Natural language processing slow to reap benefits of AI enthusiasm
By most accounts, the top technology trend for this year is artificial intelligence, but one facet of AI has has been slow to take off is natural language processing.
Natural language processing (NLP) is a component of artificial intelligence that enables computers to understand, interpret and manipulate human language, continues to advance as organizations look for ways to leverage human-to-machine communications for a variety of applications.
To date, the application of natural language technology in the enterprise has been underwhelming, said Leslie Joseph, principal analyst at Forrester Research. But this is likely to change, he said. Deep learning techniques for NLP, such as distributed representations, are improving both the generation as well as the understanding ends of NLP.
“This will allow a better representation of complex word and sentence structures, emotion, and sentiment,” Joseph said.
Chatbots have commanded the lion's share of attention from enterprises looking to leverage NLP, Joseph said. “However, chatbots largely failed to make a big splash because of the limitations of technology,” he said.
“As we go forward, enterprises will look to leverage NLP less as a tool for customer conversations and more as a way to drive deep analytics from unstructured data such as documents, contact center conversations etc.,” Joseph said.
NLP will come embedded in several forms of enterprise applications and processes such as business intelligence and robotic process automation.
“Conversations will come back in a couple of years, but the underlying technology needs time to get better for that to happen,” Joseph said.
There has always been a strong latent desire to be able to talk to computers, Joseph said.
“This continues to be a 'cool' goal,” Joseph said. “NLP is interesting. It is exciting. And after years of hype, it feels as though it is at the cusp of a minor technological breakthrough. Smart home devices are proliferating, as are mobile assistants. So we have a greater availability of data, but also computational techniques are also improving. The future of NLP is promising.”