The 5 Steps in Natural Language Processing (NLP)

Step #1: Lexical analysis

Text preprocessing in NLP precedes lexical analysis as it undergoes cleaning, tokenizing, and structuring raw data to make it readable for NLP models.

A study emphasizes that understanding grammatical structures and keywords is vital for chatbots to provide coherent and relevant responses in real-world situations. Lexical analysis helps NLP do so, making it one of the most important natural language processing steps.

It converts a sequence of characters into a sequence of tokens. This tokenization in NLP deciphers and segments languages into units (lexemes), such as sentences, phrases, and words.

Some words can stand alone, like “cat” or “jump”. These are free morphemes because they don’t need any extra help to be complete words. However, then there are bound morphemes, which have to latch onto something else to make sense. For instance,  “replaying”:

• “re-” (a prefix meaning “again”)
• “play” (the root word that holds the meaning)
• “-ing” (a suffix showing it's happening right now)

Neither of these words can exist alone, they always need a root word to stick to.

Machine learning in AI plays an important role in lexical analysis by helping NLP systems continuously improve their ability to identify and segment words accurately. When NLP analyzes text, it goes through a process to understand these layers, which helps understand word meanings, grammatical structures, and even how words change depending on context.

Lexical analysis teaches AI to deconstruct language the way the human brain does, but faster.

Step #2: Syntactic analysis

Syntax is the foundation of language, it’s how words and phrases come together to form meaningful sentences. Syntactic analysis (or parsing) lets NLP systems understand sentence structure and relationships between words rather than analyzing them in isolation.

This ensures that AI-powered chatbots, smart IVR systems, and voice assistants interpret user queries correctly.

When NLP performs syntactic analysis, it:

• Identifies sentence structure: Breaks a sentence into subject, predicate, objects, clauses, etc.
• Determines relationships between words: Determines which words depend on or modify others.
• Checks for grammatical correctness: Ensures the sentence follows valid language rules.

Syntactic analysis is important for chatbots and virtual assistants to improve customer experience with smart IVR. When a customer says, “I need help to reset my password”, the system uses syntactic analysis to recognize:

• “I” as the subject
• “need help” as the action
• “resetting my password” as the specific request

This structured understanding helps the AI respond appropriately, directing users to the right solution without confusion or misinterpretation.

Step #3: Semantic analysis

Semantic analysis in NLP deciphers meaning based on context, intent, and sentiment. The same word or phrase can imply different things depending on how it's used, and semantic analysis ensures AI comprehends these nuances accurately.

For instance, if a caller says, "I need to cancel my order because I wasn’t happy with the last one," a basic AI might fixate on the keyword "cancel" and proceed with the request.

But a Plivo-powered voice agent with semantic analysis understands the full context. It detects dissatisfaction from "wasn’t happy with the last one" and responds empathetically:

• “I’m really sorry about your previous experience. Let’s make this right.”
• Apart from processing the query, it might offer a resolution: “How about a 20% discount on your current order as an apology?”

Semantic analysis helps voice agents understand natural conversations instead of just keywords. This context-aware approach makes customer interactions more human, but you can also use these voice transcriptions for customer analytics to make informed decisions.

Step #4: Disclosure integration

Discourse integration helps NLP models grasp the broader meaning of conversations by considering previous sentences for context. This ensures more accurate interpretations and reduces confusion in AI-driven interactions.

For example, in a customer support chat:

Caller: "I lost my credit card yesterday."

Bot: “I’m sorry to hear that. Would you like to report it as lost or request a replacement?"

Caller: “Cancel it.”

Without discourse integration, the AI might struggle to understand whether the user wants to cancel the report or the card itself. By analyzing the conversation history, it correctly determines that "cancel it" refers to the lost card and proceeds accordingly.

This is important for businesses to automate customer interactions, and ensure smarter, context-aware responses in finance, healthcare, and beyond.

Step #5: Pragmatic analysis

Pragmatic analysis interprets meaning based on context, tone, and real-world knowledge rather than just literal word definitions so that the conversations feel natural and relevant. For instance, when a user asks, "What's the price of your premium subscription plan?"

The bot responds with, "Sure, our premium starts at $50 per month.”

Now, if the user simply responds, "That's a little steep," the bot automatically suggests, "Would you like to explore our standard plan starting at $30?"

While the user doesn't explicitly mention they want to explore a lower-priced option, pragmatic analysis recognizes the implied concern and suggests an alternative.

Businesses benefit from this by providing better customer engagement, fewer misunderstandings, and more effective automation.

While the steps in NLP help make customer experience better, it’s not all pros.