Enterprises strive to get the most out of Natural language processing (NLP). One way to harness NLP is through cognitive search. A cognitive search employing NLP-fueled stemming and lemmatization can inject relevancy in the search results and drive various enterprise functions.
For example, cognitive search using NLP-driven stemming and lemmatization can help enterprise search engines understand the intent behind search queries by extracting the intent and enabling them to surface relevant search results.
Another business application is sentiment analysis. Stemming and lemmatization can enable enterprises to analyze available support ticket data and detect customer sentiments and emotional tone of the incoming cases in real-time. This would allow their agents to empathize and personalize their responses accordingly.
Furthermore, stemming and lemmatization algorithms can be used to analyze customer data to understand customer needs, identify their lifestyles, and predict the best action in a matter of seconds based on search history and engagement patterns.
NLP can also help service organizations benefit significantly. For example, stemming and lemmatization can power the applications that use NLP to analyze conversations in online community threads, extract context, and tag them with relevant topics and categories. In addition, it understands queries and scours the unified index to deliver contextual responses on unanswered threads, especially at odd hours.
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