Chatbots are becoming part of many aspects of business and life and there is an interest in making them more human-like. A natural language processing chatbot is capable of such interactions. Therefore, they have been the subject of significant research and development.
With a natural language processing chatbot, users can write or even talk to the app, much like they would with another human.
Without natural language processing (NLP), chatbots can only respond to conversations in a scripted way. So, non-NLP chatbots are often called “Scripted Chatbots.” They are less costly to develop but not as sophisticated.
NLP apps are becoming mainstream; Siri, Cortana, Amazon Alexa and Google home are some examples. Consequently, businesses need to get into this trend and start thinking about how they can use NLP chatbots.
Would you like to know how NLP chatbots can give your customers an improved conversational experience? Read on.
What Is Natural Language Processing (NLP)?
Natural language processing (NLP) is a subfield of Artificial Intelligence (AI) aimed at making computers read, interpret and even write human language. Likewise, NLP also draws from computer science and linguistics.
Powered by NLP, computers can read and analyze thousands of written language pages in milliseconds.
Natural language processing is behind many AI apps and online services that you are already using today. For example:
- Search engines, for instance, Google and Bing.
- When you ask your favorite virtual assistant app (E.g., Siri, Google Assistant, Cortana) for directions.
- Automated language translation, as NLP enables computers to read not one, but as many human languages as you program it to.
- Analyze, find grammatical and style errors, and auto-correct written text. For instance, when writing a word document or email.
- When you call your bank and tell the IVR what you want to do, instead of selecting menu options.
- On email spam filters.
- Extraction and summarization information in order to write summaries and reports.
- The computer-assisted search of specific phrases on legal documents and contracts.
What Is an NLP Chatbot?
NLP Chatbots use Natural Language Processing (NLP) to understand the intent behind phrases and conversations made by a human.
Unlike Scripted Chatbots, which are menu-driven and can only handle scripted responses, a natural language processing chatbot can have a conversation using phrases and words.
Natural language processing allows a chatbot to:
- Read or listen to natural language.
- Identify spelling and grammatical errors and interpret the intended message despite user mistakes.
- Understand intent, for example, if you are asking a question or making a statement. Then, it can tailor its response.
- Understand the sentiment behind messages. For instance, determining if the user is on friendly terms or if he is angry. This allows the chatbot to adjust its answers and actions.
At a most basic level, an NLP Chatbot could assist you by giving better search results when shopping online. For example, you could ask for a particular product and the chatbot would find closing matching products.
More advanced NLP chatbots can use knowledge bases to answer frequently asked questions. For instance, chatbots can handle questions about refund policies or delivery information where the answer is always the same.
Also, NLP chatbots could determine that the customer has a complaint and is angry. Seconds later, a live rep could take over the conversation.
Can NLP Chatbots Give Businesses a Competitive Advantage?
In human conversations, intonation and body language give meaning to words that go beyond what the person is saying. Likewise, many things can go wrong when conveying a message; for instance misspellings and words that inadvertently change the tone of the conversations.
Humans are very good at taking all these cues into account to interpret the message. Still, It is a very challenging task for computers.
Natural language processing is, without a doubt, an incredible feat. As a result, it poses a significant opportunity for all kinds of businesses.
Companies that master NLP technology has the potential of gaining significant competitive advantage. Above all, with NLP you can service your customers 24/7 with human-like interaction, setting you apart from your competitors.
Is a Natural Language Processing Chatbot a Machine Learning Chatbot?
In the beginning, researchers developed NLP applications using rule-based approaches. Predefined sets of rules are unable to deal with unscripted conversations, thus it became a limitation to early NLP applications.
Decades later, during the 1980s and 1990s with the advent of Machine Learning, researchers developed statistical NLP, which is capable of considering many possible answers to problems and determine the best one.
Machine Learning powered NLP is great for handling variations of words, phrases, and even to account for misspellings. Consequently, as technology improved over time, new commercial applications of NLP began entering the tech market.
More recent research has focused on unsupervised and deep learning. Deep learning combines the use of Machine Learning statistical models with Neural Networks. As a result, Natural Language Processing becomes human-level accurate.
It is essential to point out that Natural Language Processing is not the same as Machine Learning; neither is a subfield of it. Instead, both are branches of Artificial Intelligence (AI) that can work in unison. When they do, NLP Chatbots get the ability to handle all but the most challenging language issues.
Does NLP Allow You to Talk to a Chatbot?
Chatbots can communicate with humans using voice when they use speech recognition technology. It allows a computer or program to identify words and phrases in spoken language. Once identified, it converts them into a form that the machine can read and process.
Speech recognition involves acoustic and language modeling algorithms. Acoustic modeling matches linguistic units with audio signals. In addition, language modeling matches sounds with words sequences to distinguish between similar-sounding words.
NLP Chatbot Example
Let’s exemplify the workings of an NLP Chatbot. Consider the following interaction with the 1-800-Flowers assistant.
Look at how the chatbot:
- First performed a general product search when the customer told it that he was looking for an anniversary gift for his wife.
- Then, the user said to it that he was looking for a particular product (roses). In conclusion, the chatbot was able to discern the word “roses” from the phrase, and made a more specific product search, giving the customer suitable options.
The 1-800-Flowers Assistant was one of the first chatbots with NLP capabilities. It’s a prime example of the possibilities of this technology.
How Does a Natural Language Processing Chatbot Work?
A natural language processing chatbot interaction follows a series of steps. Firstly, it reads the message, after that, it discerns its meaning and takes some action.
A typical interaction between humans and natural language processing chatbot goes as follows:
- The human writes the message or talks to the chatbot.
- If the message is in audio form, the machine converts audio to text.
- NLP uses algorithms to break down language into shorter pieces for better analysis. Then, it explores the relationships between them and how they work together.
- Based on language analysis, the program performs a predefined action, such as: Search a database, search a knowledge base, select menu options, look for an order status, among many possibilities.
- The chatbot constructs the response in written form.
- If required, the machine converts text to audio.
- The chatbot sends the answer to the user.
In most development tools, intents and entities are the basic constructs of NLP chatbots.
NLP Chatbot Intents
The Intent is the final aim of the user. For example, if he or she asks, “Show me yesterday’s entertainment news,” he intends to retrieve a list of entertainment headlines.
Intents have names, often a verb or noun. When designing a chatbot, the developer needs to identify as many intents as possible.
The chatbot intents determine the capabilities or services to end-users that features the chatbot.
When processing the user natural language message, the NLP engine will attempt to match the words or phrases with one of those intents. If the engine is unable to do so, the chatbot will tell the user that it doesn’t understand him.
If the intent match is successful, the engine will perform a predefined action. For instance, searching the web, searching a database, selecting a menu option among others.
NLP Chatbots Entities
Entities change the Intent. For instance, in the phrase “Show me yesterday’s entertainment news”, the entities are “Yesterday” and “Entertainment.”
These modify the final result. In this case, the chatbot will Show News, from yesterday and entertainment sections only.
Think of intents as the action or query that the computer will do and entities as its filters or parameters. Both of them can be discerned from language.
Take Action Now and Get a Natural Language Processing Chatbot for Your Business
Commercial NLP chatbots platforms have been entering the markets for some time now. Nevertheless, natural language processing chatbots still have some way to go.
As more research is carried out, more breakthroughs will come. Consequently, early adopters will have a clear advantage in attracting more customers to this new experience.
Players like IBM Watson, Microsoft Bot Network and Amazon Lex offer software as a service (SaaS) for NLP chatbot development and operations. As a result, barriers have come down to chatbot adoption by businesses.
Check out our post about 10 Free Chatbot Platforms For Superior Customer Engagement with some examples of chatbot development tools.
Are you ready to take your customer’s interactions to the next level with a natural language processing chatbot? Then, take action now, try one of these services or find a tech company partner to develop a chatbot for you.
What do you think about natural language chatbots? Are you planning to integrate chatbots into your business? Post us a comment below.
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Reference / Further Reading
Bernard Marr. 5 Amazing Examples Of Natural Language Processing (NLP) In Practice
Casey Phillips. Natural Language Processing (NLP) & Why Chatbots Need it
Dan Shewan. 10 of the Most Innovative Chatbots on the Web. Published in Wordstream
Paul Boutin. Does a Bot Need Natural Language Processing?
Dr. Michael J. Garbade. A Simple Introduction to Natural Language Processing
SAS Insights. Natural Language Processing (IoT). What it is and why it matters
