Machine Learning in NLP
Traditional NLP relied heavily on rule-based approaches, but modern NLP incorporates machine learning techniques to improve accuracy and scalability. Machine learning models used in NLP can be categorized as follows:
Supervised Learning: Requires labeled data and is commonly used in text classification tasks such as spam detection, sentiment analysis, and entity recognition. Popular algorithms include Naïve Bayes, Support Vector Machines (SVM), and Random Forests.
Unsupervised Learning: Works with unlabeled data to find patterns and structures within text. Techniques such as topic modeling (e.g., Latent Dirichlet Allocation) and word clustering fall under this category.
Reinforcement Learning: Applied in NLP systems like chatbots and recommendation engines, where an agent learns optimal responses through user interactions and feedback.
Machine learning-based NLP models require large datasets, pre-processing pipelines, and feature engineering to extract meaningful insights from text. While traditional ML methods provide good baseline performance, they are increasingly being replaced by deep learning models.