Natural Language Processing (NLP)
Recurrent Neural Networks with Deep Learning
Recurrent Neural Networks makes it possible for Deep Learning based models to understand natural language. These models can be used in large number of applications.
Text Classification models classify text into a distinct category.
They are used in applications like
Classifying comments/ reviews into positive or negative
Identifying intent of a user’s question to a chatbot
Classifying posts/ articles into various categories
Neural translation models improves quality of machine translation by providing contextual meaning to words and sentences.
Neural Translation is used by many popular translation services
like Google Translation
Sentiment Analysis identifies sentiment of any text into positive or negative. It also gives a magnitude of positiveness or negativeness of the text.
Sentiment analysis is used to identify sentiment of
Tweets on a particular topic
User reviews / comments on e-commerce platforms
Question Answering systems
Question answering systems finds a descriptive answer for any question from a given text.
Question answering systems can be used in
Helping students find specific answers from educational content
In areas where the user’s manual are complex and quite large,
question answering system can be implemented to answer user’s
questions from the manual
Captioning an image with descriptive text. This technique combines Computer Vision and Natural Language Processing techniques to create a model that can describe contents of an image into natural language.
This technique is useful for
Captioning images on the internet, which helps visually impaired
people using a screen reader to describe the contents of an image
Captioning a large database of images to make it easier to search
images with description.