Part-of-speech tagging in NLP - Model Pipeline Trace

Model Pipeline - Part-of-speech tagging

This pipeline takes sentences and assigns each word a part-of-speech tag, like noun or verb. It helps computers understand sentence structure.

Data Flow - 6 Stages

1Input Text

100 sentences x variable words→Raw sentences with words→100 sentences x variable words

"The cat sits on the mat."

↓

2Tokenization

100 sentences x variable words→Split sentences into words (tokens)→100 sentences x variable tokens

["The", "cat", "sits", "on", "the", "mat"]

↓

3Feature Extraction

100 sentences x variable tokens→Convert words to numeric vectors (word embeddings)→100 sentences x variable tokens x 50 features

[[0.1, 0.3, ...], [0.05, 0.2, ...], ...]

↓

4Model Training

100 sentences x variable tokens x 50 features→Train sequence model (e.g., BiLSTM) to predict POS tags→100 sentences x variable tokens x 12 tag probabilities

[[0.01, 0.7, ..., 0.02], [0.6, 0.05, ..., 0.1], ...]

↓

5Prediction

1 sentence x variable tokens x 50 features→Model outputs POS tag probabilities for each token→1 sentence x variable tokens x 12 tag probabilities

[[0.01, 0.7, ..., 0.02], [0.6, 0.05, ..., 0.1], ...]

↓

6Tag Assignment

1 sentence x variable tokens x 12 tag probabilities→Select highest probability tag per token→1 sentence x variable tokens

["DET", "NOUN", "VERB", "ADP", "DET", "NOUN"]

Training Trace - Epoch by Epoch

Loss
1.2 |*       
0.9 | **     
0.7 |  ***   
0.55|   **** 
0.45|    *****
     --------
     Epochs

Epoch	Loss ↓	Accuracy ↑	Observation
1	1.2	0.55	Model starts learning basic patterns.
2	0.9	0.68	Accuracy improves as model learns word-tag relations.
3	0.7	0.75	Model captures more context, better tagging.
4	0.55	0.82	Loss decreases steadily, accuracy rises.
5	0.45	0.86	Model converges with good tagging performance.

Prediction Trace - 5 Layers

Layer 1: Input Sentence

Layer 2: Word Embedding Layer

Layer 3: BiLSTM Layer

Layer 4: Dense + Softmax Layer

Layer 5: Tag Selection

Model Quiz - 3 Questions

What does the tokenization stage do?

A Splits sentences into words

B Converts words to numbers

C Assigns POS tags

D Trains the model

Key Insight

Part-of-speech tagging models learn to assign grammatical tags to words by understanding context through sequence models. Training improves accuracy by reducing prediction errors over time.