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Topic Classification

Classify text into topics based on the semantic and contextual meaning

Image Description
Overview

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. Accrete has developed AI that can understand the semantic and contextual nature of an individual sentence within its surrounding environment and specific to the domain of the text itself using language understanding, the topic classification can provide a deeper level of understanding as meticulously as a human at scale.

 

This is available in 2 languages: English and Simplified Chinese

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'.

Illustration
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.

Demo
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 ]
" "

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