Sequence Labeling Algorithms

Sequence Labeling Algorithms: Sequence labeling is a type of pattern recognition task that involves the algorithmic assignment of a categorical label to each member of a sequence of observed values. A common example of a sequence labeling task is part of speech tagging, which seeks to assign a part of speech to each word in an input sentence or document. These types of algorithms predicting sequence of categorical labels and real valued labels. These algorithms support both supervised and un-supervised learning.

Supervised Learning for categorical labels: Hidden Markov Model (HMM), Maximum Entropy Markov
Model (MEMM) & Conditional Random Fields (CRF)

• Unsupervised learning for categorical labels: Hidden Markov Model (HMM)
• Supervised learning for real valued labels: Kalman Filters & Particle Filters
   

Parsing Algorithms: Parsing, or, more formally, syntactic analysis, is the process of analyzing a text, made of a sequence of tokens (for example, words), to determine its grammatical structure with respect to a given (more or less) formal
grammar.
The algorithm supports both supervised & unsupervised learning and predicting tree structured labels.

Example: Probabilistic Context Free Grammars (PCFG)