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01

Named Entity Recognition

Named Entity Recognition

Identify people, locations, and organizations within a body of text. Our NER engine has surpassed the best-performing models in the industry by a wide margin. The engine is optimized on social for short informal content including emojis and spelling mistakes. The user has the option to turn on disambiguation for named entity variations.

The entities that these engines create become the backbone of your knowledge graph and analytic workflows. This engine can be tuned to specific verticals (i.e. finance or geopolitical). This engine is also available on Social Data. Available Languages: English, Arabic, Chinese (Simplified).

Documentation

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Example 1

Admiral Stavridis proposed an expansion to Eagle Guardian the NATO’s contingency plan for the reinforcement and defense of Poland.

Entities[PER]

people:

Stavridis

confidence:

0.87

[MISC]

miscellaneous:

Eagle Guardian

confidence:

0.79

[ORG]

organizations:

NATO

confidence:

0.96

[LOC]

locations:

Poland

confidence:

1.00

Example 1

Admiral Stavridis proposed an expansion to Eagle Guardian the NATO’s contingency plan for the reinforcement and defense of Poland.

Entities[PER]

people:

Stavridis

confidence:

0.87

[MISC]

miscellaneous:

Eagle Guardian

confidence:

0.79

[ORG]

organizations:

NATO

confidence:

0.96

[LOC]

locations:

Poland

confidence:

1.00

Example 2

Michelle Leung’s Xingtai Capital avoids companies that have yet to turn a profit and doesn’t hold stocks in Chinese internet giants, including Alibaba and Tencent.

Entities

people:

Michelle Leung

confidence:

1.00

needs disambiguation:

False

organizations:

Xingtai Capital

confidence:

1.00

needs disambiguation:

False

organizations:

Alibaba

confidence:

0.99

needs disambiguation:

False

organizations:

Tencent

confidence:

1.00

needs disambiguation:

False

Example 2

Michelle Leung’s Xingtai Capital avoids companies that have yet to turn a profit and doesn’t hold stocks in Chinese internet giants, including Alibaba and Tencent.

Entities

people:

Michelle Leung

confidence:

1.00

needs disambiguation:

False

organizations:

Xingtai Capital

confidence:

1.00

needs disambiguation:

False

organizations:

Alibaba

confidence:

0.99

needs disambiguation:

False

organizations:

Tencent

confidence:

1.00

needs disambiguation:

False

Available Engines

  • NER, Social Data (Twitter)
  • NER, Arabic
  • NER, Chinese (Simplified)

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02

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03

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10

Synthetic Text Detection

Classify text as likely to have been written by a human or a machine.

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