The Shift toward Cloud: Is it a Cost-Effective Process?
Delivering Unique Customer Experience via Technology
How Path to Production Ensures Safety and Soundness on Our Digital...
The Evolving Landscape of Mortgage
Put your Frontline Teams in the Driving Seat through a...
Tatiana Sorokina, Solutions Director, Data Science & Artificial Intelligence, DSAI Innovation Execution, Novartis
The Future of Chatbots
John Tubert, SVP, Technology, R/GA
Enterprise AI: How to Meet the Challenges
Dr. Anand S. Rao, Global AI Leader, PwC
Revolutionizing Customer Relationships through Chatbots
Mamie Peers, VP of Digital Marketing, The Cosmopolitan of Las Vegas
Thank you for Subscribing to CIO Applications Weekly Brief
Top Trends to Watch for in Natural Language Processing
Fremont, CA: Natural Language Processing (NLP) enables computer systems, such as robots to learn and replicate human language. It aids intelligent systems in decoding the meaning of human utterances and communicating effectively. NLP technologies are critical for firms that deal with large volumes of unstructured data, such as emails, social media interactions, survey replies, and other sorts of data. Companies may use data analytics to discover large amounts of data trends and then utilize those insights to automate processes and make business choices. Popular applications of NLP technology include sentiment analysis, in which machines learn to understand common human emotions such as sarcasm to detect fake news online, text classification, which brings order to unstructured data by making sense of it, chatbots and virtual assistants to make them smarter and better obey commands, and improving auto-correct and speech recognition software.
Let’s see some of the top NLP trends:
- Transfer learning
Transfer learning is the process of teaching a machine to execute one activity and then repurposing that expertise for another. It has the potential to make industrial equipment smarter while requiring less data for training.
- The rapid adoption of low-code tools
Coding skills are required to create NLP models. With the advent of low-code tools, NLP technology is becoming more accessible to non-technologists who want to participate in development.
- Reinforcement learning
Algorithms in reinforcement learning learn by executing a task using a rail and error process. Feedback from prior activities and experiences can be helpful to train NLP models.
- Automation in customer service
Simple customer service operations, such as routing customer support tickets, creating more engaged chatbots, and automatic product labeling, may be automated using NLP techniques.
- Fake news detection
Fake news may get discovered on numerous websites as NLP develops sentiment analysis tools, resulting in a safer atmosphere on social media platforms. It can help to reduce the frequency of internet fraud.