Natural Language Processing
Some of the most used applications that employ NLP include Digital Assistants, Chatbots, and Voice-controlled GPS. Raybiztech offers NLP-based AI solutions that can serve complex business demands.
Common applications that use NLP
Apple’s Siri or Amazon’s Alexa – the smart gadgets that understand voice command, communicate in human language, and assists you in every step – uses NLP to recognize voice patterns and generate language. Any computer program that takes your command in human language and communicates in the same way, uses Natural Language Processing.
Chatbots are now very common on the web, particularly to handle customer relationships. Chatbots work in the same way as virtual assistants, only here they churn texts instead of voice commands. These tools are great at answering questions, relatively cheaper and efficient NLP solutions.
Google Translate is a popular example of machine translation that uses NLP. This application employs the NLP methodology to decipher the texts and generate the same sense in a different language. Machine translation has come a long way since its earlier days, and quality-wise has improved considerably. Still, there’s room for improvement.
Tools that summarize long texts into a small and crisp key idea or synopsis to be used as an index or for quick reference, uses Natural Language Processing. The best quality text summarization tools depend on natural language generation and semantic reasoning for creating summaries.
All spam detection tools are NLP solutions. Gmail uses NLP to sort the Spam emails from the important ones. These advanced applications use the text classification patterns of NLP to scan emails and find languages and terms that indicate the type of mail. If there are certain, terms in the mail, they will be directly sent to the Spam folder.
Natural Language Processing uses statistical techniques and models for interpreting human language. The set of techniques vary from one NLP application to another while using some common libraries. These programs break down the task into shorter pieces and then find a relationship between these sections to establish meaning. Deep learning models and advanced learning techniques have enabled the NLP solutions to learn as they work and automatically improve their performance over time.
Different tools and libraries for NLP are provided in Python. The elements are integrated into the application for performing tasks like word segmentation, sentence parsing, tokenization, etc. Natural Language Processing with Python has made it easy to employ the power of AI for creating intelligent bots capable of processing human language.