Natural Language Processing (NLP)

What is Natural Language Processing (NLP)?

Natural Language Processing (NLP) is a software capability that allows a computer to read, interpret, understand, and provide meaningful responses to natural human language.

NLP is a developmental area of artificial intelligence (AI) technology, which focuses on teaching computers to process information and solve problems in ways that replicate, or even exceed, human intelligence. 

NLP applications combine AI and machine learning (ML) technologies with deep-learning and rule-based language models. By leveraging these technologies, NLP software can process human language (in both spoken and textual formats), recognize the speaker’s intent or sentiment, and deliver meaningful responses that help the speaker accomplish their goals.

How Does Natural Language Processing Work?

To understand how NLP works, let’s look at a hypothetical example of how a B2B SaaS customer support call center might develop an NLP application to automate part of its customer service function. 

NLP technology is complex, and our example is necessarily oversimplified, but we hope to convey the general steps involved in programming and deploying a customer-facing NLP application.


Example 1: NLP Technology for Customer Service

Step One: Data Collection

NLP solutions depend on complex algorithms developed with machine learning. Machine learning requires a large set of training data to be effective, so the first step is to identify a source of training data that can be used to support our NLP application. 

A call center would have access to hundreds, or even thousands, of recorded customer support calls. These recordings could easily be used to train a human customer service representative, as they exemplify how existing reps understand and respond to customer queries. By the same token, they may also serve as a valuable data source for training an NLP software application.

Step Two: Data Pre-processing

Data pre-processing involves translating audio data into text and formatting it in a way that’s easier for machines to analyze. Data pre-processing for NLP training datasets may include steps like:

  • Tokenization – A process that deconstructs complete sentences or text into smaller units that may be easier and faster to process.
  • Stop Word Removal – A process that removes common or meaningless words from text, such that the remaining words convey the most important information in the text.
  • Stemming – A process of reducing words to their root forms such that words in the same family are processed in the same way.
  • Tagging/Categorization – A process where words or phrases are labeled according to various characteristics (e.g.part-of-speech, sentiment association, intent, etc.)

For our customer service NLP example, the call center might want to tag or label certain customer phrases by their intent, allowing the machine learning algorithm to easily recognize many different ways that customers might be asking the same questions.

Step Three: Algorithm Development

Once the natural language data has been pre-processed, it will be fed into a machine-learning algorithm as training data for the NLP application. 

Machine learning systems learn to perform a task by automatically developing algorithms based on the training data they receive. The more training data you provide to a machine learning system, the more it will refine its methods and adjust its algorithm for processing the data and returning a useful response. 

For our customer service NLP example, the end goal is to have the software application take the place of a live customer service representative to answer basic questions or triage requests. 

Step Four: Testing

Once the training data has been fully processed by a machine learning system, the resultant algorithm can be tested by simulating customer service interactions and assessing whether the responses generated by the NLP algorithm are helpful and meaningful. 

Step Five: Deployment

When the algorithm shows a high success rate at delivering meaningful responses to customer inquiries, it can be deployed in a customer-facing context. Customers will ask their questions through the user interface and the NLP algorithm will process the questions and deliver meaningful responses that help the customer accomplish their goals.

Natural Language Processing (NLP) Tasks for Artificial Intelligence

NLP technology has existed since the first chatbots were developed in the 1950s, but the recent rise of big data and hyper-scale computation has advanced NLP significantly over the past decade. We now see NLP technology deployed by everyone from enterprise technology to start-up SaaS companies.

Below are just a handful of the many functions that can be fulfilled by NLP technologies:

Speech Recognition

Speech recognition, or speech-to-text, is a software function that converts voice data into a textual format. Speech recognition is used in applications that can follow voice commands or respond to questions from users, including mobile phones and home automation products like Google Nest and Amazon Echo.

Machine Translation

Machine translation is the automatic translation of speech or text into a different language.


Word sense disambiguation is an NLP function used to identify the contextual meaning of a word that can have multiple meanings.

Document Summarization

NLP applications can be used to process and summarize documents for users who need the insights, but don’t have time to consume and understand all of the source material.

Contextual Extraction

Contextual extraction is an NLP function that extracts structured data from unstructured, text-based sources.

Natural Language Generation

Natural language generation (NLG) takes structured data and expresses it using human language, usually in a conversational format. Chatbots use both NLP and NLG – first, to understand users, and then to reply and answer their questions.

Natural Language Processing (NLP) Use Cases in SaaS Marketing

Social Media Sentiment Analysis

NLP-driven software programs can be used by SaaS companies to monitor social media channels and measure user sentiment across a variety of topics. Sentiment analysis can help SaaS marketing teams understand customer pain points, measure the response to new trends,  collect real-time feedback from customers, and see how audiences are engaging with their competitors. 

Marketing Chatbots

Marketing and customer service chatbots use NLP and NLG to communicate with prospective customers and provide service or generate leads and demos in real time. Companies like Drift, Intercom, and Zendesk have productized chatbots for marketing and customer service, making them easy for companies to implement with a rules-based approach and minimal technical knowledge.


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