Primer launches Primer Command, an AI-powered, real-time intelligence solution

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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Named Entity Recognition

Identify people, locations, and organizations within a body of text.

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Coreference Resolution

Identify who the words “he,” “she,” and “they” are referring to.

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Relation Extraction

Identify relationships between entities.

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Classifiers

Categorize any kind of text.

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Sentiment Analysis

Classify text as positive, negative, or neutral, returned as a classifier.

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Topic Modeling

Identify the topic that the text is talking about without the need for training or ontology.

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Summarization

Summarize a document to meet your needs and identify the key information.

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Key Phrase Extraction

Automatically identify and extract the key information within a document.

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Explainability

See what parts of the text the models are using for your predictions.

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Synthetic Text Detection

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

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Claims Detection and Dispute Resolution

Identify claims within any text and determine if these claims are supported or refute other claims.

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Jargon Detection and Explanation

Identify jargon and industry acronyms within the text and link it to an explanation of the terms.

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Quotes Attribution

Extract a quote and its author from a body of text.

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Title Generator

Generate a human-quality title or headline for a body of text.

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Question Answering

Answer specific questions about a body of text.

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DIMEFIL

Classify a piece of text as being related to Diplomacy, Information Operations, Military, International Economics, Illicit Finance, Intelligence, and Law Enforcement.

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