POS Tagging Process Flow
This MicroSim traces how a part-of-speech (POS) tagger assigns a grammatical tag to every word in a sentence. It uses a trained statistical model, processes words left-to-right so it can use context, and falls back to heuristics for words it has never seen. Hover any step to read what happens there.
About This Diagram
The flowchart follows one word at a time around a loop. Color tells you what kind of work each step does: blue for input and output, green for probability calculations, yellow for decisions, and purple for unknown-word handling.
Interactive Demo
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How It Works
- Input a tokenized sentence such as
[Can, you, show, sales, ?]. - Initialize by loading the trained model: emission probabilities
P(tag | word)and transition probabilitiesP(tag | previous_tags). - For each word, look the word up in the vocabulary.
- Decision - word known? If yes, use the trained probabilities (Viterbi); if no, apply unknown-word heuristics based on capitalization and suffixes.
- Assign the most probable tag given the word and the context history.
- Decision - more words? Loop back if tokens remain; otherwise return the tagged sequence and hand it to the parser.
The example panel shows how the ambiguous word "show" is resolved: the model
weighs P(VB|show)=0.65 against P(NN|show)=0.35, and the modal context
"Can you ___" pushes the choice toward the verb tag VB.
Lesson Plan
- Walk the loop. Have students follow one word at a time and note where the flow branches and where it loops back.
- Resolve ambiguity. Discuss why context matters for words like "show" or "sales" that can be more than one part of speech.
- Handle the unknown. Brainstorm suffix and capitalization rules for words the model never saw in training.
- Connect downstream. Explain why POS tags are a prerequisite for dependency parsing and entity extraction.