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