Named Entity Recognition basics in ML Python - Model Pipeline Trace

Model Pipeline - Named Entity Recognition basics

Named Entity Recognition (NER) finds important names in text, like people, places, or dates. It helps computers understand text by marking these special words.

Data Flow - 4 Stages

1Raw Text Input

1 sentence (variable length)→Input sentence with words→1 sentence (variable length)

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

↓

2Tokenization

1 sentence (variable length)→Split sentence into words or tokens→1 sentence x 10 tokens

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

↓

3Feature Extraction

1 sentence x 10 tokens→Convert tokens to numbers (word embeddings)→1 sentence x 10 tokens x 50 features

[[0.12, -0.03, ...], [0.05, 0.10, ...], ...]

↓

4Model Prediction

1 sentence x 10 tokens x 50 features→Model predicts entity label for each token→1 sentence x 10 tokens x 1 label

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

Training Trace - Epoch by Epoch

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

Epoch	Loss ↓	Accuracy ↑	Observation
1	1.2	0.60	Model starts learning, loss is 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' stage do in NER?

A Predicts entity labels

B Converts tokens into numbers

C Splits text into words or tokens

D Calculates model accuracy

Key Insight

Named Entity Recognition models learn to identify special words by breaking text into tokens, turning words into numbers, and then predicting labels. Training improves accuracy by lowering loss over time.

Practice

1. What is the main goal of Named Entity Recognition (NER) in text processing?

easy

A. To generate new sentences based on input

B. To find and label important words like names and places

C. To count the number of words in a sentence

D. To translate text from one language to another

Solution

1. Step 1: Understand NER purpose

   NER is designed to identify and label entities such as names, places, and organizations in text.

2. Step 2: Compare options

   Only To find and label important words like names and places describes this goal correctly; others describe different tasks like translation or counting.

3. Final Answer:

   To find and label important words like names and places -> Option B

4. Quick Check:

   NER = Label important words [OK]

Hint: NER labels names and places in text quickly [OK]

Common Mistakes:

• Confusing NER with translation
• Thinking NER counts words
• Mixing NER with text generation

2. Which of the following is the correct way to label the entity 'London' as a location in a simple NER tagging scheme?

easy

A. London

B. LOC-London

C. [LOC]London[/LOC]

D. B-LOC

Solution

1. Step 1: Recall common NER tagging formats

   NER often uses tags like B-LOC (Beginning of Location) and I-LOC (Inside Location) in BIO format.

2. Step 2: Identify correct tag for 'London'

   Since 'London' is a single word entity, it is labeled as B-LOC to mark the start of a location entity.

3. Final Answer:

   B-LOC -> Option D

4. Quick Check:

   B-LOC tags start of location entity [OK]

Hint: Use B-LOC to mark start of location entity [OK]

Common Mistakes:

• Using XML-like tags which are not standard in NER
• Confusing bracket styles
• Writing label after the word instead of before

3. Given this simple NER output for the sentence: ['John', 'B-PER'], ['lives', 'O'], ['in', 'O'], ['Paris', 'B-LOC'], what is the recognized entity?

medium

A. No entities recognized

B. John is a location, Paris is a person

C. John is a person, Paris is a location

D. John and Paris are both organizations

Solution

1. Step 1: Interpret tags

   'B-PER' means beginning of a person entity, 'B-LOC' means beginning of a location entity, 'O' means outside any entity.

2. Step 2: Map tags to words

   John is tagged as a person, Paris as a location, and 'lives' and 'in' are not entities.

3. Final Answer:

   John is a person, Paris is a location -> Option C

4. Quick Check:

   B-PER = person, B-LOC = location [OK]

Hint: B-PER means person, B-LOC means location [OK]

Common Mistakes:

• Mixing up person and location tags
• Ignoring 'O' tags as no entity
• Assuming all words are entities

4. You have this NER tagging output: ['Apple', 'I-ORG'], ['is', 'O'], ['big', 'O']. What is the error in this tagging?

medium

A. 'Apple' should be tagged as B-ORG, not I-ORG

B. 'is' should be tagged as B-ORG

C. 'big' should be tagged as I-ORG

D. No error, tags are correct

Solution

1. Step 1: Understand BIO tagging rules

   'B-' means beginning of an entity, 'I-' means inside an entity. The first word of an entity must be B- tag.

2. Step 2: Check 'Apple' tag

   'Apple' is the first word of the organization entity, so it must be tagged B-ORG, not I-ORG.

3. Final Answer:

   'Apple' should be tagged as B-ORG, not I-ORG -> Option A

4. Quick Check:

   First entity word = B- tag [OK]

Hint: First entity word must use B- tag [OK]

Common Mistakes:

• Tagging first entity word as I-
• Tagging non-entity words as entities
• Ignoring BIO format rules

5. You want to improve a simple NER model trained on a small dataset. Which approach is best to get better results on recognizing entities in new text?

hard

A. Use a pretrained NER model and fine-tune it on your data

B. Increase the number of epochs without changing the model

C. Remove all entity labels and train a new model from scratch

D. Use random labels to increase data size

Solution

1. Step 1: Understand pretrained model benefits

   Pretrained NER models have learned from large data and generalize well to new text.

2. Step 2: Fine-tuning improves performance

   Fine-tuning a pretrained model on your small dataset adapts it to your specific task and improves accuracy.

3. Final Answer:

   Use a pretrained NER model and fine-tune it on your data -> Option A

4. Quick Check:

   Pretrained + fine-tune = best for small data [OK]

Hint: Fine-tune pretrained models for best NER results [OK]

Common Mistakes:

• Training from scratch on small data
• Ignoring pretrained models
• Using random labels that confuse training