Named entity recognition in NLP - Model Pipeline Trace

Model Pipeline - Named entity recognition

Named Entity Recognition (NER) finds and labels names of people, places, or things in text. It helps computers understand important words in sentences.

Data Flow - 4 Stages

1Raw Text Input

1 sentence, 10 words→Input sentence to be analyzed→1 sentence, 10 words

"Apple is looking at buying U.K. startup for $1 billion"

↓

2Tokenization

1 sentence, 10 words→Split sentence into individual words (tokens)→1 sentence, 11 tokens

["Apple", "is", "looking", "at", "buying", "U.K.", "startup", "for", "$", "1", "billion"]

↓

3Feature Extraction

1 sentence, 11 tokens→Convert tokens into numerical vectors→1 sentence, 11 tokens, 100 features each

[[0.12, 0.34, ..., 0.56], ..., [0.78, 0.11, ..., 0.22]]

↓

4Model Prediction

1 sentence, 11 tokens, 100 features→Predict entity label for each token→1 sentence, 11 tokens, 1 label each

["ORG", "O", "O", "O", "O", "LOC", "O", "O", "O", "O", "O"]

Training Trace - Epoch by Epoch

Loss
1.2 |*       
0.9 | **     
0.7 |  ***   
0.5 |    ****
0.4 |     *****
     --------
     Epochs

Epoch	Loss ↓	Accuracy ↑	Observation
1	1.2	0.60	Model starts learning, loss high, accuracy low
2	0.9	0.72	Loss decreases, accuracy improves
3	0.7	0.80	Model learns important patterns
4	0.5	0.86	Better recognition of entities
5	0.4	0.90	Model converges with good accuracy

Prediction Trace - 3 Layers

Layer 1: Tokenization

Layer 2: Feature Extraction

Layer 3: Model Prediction

Model Quiz - 3 Questions

What does the tokenization step do in NER?

A Labels each word with entity type

B Splits text into individual words

C Converts words into numbers

D Trains the model to recognize entities

Key Insight

Named Entity Recognition models learn to identify important words like names and places by converting text into numbers and training to label each word. Over time, the model improves by reducing errors and increasing accuracy.