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Topic Classification

Classify text into topics based on the semantic and contextual meaning

Image Description

To be able to classify topics accurately you need to understand language like a human. Using a deep-learning mathematical framework each text snippet can be classified into a topic with 93% accuracy. This framework comprises the latest advancements in natural language processing and understands the contextual meaning of the individual sentence to its adjacent sentences and its placement within the entire paragraph/document. By developing an AI that can understand the semantic and contextual nature of an individual sentence within its surrounding environment, the topic classification can provide a deeper level of understanding as meticulously as a human at scale.

Key Features

  • Language has multi-scaled representation -

    The tool is able to look at words that are both near and far away from each other in terms of their relationships massively increasing the topic classification accuracy.

  • Builds memory across the board -

    The model has been trained to extract context from sentences, near and far from within the document. Therefore accounting for changing context and nuance within the text.

  • Expandable topics -

    Users can generate their own list of topics and add them to the ones surfaced by the knowledge function. Increasing the accuracy and value of the AI

  • Continuous dynamic learning -

    The model keeps growing in size and gets smarter as it encounters new data which ultimately delivers more value over time

  • Rapid contextual learning -

    With a very limited training dataset the AI is able to rapidly learn the context of a document. The model achieves 93% accuracy with respect to 'Bayes Error'.

data = [
{ " text " :
[ " For example, last month, Stanford Medicine reported results of the Apple Heart Study, the largest study ever of its kind which enrolled over 400,000 participants from all 50 states in a span of only eight months " ]
response = requests.post (ACCRETE.CONCEPT , data = data)
return (response.json ())
result : " "
[ " \ Cardiovascular \ " ] , [ 0.5477317571640015 ]
" "
How It Works

The model starts by converting the text into a multi-scale numerical representation. This representation allows the model to build a multi-scale memory from the text and thus is able to extract the most relevant information related to the task. These numerical representations are then passed on to a custom architecture based deep-learning network module. We have been conducting extensive research to build a continuous dynamic learning network, that can learn from the feedback given by a user, without going into catastrophic forgetting, as is usually the case with online learning for deep-learning networks. Our network has the ability to semi-autonomously grow in size, to alleviate this issue as well as become smarter as it looks at more data, just like a child becomes an educated adult.

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