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Contextual Relevance

Intelligently score relevance with respect to its context.

Image Description

Assess how important a piece of data is in context to a specific domain just like a human expert. Using the next generation of natural language processing and the latest developments in deep learning, the machine is able to understand the meaning of a piece of information within a specific area of expertise i.e. finance, real estate, marketing, etc. Articles can now be intelligently appraised and a score can be set to determine how important and relevant any piece of information is. Categorize and distill domain-specific information with artificial intelligence, just like a human expert.

Key Features

  • Agile learning -

    The model is built using a semi-supervised approach and is very agile in understanding and operating within different domains

  • Variable relevance -

    Users can configure the model with a numerical score and have the freedom to choose their own threshold to differentiate between relevant and irrelevant information

  • Adaptable scoring -

    The scoring can be used both to extract important, as well as, ‘potentially important’ pieces of text.

  • Continous Dynamic Learning -

    The model intelligently learns and adapts to user-specific relevant texts, which is dynamically adjusted for within the knowledge function providing ever-increasing value

data = [
{ " Text " :
[ "$MARA over 100+ BitCoins on the bid at $5,490. About to explode." ]
response = requests.post (ACCRETE.RELEVANCE , data = data)
return (response.json ())
result : " "
[ 1.9452123478853652 ]
" "
How It Works

The power and advantages of building a model using a semi-supervised approach is the agility it provides for the model to be able to learn new domains quickly. The model in such cases only learns the specific relevant texts, while everything else is classified as irrelevant. We combine a plethora of machine learning algorithms with in-house deep-learning models to produce a score that signifies whether or not the content being evaluated is relevant. The models work in tandem, in a nested fashion to improve performance and pass context from one step to another. Finally, as the model learns new topics/domains, this semi-supervised approach allows it to develop branches where each branch specializes in one domain and thus allows the model to learn in a continuous and dynamic manner.

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