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.