Python Natural Language Processing

Semi-supervised ML

Semi-supervised ML or semi-supervised learning (SSL) is basically used when you have a training dataset that has a target concept or target label for some data in the dataset, and the other part of the data doesn't have any label. If you have this kind of dataset, then you can apply semi-supervised ML algorithms. When we have a very small amount of labeled data and a lot of unlabeled data, then we can use semi-supervised techniques. If you want to build an NLP tool for any local language (apart from English) and you have a very small amount of labeled data, then you can use the semi-supervised approach. In this approach, we will use a classifier that uses the labeled data and generates an ML-model. This ML-model is used ...