NER with spaCy in NLP - Cheat Sheet & Quick Revision

Recall & Review

beginner

What does NER stand for in spaCy?

NER stands for Named Entity Recognition. It means finding names of people, places, dates, and other important things in text.

beginner

How does spaCy represent entities found in text?

spaCy uses the Doc.ents attribute to list all entities found in the text. Each entity has a label like PERSON, ORG, or DATE.

beginner

What is the purpose of the nlp object in spaCy?

The nlp object is the main tool that processes text. It breaks text into words, finds entities, and adds other language features.

intermediate

How can you add a new entity label to spaCy's NER model?

You can add a new entity label by updating the NER pipeline component and training the model with examples that include the new label.

beginner

What is the role of training data in improving spaCy's NER model?

Training data with examples of text and correct entity labels helps spaCy learn to recognize entities better and improve accuracy.

Which spaCy attribute gives you the entities found in a text?

A Doc.ents

B Doc.tokens

C Doc.text

D Doc.sents

The Doc.ents attribute contains all named entities found in the processed text.

What type of information does NER extract from text?

A Sentiment scores

B Topic categories

C Parts of speech tags

D Named entities like people, places, and dates

NER focuses on finding named entities such as people, organizations, locations, and dates.

How do you process text with spaCy to find entities?

A Pass text to the <code>nlp</code> object

B Call <code>Doc.ents</code> directly on raw text

C Use <code>nlp.entities()</code> function

D Use <code>nlp.tokenize()</code> only

You process text by passing it to the nlp object, which returns a Doc with entities.

Which of these is NOT a common entity label in spaCy?

A ORG

B COLOR

C PERSON

D DATE

COLOR is not a standard entity label in spaCy's default NER model.

What do you need to improve spaCy's NER model for a new entity type?

A Adding more stop words

B Only changing the model code

C Training data with examples of the new entity

D Increasing the text length

You need training data showing examples of the new entity to teach the model to recognize it.

Explain how spaCy's NER pipeline works to find entities in text.

Think about how text flows through spaCy and how entities are marked.

Describe the steps to add a new custom entity label to spaCy's NER model.

Focus on data preparation and model training.

Practice

1. What does NER (Named Entity Recognition) do in natural language processing?

easy

A. It generates new text based on input prompts.

B. It translates text from one language to another.

C. It summarizes long documents into short paragraphs.

D. It finds and labels important names and terms in text automatically.

Solution

1. Step 1: Understand NER's purpose

   NER identifies specific names like people, places, or organizations in text.

2. Step 2: Compare with other NLP tasks

   Translation, summarization, and text generation are different tasks than NER.

3. Final Answer:

   It finds and labels important names and terms in text automatically. -> Option D

4. Quick Check:

   NER = Finds names and terms [OK]

Hint: NER extracts names and terms, not translations or summaries [OK]

Common Mistakes:

• Confusing NER with translation or summarization
• Thinking NER generates new text
• Believing NER only finds keywords, not named entities

2. Which of the following is the correct way to load a pre-trained spaCy model for NER?

easy

A. import spacy; nlp = spacy.load('en_core_web_sm')

B. import spacy; nlp = spacy.model('en_core_web_sm')

C. import spacy; nlp = spacy.load_model('en_core_web_sm')

D. import spacy; nlp = spacy.get('en_core_web_sm')

Solution

1. Step 1: Recall spaCy model loading syntax

   spaCy uses spacy.load('model_name') to load pre-trained models.

2. Step 2: Check each option

   Only import spacy; nlp = spacy.load('en_core_web_sm') uses spacy.load correctly; others use invalid functions.

3. Final Answer:

   import spacy; nlp = spacy.load('en_core_web_sm') -> Option A

4. Quick Check:

   spaCy model loading = spacy.load() [OK]

Hint: Use spacy.load('model_name') to load models [OK]

Common Mistakes:

• Using spacy.model or spacy.load_model which don't exist
• Trying spacy.get which is not a spaCy function
• Forgetting to import spacy before loading

3. Given this code snippet using spaCy for NER:

import spacy
nlp = spacy.load('en_core_web_sm')
doc = nlp('Apple is looking at buying U.K. startup for $1 billion')
entities = [(ent.text, ent.label_) for ent in doc.ents]
print(entities)

What will be the output?

medium

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

B. [('Apple', 'ORG'), ('startup', 'ORG'), ('$1 billion', 'MONEY')]

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

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

Solution

1. Step 1: Understand spaCy NER labels

   Apple is recognized as an organization (ORG), U.K. as geopolitical entity (GPE), and $1 billion as money (MONEY).

2. Step 2: Match entities with labels

   [('Apple', 'ORG'), ('U.K.', 'GPE'), ('$1 billion', 'MONEY')] correctly matches these entities and labels as spaCy outputs.

3. Final Answer:

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

4. Quick Check:

   spaCy NER output matches [('Apple', 'ORG'), ('U.K.', 'GPE'), ('$1 billion', 'MONEY')] [OK]

Hint: Check spaCy's common entity labels for correct matches [OK]

Common Mistakes:

• Confusing ORG with PERSON or GPE
• Mislabeling MONEY as QUANTITY
• Including words like 'startup' as entities

4. You run this code but get an error:

import spacy
doc = nlp('Google is a tech giant')

What is the most likely cause?

medium

A. spaCy does not support the word 'Google'.

B. The variable 'nlp' is not defined before use.

C. The text input is too short for NER.

D. Missing parentheses in the print statement.

Solution

1. Step 1: Check variable definitions

   The code uses 'nlp' without defining it by loading a spaCy model first.

2. Step 2: Identify error cause

   This causes a NameError because 'nlp' is undefined.

3. Final Answer:

   The variable 'nlp' is not defined before use. -> Option B

4. Quick Check:

   Undefined variable 'nlp' causes error [OK]

Hint: Always load model with spacy.load before using nlp [OK]

Common Mistakes:

• Assuming text length causes error
• Thinking spaCy can't recognize common words
• Confusing print syntax errors with variable errors

5. You want to extract only person names from a text using spaCy's NER. Which code snippet correctly filters for persons?

hard

A. persons = [ent.text for ent in doc.ents if ent.label_ == 'PERSON']

B. persons = [ent.text for ent in doc.ents if ent.label_ == 'ORG']

C. persons = [ent.text for ent in doc.ents if ent.label_ == 'GPE']

D. persons = [ent.text for ent in doc.ents if ent.label_ == 'MONEY']

Solution

1. Step 1: Identify label for persons in spaCy

   spaCy uses 'PERSON' label for people names.

2. Step 2: Filter entities by 'PERSON'

   Filtering doc.ents by ent.label_ == 'PERSON' extracts only person names.

3. Final Answer:

   persons = [ent.text for ent in doc.ents if ent.label_ == 'PERSON'] -> Option A

4. Quick Check:

   Filter entities by 'PERSON' label [OK]

Hint: Filter entities with label_ == 'PERSON' to get names [OK]

Common Mistakes:

• Using wrong labels like ORG or GPE for persons
• Not filtering entities at all
• Confusing entity text with label