Named entity recognition

As explained in Graph Algorithms for Data Science

Named entity recognition is a natural language processing task that identifies and classifies textual mentions of entities into predefined categories.

Named entity recognition (NER) identifies and classifies mentions of named entities into predefined categories. The categories depend on the domain: venture-capital analysis may focus on people, organizations, and locations, while biomedical analysis may focus on genes, proteins, diseases, and drugs. section 12.2

A practical information-extraction pipeline can use NER to identify:

• People, organizations, locations, and dates for a general business-oriented extraction task. section 12.2
• Entity mentions that later support entity linking, relation extraction, and graph construction. section 12.0 section 12.6
• Domain-specific entity types selected according to the organization’s analytical needs. section 12.2

Organizations and people in a sentence

In one example, an NER model labels Google and YouTube as organizations and Eric Schmidt as a person. displaCy visualizes the extracted entities. section 12.2

Figure 12.4 Named entity recognition

View the labeled-entity visualization for the Google, YouTube, and Eric Schmidt example. section 12.2

How Graph Algorithms for Data Science covers it

How deep it goes

Defines NER, explains domain-dependent categories, and demonstrates labeled output without detailing model internals.

Examples

Uses Google, YouTube, and Eric Schmidt as labeled entities, visualized with displaCy.

What it assumes

Assumes familiarity with unstructured text and NLP while teaching entity categories through concrete domain examples.

What you can do after

Apply NER by labeling domain-relevant people, organizations, locations, dates, or specialized biomedical entities.

How it connects

Connects NER with information extraction, coreference resolution, entity linking, relation extraction, and graph construction.

liveBookmanning.com

Read Graph Algorithms for Data Science on liveBook Get the book at manning.com

Also explained in

• 

  liveBookmanning.com
  Natural Language Processing in Action, Second EditionNamed entity recognition identifies spans of text that refer to entities such as people, places, organizations, dates, or locations and assigns them entity categories.
• 

  liveBookmanning.com
  Knowledge Graphs and LLMs in ActionNamed entity recognition is the natural-language-processing technique of identifying mentions of named entities in text and assigning them to predefined categories.
• 

  liveBookmanning.com
  Machine Learning System DesignNamed entity recognition is a natural-language-processing technique that identifies and classifies mentions of entities in text.

Compare how 4 books explain named entity recognition →

Related concepts

• named entityproduces · A named entity is a textual mention of a specific, identifiable person, place, organization, date, or other entity that can be categorized.
• disambiguationcontrasts with · Disambiguation is the process of resolving ambiguity by selecting the intended interpretation, entity, or goal among plausible alternatives.
• classifierkind of · A classifier is a predictive computational model that assigns inputs to one or more discrete classes or categories.
• information extractionpart of · Information extraction is the computational process of identifying specified facts, entities, fields, or relationships in input data and producing structured information.
• structured extractionpart of · Structured extraction is the process of identifying specified information in source content and representing it as organized data in a defined structure.

This explainer was generated by Manning's knowledge graph from the text of Graph Algorithms for Data Science. The section links point to the parts of the book it draws on.