Building Conditional Random Fields for sequential text data
The Conditional Random Fields (CRFs) are probabilistic models used to analyze structured data. They are frequently used to label and segment sequential data. CRFs are discriminative models as opposed to HMMs, which are generative models. CRFs are used extensively to analyze sequences, stocks, speech, words, and so on. In these models, given a particular labeled observation sequence, we define a conditional probability distribution over this sequence. This is in contrast with HMMs where we define a joint distribution over the label and the observed sequence.
Getting ready
HMMs assume that the current output is statistically independent of the previous outputs. This is needed by HMMs to ensure that the inference works in a robust way. However, this assumption need not always be true! The current output in a time series setup, more often than not, depends on previous outputs. One of the main advantages of CRFs over HMMs is that they...
