In computational linguistics and NLP, understanding the distinction between supervised and unsupervised learning is crucial. Supervised learning algorithms rely on labeled ...
In computational linguistics and NLP, understanding the distinction between supervised and unsupervised learning is crucial. Supervised learning algorithms rely on labeled datasets to learn the mapping from input features to output labels. This approach is commonly used in tasks like text classification, sentiment analysis, and named entity recognition, where the model is trained on examples with known outcomes.
On the other hand, unsupervised learning algorithms work with unlabeled data and aim to uncover hidden patterns or structures within the data. This method is often used for tasks such as topic modeling, clustering, and word embeddings, where the goal is to explore and organize data without predefined categories.
Discussing these differences helps in understanding the applicability and limitations of each learning approach in various NLP tasks. Can you provide specific examples of how each type of learning is applied in NLP?