Artificial intelligence (AI) vs. natural language processing (NLP): What are the differences?

Artificial intelligence (AI) vs. natural language processing (NLP): What are the differences?

What is natural language processing (NLP)? How does it differ from AI? Let’s discuss this branch of artificial intelligence in plain terms and look at problems it can solve

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AI vs. NLP

NLP use cases

While NLP may get the most attention in consumer applications today, it has significant implications for organizational IT. “Understanding language and communication in general is huge for the enterprise as we spend most of our day communicating in one form or another,” Butterfield says.

Think chatbots, social media feeds, emails, or complex documentation like contracts or claims forms.

Any area of the business where natural language is involved may be fodder for the deployment of NLP capabilities, says Vijayan. Think chatbots, social media feeds, emails, or complex documentation like contracts or claims forms.

“NLP is typically deployed to categorize content, extract content, analyze sentiment, summarize documents, translate languages, deploy voice-driven or chat-driven interfaces, etc.,” Baritugo adds.

The discipline of NLP may be broken down into any number of more discrete NLP tasks, based on the enterprise’s need. Some may involve serious AI, while others are more rules-based. 

Those tasks then combine to create NLP capabilities like content categorization, contextual extraction, sentiment analysis, document summarization, or speech-to-text and text-to-speech conversion, as examples.

The challenges of enterprise NLP

NLP applications come with the same risks of failure as any other AI deployments, says Vijayan: Most notably, they can suffer from inflated expectations, unclear business cases, and lack of training data. Additionally, NLP opportunities may require entirely different training sets depending on the language being processed and the context, Vijayan says. You may need one set of training data when creating an NLP-enabled solution for processing contracts and entirely different data when coming up with a solution to answer payroll queries, for example.

Butterfield points out some of the hurdles NLP must overcome: Interpreting the actual meaning of voice or text correctly, dealing with sarcasm, understanding local dialects, parsing multiple potential intents, and generating bespoke responses, to name just a few.

Can’t picture where NLP will fit into your organization’s work? Consider this: “NLP is everywhere, and [is] much farther-reaching than the more recently developed smart assistants,” Keiland Cooper, director at ContinualAI and a neuroscience researcher at the University of California, Irvine, recently told us. “Everything from search, email spam filtering, online translation, grammar- and spell-checking, and many more applications [use NLP]. Any machine learning that is done involving natural language will involve some form of NLP.”

[ What’s next in AI and NLP? Read 10 AI trends to watch in 2020. ]

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