Natural language processing (NLP) techniques are essential for enterprises that deal with significant amounts of unstructured text, such as emails, social network chats, online chats, survey replies, and a variety of other data types.
Companies may analyze data to identify what is meaningful within the clutter and acquire valuable insights to automate processes and drive strategic decisions by introducing NLP into the workspace.
Why Should You Learn Natural Language Processing?
Our thoughts and sentiments, ideas, research, facts, opinions, and feedback enter the digital space through social media feeds, warranty claims, electronic health records, contact center logs, legal case files, and other means. Natural language processing reveals the hidden meanings in this ocean of these word patterns.
Natural language processing converts text into data that can be analyzed. Banks, life sciences, health care, and manufacturers adopt NLP to improve client experiences and prevent fraud. Therefore, it is one of the most in-demand skills to learn and boost up your career.
There are many online courses that you may opt for to learn more about this branch of artificial intelligence.
Now let us learn about a few applications of natural language processing.
For autonomous questions and replies, chatbots and virtual assistants are utilized. They are meant to comprehend natural language and respond appropriately using natural language generation. Chatbots powered by AI learn from every conversation and choose how to reply.
These intelligent machines are becoming more common at the front lines of customer service. They can assist teams in answering up to 80% of routine questions and direct more complex issues to human operators. Chatbots, available 24 hours, can reduce response times and free an employee from answering repeated and time-consuming inquiries.
- Sentiment Analysis
Interpreting natural language is particularly tough for computers when opinions are to be analyzed because people frequently use sarcasm and irony. Sentiment analysis, a form of NLP, though, can detect minor distinctions in feelings and attitudes and identify whether they are positive or negative.
- Text Classification
Text classification entails automatically comprehending, processing, and categorizing unstructured text, and it also incorporates sentiment analysis.
Suppose you have to look at scores of open-ended replies from a recent NPS survey. Manually doing it would take quite a long time and be extremely costly. However, a natural language processing model can instantly classify your data in no time, using established categories like Customer Support, Features, and Pricing.
- Machine Translation
One of the early uses of natural language processing was machine translation (MT). Though machine translation still struggles to recognize the text’s context, if you have used Google Translate, you will know that it has made a lot of progress since its start, thanks to the tremendous progress made in neural networks and better access to massive amounts of data.
Grammar checking software and auto-correct features rely heavily on Auto-Correct NLP. Grammarly, for instance, makes use of natural language processing to help you optimize your content by detecting grammatical, spelling, and sentence structure mistakes.
There are numerous fascinating applications of natural language processing. They are used not only to gather information or to aid decision-making but also to streamline time-consuming operations.