Named Entity Recognition basics in ML Python - Cheat Sheet & Quick Revision

Recall & Review

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What is Named Entity Recognition (NER)?

NER is a process in machine learning that finds and labels important words or phrases in text, like names of people, places, or dates.

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Name three common types of entities that NER systems identify.

People's names, locations (cities, countries), and organizations (companies, groups) are common entity types in NER.

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Why is NER useful in real life?

NER helps computers understand text better, making tasks like searching, summarizing, or answering questions faster and more accurate.

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What is the output of a Named Entity Recognition model?

The output is the original text with special labels showing which words are entities and what type they are, like <PERSON> or <LOCATION>.

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How does NER handle words that are not entities?

Words that are not entities are usually labeled as O (meaning Outside), showing they don't belong to any special category.

What does Named Entity Recognition do?

A Translates text into another language

B Finds and labels important words in text

C Generates new text from scratch

D Counts the number of words in a sentence

NER finds and labels important words like names and places in text.

Which of these is NOT a typical entity type in NER?

A Color

B Location

C Organization

D Person

Colors are usually not labeled as entities in basic NER tasks.

What label is often used for words that are not entities?

A B-ENTITY

B I-ENTITY

C O

D E-ENTITY

The label 'O' means the word is Outside any named entity.

Which real-life task can benefit from NER?

A Searching for specific names in documents

B Drawing pictures

C Playing music

D Cooking recipes

NER helps find names and places quickly in text documents.

What kind of data does NER work on?

A Images

B Videos

C Audio

D Text

NER analyzes text to find named entities.

Explain what Named Entity Recognition is and why it is useful.

Think about how computers find names or places in sentences.

Describe the typical output format of a Named Entity Recognition model.

Imagine highlighting words in a sentence with tags.

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?

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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?

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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?

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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