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Feature Description
A Part-of-Speech tagger using a Hidden Markov Model (HMM) assigns grammatical categories to words in a sentence based on learned probabilities. It predicts the most likely sequence of tags by considering both the current word and the previous tags.
Use Case
This POS tagger is essential in natural language processing for tasks like text analysis, speech recognition, and machine translation, where accurate tagging improves understanding and interpretation of language structure.
Benefits
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Add ScreenShots
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Priority
High
Record