Admiral Stavridis proposed an expansion to Eagle Guardian the NATO’s contingency plan for the reinforcement and defense of Poland.
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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
VIEW IN API DOCSExample 1
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
Available Engines
- NER, Social Data (Twitter)
- NER, Arabic
- NER, Chinese (Simplified)
Try Engines For Free
01
Get Started for Free
Contact us and we’ll set you up with free credits.
02
Test Our Engines
Test our API on your data.
03
Integrate at Scale
Once you’ve seen the power of engines, build your own scalable engine pipelines.
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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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Location Extraction
Identify all the locations in a piece of text and connect them to a map.
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Named Entity Linking
Link extracted entities with your knowledge graph or Primer’s knowledge base.
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Custom Entity Detection
Move beyond people, places, and organizations and identify other entities.
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Difference Engine
Determine the difference between two pieces of text or two versions.
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Event Detection
Identify the real world events within any piece of text.
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Event Linking
Connect events to form a timeline across your set of documents.