Part-of-speech tagger for English natural language processing
Description
The module is a probability based, corpus-trained tagger that assigns POS
tags to English text based on a lookup dictionary and a set of probability
values. The tagger assigns appropriate tags based on conditional
probabilities - it examines the preceding tag to determine the appropriate
tag for the current word. Unknown words are classified according to word
morphology or can be set to be treated as nouns or other parts of speech.
