What is Natural Language Processing?
Put simply, Natural Language Processing (NLP) is a technology used to enable computers to understand human language in the same way that we, humans, do.
NLP is a subfield of Artificial Intelligence (AI) that merges linguistics and computer science in order to interpret human language by breaking it into words, deriving context from the relationship between words, and structuring this data to extract the meaning.
Why is Natural Language Processing important?
Let’s talk about language
As humans, we use natural language as an everyday means to communicate with one another. We do so thanks to our innate ability to understand, process, and utilize words in one or various languages.
Although all natural languages comply with unique rules, such as syntax and grammar, there are sometimes ambiguities. Natural languages also differ from artificially created ones, like computer programming languages.
Helping computers understand humans with NLP
Because natural languages have not been ‘designed’ in the same way that formal languages have, they tend to have many ambiguities. The same word, phrase or even an entire sentence can have multiple meanings, and one concept may be expressed in multiple different ways.
That’s where Natural Language Processing comes in. Thanks to theoretical linguistic frameworks like the meaning-text theory (MTT), NLP enables computers to process language and understand the meaning behind the words, thus filling the gap between machines and humans.
Understand language in all its forms and complexity
With the help of NLP technology, computers now can automatically handle natural human languages like speech or text, and even go further by understanding it in all its forms and complexity.
Natural Language Processing enables:
✓ Spell correction
✓ Context understanding
✓ Intent detection
✓ Sentiment analysis
How does Natural Language Processing work?
As explained before, NLP extracts meaning by breaking the language into words and deriving context from the relationship between these words. The data is then indexed and segmented into specific groups or classes with a high degree of accuracy.
The indexing process can be further broken down into stages:
Breaking down text into smaller semantic units or single clauses.
Marking up words as nouns, verbs, adjectives, adverbs, pronouns, etc.
Stop word removal
Filtering out common words that add little or no unique information, such as prepositions and articles
(at, to, a, the).
Stemming / lemmatization
Transforming words to their root form and assessing the context of word use.
Once all these stages have happened, language has been transformed into a structure that the computer is able to understand. If you’d like to understand how these different stages happen, we explain the process in more depth and how Inbenta NLP finds answers here.
Why is Inbenta NLP better than its competitors?
Inbenta’s approach: Neuro-Symbolic AI
Inbenta Natural Language Processing technology is powered by Neuro-Symbolic AI which combines symbolic reasoning with a few layers of machine learning, bringing the best of both technologies together.
The matching process is not keyword-oriented or statistical but based on semantics. Coupled with our own exhaustive Lexicon, Inbenta Customer Interaction Management Platform is able to understand the meaning of users’ queries even when the questions are incomplete, ambiguous, or unstructured.
Thanks to these three key elements – NLP, Neuro-Symbolic AI, and Lexicon – Inbenta solutions efficiently help users find accurate answers to their questions, without any lengthy data training.
NLP powered by Machine Learning
Most conversational AI solutions on the market today use machine learning to power their Natural Language Processing technology.
Machine learning uses a statistical approach to solve users’ queries. This means that the solution needs to be trained with a lot of data, i.e. different phrasings and utterances of customer requests, for the algorithms to statistically decide how to answer a specific question.
At Inbenta, our main matching technology is based on semantic matching, which we can complement by combining it with some machine learning matching. This enables our NLP engine to analyze and learn from users’ behavior in order to display the most popular contents first.