Active_learning_(machine_learning)