This project builds a Part-of-Speech (POS) tagger using a Hidden Markov Model (HMM). The POS tagger labels each word in a sentence with its grammatical role, such as noun, verb, or adjective. This is an essential step in enabling a digital assistant, like "Sudi," to understand and process natural language.
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Training the Model:
- The model is trained on a labeled dataset where each word in a sentence is tagged with its corresponding part of speech.
- The training process counts the frequency of transitions between tags (e.g., from noun to verb) and the frequency of words associated with each tag.
- These frequencies are converted into probabilities, which the model uses to determine the most likely sequence of tags for a given sentence.
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Viterbi Algorithm:
- The Viterbi algorithm is used to find the most likely sequence of POS tags for a new sentence.
- It works by calculating the probabilities of different tag sequences and selecting the one with the highest probability.
- The algorithm tracks the best paths through the model to determine the correct tags based on the context of the sentence.
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Tagging Sentences:
- Once trained, the model can be used to tag new sentences.
- The input is a sentence, and the output is the same sentence with each word labeled with its corresponding POS tag.