Information extraction patterns in NLP - Model Pipeline Trace

Model Pipeline - Information extraction patterns

This pipeline extracts useful facts from text using patterns. It finds specific information like names, dates, or places by matching text shapes and words.

Data Flow - 4 Stages

1Raw Text Input

1000 sentences x variable length→Input raw text data containing sentences→1000 sentences x variable length

"John Smith was born in 1990 in New York."

↓

2Text Preprocessing

1000 sentences x variable length→Lowercase, remove punctuation, tokenize sentences into words→1000 sentences x 10 words (average)

["john", "smith", "was", "born", "in", "1990", "in", "new", "york"]

↓

3Pattern Matching

1000 sentences x 10 words→Apply predefined patterns to find entities like names, dates, locations→1000 sentences x extracted entities (0-5 per sentence)

[{"PERSON": "John Smith"}, {"DATE": "1990"}, {"LOCATION": "New York"}]

↓

4Entity Normalization

1000 sentences x extracted entities→Standardize extracted entities (e.g., date formats)→1000 sentences x normalized entities

[{"PERSON": "John Smith"}, {"DATE": "1990-01-01"}, {"LOCATION": "New York, NY"}]

Training Trace - Epoch by Epoch

Loss
1.0 |****
0.8 |****
0.6 |****
0.4 |****
0.2 |****
0.0 +----
      1 2 3 4 5 Epochs

Epoch	Loss ↓	Accuracy ↑	Observation
1	0.85	0.60	Model starts learning basic patterns, accuracy is moderate.
2	0.60	0.75	Loss decreases as model better recognizes entities.
3	0.45	0.82	Model improves entity extraction accuracy.
4	0.35	0.88	Loss continues to decrease, accuracy nearing good performance.
5	0.28	0.91	Model converges with high accuracy on pattern extraction.

Prediction Trace - 4 Layers

Layer 1: Input Sentence

Layer 2: Text Preprocessing

Layer 3: Pattern Matching

Layer 4: Entity Normalization

Model Quiz - 3 Questions

What happens during the 'Pattern Matching' stage?

A Extracting entities like names and dates from tokenized text

B Lowercasing and splitting sentences into words

C Standardizing date formats

D Inputting raw text sentences

Key Insight

Information extraction patterns help machines find useful facts in text by matching shapes and words. Training improves the model's ability to spot entities accurately, and normalization makes the results consistent and easier to use.