Named Entity Recognition basics in ML Python - Practice Problems & Coding Challenges

Challenge - 5 Problems

🧠 Conceptual

intermediate

What is the main goal of Named Entity Recognition (NER)?

Choose the best description of what NER does in text processing.

A Identify and classify key information like names, places, and dates in text.

B Translate text from one language to another automatically.

C Summarize long documents into short paragraphs.

D Detect the sentiment or emotion expressed in a sentence.

💡 Hint

Think about what entities like people or locations are in a sentence.

NER finds and labels important words or phrases such as names of people, places, or organizations in text.

❓ Predict Output

intermediate

Output of a simple NER tagging example

What is the output of this NER tagging code snippet?

ML Python

sentence = "Apple is looking at buying U.K. startup for $1 billion"
entities = [('Apple', 'ORG'), ('U.K.', 'GPE'), ('$1 billion', 'MONEY')]

A [('Apple', 'ORG'), ('U.K.', 'PERSON'), ('$1 billion', 'MONEY')]

B [('Apple', 'PERSON'), ('U.K.', 'ORG'), ('$1 billion', 'DATE')]

C [('Apple', 'ORG'), ('U.K.', 'GPE'), ('$1 billion', 'MONEY')]

D [('Apple', 'ORG'), ('U.K.', 'GPE'), ('$1 billion', 'QUANTITY')]

💡 Hint

ORG means organization, GPE means geopolitical entity, MONEY means monetary value.

The entities are correctly labeled as Apple (organization), U.K. (location), and $1 billion (money).

❓ Model Choice

advanced

Choosing the best model type for NER

Which model type is most suitable for Named Entity Recognition tasks?

A Recurrent Neural Network (RNN) or Transformer-based model trained on sequence labeling

B Convolutional Neural Network (CNN) designed for image classification

C Generative Adversarial Network (GAN) for image generation

D K-Means clustering for unsupervised grouping

💡 Hint

NER requires understanding word order and context in sentences.

RNNs and Transformers handle sequences well and are commonly used for labeling each word in a sentence with entity tags.

❓ Metrics

advanced

Evaluating NER model performance

Which metric is most appropriate to evaluate a Named Entity Recognition model?

A Silhouette score measuring cluster separation

B Accuracy of predicting the exact entity tags for each token

C BLEU score comparing generated text to reference text

D Mean Squared Error between predicted and true entity counts

💡 Hint

NER is a classification task at the token level.

Accuracy or F1-score on token-level entity tags is used to measure how well the model predicts correct entity labels.

🔧 Debug

expert

Debugging incorrect NER predictions

Given this NER model output on the sentence "Barack Obama was born in Hawaii.": Predicted entities: [('Barack', 'PERSON'), ('Obama', 'PERSON'), ('Hawaii', 'ORG')] What is the likely cause of the error?

A The model incorrectly labeled 'Hawaii' as an organization instead of a location.

B The model did not detect any entities in the sentence.

C The model confused the entity type for 'Barack' and 'Obama'.

D The model failed to recognize 'Barack Obama' as a single entity and split it into two.

💡 Hint

Consider how multi-word entities should be tagged in NER.

The model split the full name 'Barack Obama' into two separate entities instead of one combined entity, which is a common error in NER.

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