When you ask Siri for the weather or your chatbot understands your question, NLP is at work. This technology enables machines to understand and interact with human language.
In this guide, discover what NLP is, how it works, and its practical applications in customer service.
Natural Language Processing (NLP) is a branch of artificial intelligence that enables machines to understand, interpret, and generate human language.
NLP bridges the gap between human language (ambiguous, contextual, full of nuances) and computer language (binary, logical, structured).
Thanks to NLP, you can:
NLP breaks down language processing into several steps:
The text is divided into basic units: words, sub-words, or characters.
Example: "Hello, I would like a refund" → ["Hello", ",", "I", "would like", "a", "refund"]
Each word is identified: is it a verb, a noun, an adjective? What is its base form (lemma)?
The structure of the sentence is analyzed: subject, verb, complement. How are the words connected to each other?
The meaning of the sentence is extracted. What does the text mean? What is the intention?
Context is taken into account. The same word can have different meanings depending on the situation.
Determine whether a text expresses a positive, negative, or neutral sentiment. Very useful for analyzing customer reviews or detecting dissatisfied customers.
Automatically categorize text: spam/non-spam, type of request, urgency, etc.
Identify key elements: names of people, companies, dates, amounts, order numbers, etc.
Condense a long text while retaining the essential information.
Convert text from one language to another.
Produce coherent text based on instructions or context.
Understanding a question and answering it based on a knowledge base.
NLP is at the heart of customer service transformation. Here are some practical applications:
NLP enables chatbots to understand customer questions, even when phrased in unusual ways, and provide relevant answers.
Automatic content analysis to route the ticket to the right team or agent, without manual intervention.
Identify dissatisfied customers or urgent situations to prioritize handling.
Provide pre-written responses or relevant FAQ articles to the agent.
Extract insights from thousands of conversations: recurring themes, emerging issues, opportunities for improvement.
Enable a French agent to respond to a Spanish customer, with automatic translation on both sides.
Klark uses NLP to analyze each request, understand the customer's intent, and instantly provide the best response.
Manually defined rules: "if the message contains 'refund' then categorize it as 'Billing'."
Advantages: precise within a defined scope, explainable.
Limitations: rigid, does not handle variations, heavy maintenance.
Algorithms learn patterns from examples: Naive Bayes, SVM, Random Forest.
Advantages: more flexible than rules, learning from your data.
Limitations: requires labeled data, limited performance on complex language.
Deep neural networks, particularly the Transformer architecture (the basis of LLMs), have revolutionized NLP.
Advantages: exceptional performance, understands nuances and context.
Limitations: requires computing power, less explainable.
Three similar but distinct acronyms:
NLP encompasses NLU and NLG. A chatbot uses NLU to understand the question and NLG to formulate the response.
"I'm going to see my mother at the bank" → financial bank or school of fish? Context is essential.
"It's raining cats and dogs" doesn't really refer to cats and dogs. Figurative expressions are challenging.
Every language has its rules, exceptions, and subtleties. NLP must adapt.
Spelling mistakes, abbreviations ("hi," "pls"), emojis... Real language is far from perfect.
"It's cold" can be a complaint or a simple observation depending on the context of the conversation.
Automatic categorization? Sentiment detection? Chatbot? Start with a specific case.
NLP needs data to learn. Your historical tickets are a gold mine.
Rules for simple and stable cases, ML/Deep Learning for complex and variable cases.
NLP is never perfect the first time around. Measure, identify errors, improve.
For ambiguous or sensitive cases, always allow for human intervention.
NLP is a branch of AI that specializes in language processing. AI is the broader field that also includes computer vision, robotics, etc.
It simulates understanding by identifying statistical patterns. It is not "true" understanding like a human, but it is often sufficient for practical applications.
It depends on the approach. Pre-trained models (LLMs) work with little specific data. Custom models require more training data.
Yes, but historically, English models perform better. The situation is improving with multilingual models and French initiatives (CamemBERT, Mistral).
NLP (Natural Language Processing) is the technology that enables machines to understand and communicate with humans. For customer service, it is a major driver of automation and experience improvement.
The keys to using NLP effectively:
Want to leverage NLP in your customer service? Discover how Klark can help you.
