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What You Need to Know About Deep Learning and NLP

Deep Learning, Machine Learning and Natural Language Processing are key master components of Artificial Intelligence.

 

While Natural Language processing (NLP) is a way for computers to analyze, understand, and derive meaning from human language, Machine Learning provides to computers the capability to make predictions about the future data by creating an intuition using the past data.

 

On the other hand, Deep Learning creates better solutions with less data and less manual intervention, since it uses a cascade of many layers of nonlinear processing units for information extraction and transformation. Each successive layer uses the output from the previous layer as input.

 

In Symanto we use Deep Learning technology in two major ways:

 

1. Model & prediction:

Given expert annotation of the data we build predictive models that accurately infer from the data all the information we need to correctly profile users, products and brands. Deep Learning allows to achieve better performance with less human intervention and feature engineering as well as the possibility to transfer learning among different aspects of Psychology AI

 

2. Explain and transform:

Without any human annotation, we use low resolution noisy data to infer a clean structured information that is immediately actionable. Explainable Deep Learning helps achieve this transformation of cheap ubiquitous meta data into the expensive fine grained information that we need in a very accurate fashion.

 

Furthermore, we have developed a Real-Time analyzer, built with the latest deep learning techniques that have learnt to understand text at the most granular level to both understand exactly what has been written, as well as understanding the person who wrote it. The sentiment and emotion capabilities have been developed with ZERO human annotation, while the personality traits and communication style have been produced through annotation cycles by our Psychology and Research teams. This tool is in English, and using our proprietary techniques, we can project any deep learning capability to any language.