POS Tagging Process Flow
flowchart TD
Start([Input: Tokenized sentence]):::io
Init[Initialize: Load POS tag probabilities]:::prob
ForEach[For each word in sequence]:::prob
Lookup[Lookup word in vocabulary]:::prob
Known{Word known?}:::decision
UseProb[Use trained probabilities]:::prob
Unknown[Apply unknown word heuristics]:::unknown
Assign[Assign most probable tag]:::prob
More{More words?}:::decision
Return[Return tagged sequence]:::io
End([Tagged sentence ready for parsing]):::io
Start --> Init --> ForEach --> Lookup --> Known
Known -->|Yes| UseProb --> Assign
Known -->|No| Unknown --> Assign
Assign --> More
More -->|Yes| ForEach
More -->|No| Return --> End
classDef io fill:#42a5f5,stroke:#0d47a1,stroke-width:2px,color:#fff,font-size:15px
classDef prob fill:#66bb6a,stroke:#1b5e20,stroke-width:2px,color:#fff,font-size:15px
classDef decision fill:#ffd54f,stroke:#f57f17,stroke-width:2px,color:#333,font-size:15px,font-weight:bold
classDef unknown fill:#ab47bc,stroke:#4a148c,stroke-width:2px,color:#fff,font-size:15px
linkStyle default stroke:#777,stroke-width:2px,font-size:14px
Step Color Key
Input / output
Probability calculations
Decision points
Unknown-word handling
Step Details
Hover a step to see how it works.
Example: for "show" the model holds P(VB|show)=0.65 and
P(NN|show)=0.35. Given the modal context "Can you ___", the
tagger selects VB.