This is part one of a three-article series into natural language processing (NLP) and modern chatbot applications.
What is natural language processing?
As computer technology evolves beyond their artificial constraints, organizations are looking for new ways to take advantage. A sharp increase in computing speeds and capabilities has led to new and highly intelligent software systems, some of which are ready to replace or augment human services.
The rise of natural language processing (NLP) is one of the best examples, with smart chatbots ready to transform the world of customer service and beyond.
NLP is a branch of artificial intelligence (AI) used to help computers understand natural human language.
While computers have always been highly useful for abstract tasks involving quantification, flesh-and-blood human beings have always presented a challenging interface.
Even though computing systems enable fast and highly accurate communication channels, machines have never been good at understanding how and why we communicate in the first place.
NLP is dedicated to understanding the interaction between computers and machines through the medium of language.
In order to make sense of natural language, computers need to listen, process, and analyze human text and speech.
Recognition of language is not enough, however, working systems also need to create value through real world applications.
Machine learning has played an important role in the evolution of NLP systems.
Rather than programming everything from scratch…
Machine learning gives computers the ability to automatically learn and improve from experience.
This is very important for things like chatbot training because it allows developers to focus on useful outcomes and applications rather than explicit instructions.
Rather than programming a chatbot to answer every possible question and permutation…
Machine learning involves a deeper technique that involves real-time training through the act of conversation.
Trained chatbots can be used to add value to customer service, the ordering process, and big data analytics.
In addition, fundamental meaning is sometimes applied to improve overall language efficiency.
Fundamental meaning helps bots to understand individual isolated words in order to define intent and allocate importance to individual terms. When each conversation is broken down, the chatbot can understand what the user wants and how to help them.
Along with understanding text terms, NLP can also work with the human voice. In this context, processing systems combine speech to text (STT) capabilities with natural language understanding (NLU) and text to speech (TTS).
Exciting business applications for NLP
If you’ve ever used smart device or home technology such as Apple Siri or Google Home, then you’ll already be familiar with the wonders of NLP.
NLP has been successfully used in several commercial and non-commercial applications, including:
Customer service chatbots
While this technology first gained a foothold in the modern home, more and more businesses are taking advantage of NLP technology:
A virtual assistant (think Siri or Alexa) is an open-ended and user-oriented software system designed to interact with human beings in real time. These assistants understand voice commands and create value by competing specific tasks. While virtual assistants are basically chatbots, they have a more open-ended design.
A dedicated conversational chatbot or Messenger chatbot is designed to interact with humans via text. This technology is often seen on websites, with bots used to guide users towards information or customer service operators. Text is a primary method of communication, with text communication able to reach most consumers and text analysis able to interpret significant customer trends.
NLP has become significantly advanced enough to understand human speech patterns, with a voice-activated chatbot able to interact with people in real-time. While the technology used for virtual assistants and voice chatbots is almost identical, chatbots are typically limited to particular applications.
The evolution of NLP engines and applications
NLP engines are becoming more advanced and accurate all the time.
Whether they work with voice or text communications, commercial chatbots that implement NLP are able to perform an increasingly wide scope of operations.
Modern NLP engines offer numerous advantages when it comes to deriving meaning from user input, with the intent of the user recognized and important details extracted & contextualized to create value.
In order to understand NLP, it’s important to have a basic understanding of what the engine is attempting to do when a user types a query or speaks with chatbot software:
NLP engines utilize machine learning and fundamental meaning to identify the entities or isolated elements necessary to make sense of the user intent.
Utterances are analyzed and key entities are extracted, including locations, dates, times, and other important keywords.
NLP software engines need to have a thorough understanding of human vocabulary and grammar. Proper nouns are capitalized to signify meaning, vocabulary is expanded and transferred to enable understanding, and nouns and verbs are processed based on their nature and tense.
Once the language elements are known, important data is given context and shared throughout the communication session.
To be continued…
Part 2 of 3 coming soon!
If you’re ready to take advantage of this brave new world, Automated Dreams provides leading NLP consulting services to help you prosper and grow.
From customer service and order processing through to analytics, the clever use of chatbots can streamline your operation and get you ready for the future!