Natural Language Processing is a way for computer systems like robots to learn and mimic the human language. It helps intelligent systems decode the meaning of human sentences and master communication. NLP tools are important for businesses that work with huge amounts of unstructured data, be it emails, social media conversations, survey responses, and data in other forms. Companies can analyze data to find what is trending amidst the pools of data and use those insights to automate tasks and make business decisions. Popular applications of NLP technology are in sentiment analysis, where machines learn to understand common human feelings like sarcasm to detect fake news online, text classification that brings order to unstructured data by making sense out of it, in chatbots and virtual assistants to make them smarter and obey commands better, and improves auto-correct and speech recognition software.
These are the popular NLP trends:
- Transfer learning: Transfer learning trains a machine to perform one task and the skill is repurposed for another task. This can make industrial machines smarter with less amount of data for training.
- The rapid adoption of low-code tools: To build NLP models, coding skills are crucial. With the emergence of low-code tools, NLP technology can accessible to non-tech people who can partake in developments.
- Reinforcement learning: In reinforcement learning, algorithms learn by performing a task using a rail and error method. NLP models can be trained with feedback from previous tasks and experiences.
- Automation in customer service: Simple customer service tasks can be automated using NLP tools like routing customer support tickets, making more interactive chatbots, and automatic product tagging.
- Fake news detection: As NLP develops sentiment analysis features, fake news can be identified on several websites to create a safer environment on social media platforms. This can lessen the occurrence of online scams.
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