Enhancing Customer Experience with Natural Language Processing
ModelingNLP/Text AnalyticsNatural Language Processingposted by Andrew Prokop September 18, 2017 Andrew Prokop
Processing language into actionable components is the future of communication.
If you talk to a man in a language he understands, that goes to his head. If you talk to him in his language, that goes to his heart.
— Nelson Mandela
I would venture to guess that most people had their first encounter with natural language processing (NLP) when Apple added Siri to the iPhone. Starting with the iPhone 4S, you could ask “her” simple questions such as “Who was the 12th president of the United States?” (Zachary Taylor) and “Will you marry me?” (We hardly know one another). Personally, I use Siri on a near daily basis for getting me to where I need to go and finding the best Indian, Thai, or Mediterranean restaurant once I arrive there.
Of course, NLP isn’t limited to iPhones, today. You can now talk to your Android devices, and contact centers are increasingly adding automated “Tell me what you are calling about” functionality. It’s not out of the realm to envision a world where typing becomes as old fashioned as rotary telephones and stick shifts.
The Basics of Natural Language Processing
To understand NLP, it’s important to know what’s going on underneath the covers. While a detailed look at NLP is beyond the scope of this article, there are a few simple concepts that should supply most people with enough knowledge to consider themselves dangerous.
First, there is the intent. As it implies, intent is the intention conveyed by the user. For instance, “weather” is the intent of the question, “Will it rain today?”
You can classify intents into two groups. Casual intents are like small talk. Greetings such as “hello” and “goodbye” are casual intents. If I say “Hi” to a text bot, an appropriate response might be “What can I do for you today?” The same can be said for affirmative and negative responses — “Yes,” “Thank you,” and “Not today” fall into the casual intent category.
The second group, business intents, correspond directly to the focus of the statement or conversation. “When will my package arrive?” would direct the NLP computer to return a date or send a tracking number.
The next big concept of NLP is entities. An entity is the metadata of an intent.
Like intents, entities come in multiple flavors. You can think of a nominal entity as a noun. For example, car is a nominal entity. So are city, book, movie, and person.
A named entity is more like a proper noun. Using my nominal entities above as examples, their named entity counterparts might be Chicago, “The Great Gatsby,” “Love Actually,” and, of course, “Andrew Prokop.”
Returning to my “Will it rain today?” question, if weather is the intent, rain, snow, and hail are valid entities.
Composite entities consist of a number of component entities. Size, color, brand, and category could be the component entities for a product details composite entity.
Once you’ve designed your intents and entities, the next step is to train your system. This requires you to ask a series of questions that the NLP systems might encounter. For a weather bot, you might enter the following:
“Will it be sunny today?”
“What are the chances of it raining today?”
“Is there snow in the forecast?”
“Do I need to bring an umbrella to work?”
These questions train the system to the many ways that a user might ask for the same information. A simple rule for training is that you can never provide the system with too much data. Additionally, training is not a once-and-forget operation. A well behaved NLP system must be trained and retrained throughout its entire lifecycle.
NLP for the Masses
I learn best by doing, so I was overjoyed when I was told about Facebook’s wit.ai NLP engine. Wit.ai is a free cloud service that provides developers with an easy-to-use console to create intents and entities, and then train them into an “application.” I put application in quotes because a wit.ai application isn’t an application in the traditional sense. It’s more like an intelligent database of information that can be utilized by a “real” application to turn spoken and written languages into their actionable components.
Since wit.ai is a cloud service, that means you would use Web services to interact with it. While wit.ai exposes APIs to manage intents and entities, more importantly to the end-user application are the APIs that pass human speech to the service for processing.