Natural language processing (NLP) technology interprets people’s spoken and written language, enabling them to have conversations with automated systems that feel natural.
NLP can infer meaning and sentiment from what people write and say. It enables chatbots to respond in a way that feels human, helps digital assistants recognize and understand user needs, and powers accurate automated translation.
NLP is the understanding and generation of human language by a machine, enabling automated systems to have natural-feeling conversations with humans through speech or text.
NLP helps businesses deliver strong digital customer experiences, reduce contact center costs, and better understand customer needs.
NLP systems must be trained to correctly interpret human language. And if you have to build NLP models from scratch, it can take a long time to get accurate results.
NLP is used for translation, speech and intent recognition, sentiment analysis, automatic text transcription and summarization, chatbots, spelling and grammar checks, and more.
What is it?
Natural language processing (NLP) can both understand and generate human language. It helps machines infer meaning from speech and text, so they can reply in a way that feels naturally human and provide satisfying automated customer experiences.
NLP systems can also be taught to interpret more sophisticated aspects of human language to understand the changing sentiment and context during a conversation. This enables automated systems to adjust their responses in real time — if a customer becomes angry or frustrated, for example.
This ability to understand the context and tone of the conversation helps automated tools like chatbots deliver truly engaging, conversational experiences.
What’s in for you?
Organizations can use NLP to provide conversational self-service experiences for customers, reducing communication costs and increasing customer satisfaction. NLP can also help reduce costs and improve the efficiency of internal workflows by automating or assisting with information capture.
For example, NLP applications allow doctors to dictate medical notes directly into health information systems, enabling them to see more patients and improve the quality of care.
What are the trade offs?
While NLP technologies are more accurate than ever at inferring meaning, intent, and sentiment from human language, no NLP solution can yet guarantee 100% accuracy.
NLP technologies are also dependent on huge amounts of computing power and can be difficult and expensive to set up without using pre-trained models as a starting point.
Accents, dialects, regional slang and industry- or company-specific jargon can also be difficult for less advanced NLP solutions to interpret.
For example, if a company wants its field service teams to be able to talk to their devices to get accurate repair guidance, the NLP model will need to understand specific technical terms, as well as how individual engineers use or abbreviate them.
And while some individuals are accustomed to talking to digital assistants, others can find the act of conversing with a computer — especially in a customer service scenario — to be a source of frustration.
How is it being used?
Most of us use some form of NLP every day to do things like online searches, spell-checking, and language translation. Enterprise use cases include customer service chatbots that deliver empathetic, personalized interactions based on the context of the conversation.
Conversational applications — like voice-activated virtual assistants used on smart speakers — rely on NLP to identify and contextualize speech and respond appropriately.
Elsewhere, NLP has been used to screen resumés to identify ideal candidates, and recruiters have even used NLP to craft stronger job descriptions that will appeal to the right applicants.
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