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

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

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

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

Image captioning

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

  into words.

• Captioning a large database of images to make it easier to search

  images with description.