Words Types NLP algorithms.

Support Vector Machines (SVM): SVM is a supervised machine learning algorithm that can be applied to various NLP tasks, including text classification and sentiment analysis.

Bayesian Networks: Bayesian Networks are probabilistic graphical models that can be used for tasks such as document classification, topic modeling, and information extraction.

Maximum Entropy (MaxEnt): Maximum Entropy models are widely used in NLP for tasks like part-of-speech tagging, named entity recognition, and information extraction.

Conditional Random Fields (CRF): CRF models are sequence labeling algorithms that have been successfully applied to tasks such as named entity recognition, chunking, and part-of-speech tagging.

Neural Networks/Deep Learning: Deep learning techniques, particularly recurrent neural networks (RNNs) and convolutional neural networks (CNNs), have gained significant popularity in NLP. They have been successful in various tasks like machine translation, sentiment analysis, and language generation.