Primer acquires Yonder, adds disinformation analysis to AI portfolio for information operations.

Synthetic Text Detection

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

Documentation

Example

Bitcoin will hit $100,000 in the next year. It will be one of the most difficult investments you have ever seen. In a couple of years we will be able to pay for our education and our schooling and we will get a chance to retire as well…

Output[RESULTS]

class:

synthetic text

confidence:

0.81

Example

Output[RESULTS]

class:

synthetic text

confidence:

0.81

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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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