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.