Simulating human intelligence has been a decades-long pursuit and with the recent advancements in deep neural networks and computational processing power significant breakthroughs are being made. Language has been a major feature of human intelligence that has differentiated us from machines. As a result, scientists have been working on enabling machines to achieve human level language skills. These skills, which include speech recognition, natural language understanding and natural language generation, make up what is known as Natural Language Processing (NLP).

Early NLP technologies were rule based programs when statistical models proved to be more robust. In recent times however, deep neural networks and representation learning have revolutionized NLP by offering state-of-the-art performance. Each NLP application comes with its own nuances in data processing and training, requiring solutions that are customized (at least partially) to meet the application specific requirements.

NLP applications enable humans to seamlessly interact with devices and vice versa. This capability offers a wide spectrum of use cases. NLP is the technology behind smart devices such as Google Home and Amazon’s Alexa. The technology also powers chatbots that have become the de facto customer service on thousands of websites. Some other general applications include sentiment analysis, language modeling, machine translation, document summarization and question answering. These applications will speed up the access to information and shortens the communication channel between humans and devices, hence improving efficiency in personal tasks and organizational operations.