What is natural language processing?
In computer science, natural language processing (NLP) is the ability of artificial intelligence (AI) products and services to add context and derive meaning from human speech or written text, using statistical methods and machine learning algorithms.
While more basic speech-to-text software can transcribe the things we say into the written word, things start and stop there without the addition of computational linguistics and NLP. Natural language processing goes one step further by being able to parse tricky terminology and phrasing, and extract more abstract qualities – like sentiment – from the message.
The way we speak and write is fascinatingly complex, but our brains are great at understanding the meaning and intent behind someone’s words – even if things are spelled wrong, come amid a flurry of ‘um‘s and ‘ah‘s, or are delivered in a roundabout way.
Natural language processing software can mimic the steps our brains naturally take to discern meaning and context.
How does Natural language processing work?
Whenever natural language processing attempts to find meaning in text or audio, there are a number of statistical methods, machine learning processes, and language detection tasks happening at once. Here are some of the common ones:
This is where human speech is converted into text. While natural language processing isn’t always required in this step, it helps with unraveling the disorganized way in which we sometimes speak. Note: NLP also works with text-first messages, not just speech.
Tagging and categorizing
As part of speech tagging, machine learning detects natural language to sort words into nouns, verbs, etc. This is useful for words that can have several different meanings depending on their use in a sentence. This semantic analysis, sometimes called word sense disambiguation, is used to determine the meaning of a sentence.
Name and entity recognition
These NLP tasks break out things like people’s names, place names, or brands. A process called ‘coreference resolution’ is then used to tag instances where two words refer to the same thing, like ‘Tom/He’ or ‘Car/Volvo’ – or to understand metaphors.
The program will then use natural language understanding and deep learning models to attach emotions and overall positive/negative detection to what’s being said.
What is machine learning?
Simply put, ‘machine learning’ describes a brand of artificial intelligence that uses algorithms to self-improve over time. An AI program with machine learning capabilities can use the data it generates to fine-tune and improve that data collection and analysis in the future.
What is Natural Language Generation?
Natural Language Generation, otherwise known as NLG, utilizes Natural Language Processing to produce written or spoken language from structured and unstructured data. The most common methods of NLG are extractive and abstractive.
An extractive approach takes a large body of text, pulls out sentences that are most representative of key points, and concatenates them to generate a summary of the larger text.
An abstractive approach creates novel text by identifying key concepts and then generating new sentences or phrases that attempt to capture the key points of a larger body of text.
Why is Natural Language Processing important?
Reading through all of this text is next to impossible: assuming that the average person can process 50 items of unstructured data an hour, it would take nearly seven years for one person to read through one million items. So how do you understand and learn from all of this feedback?
Natural Language Processing automates the reading of text using sophisticated speech recognition and human language algorithms. NLP engines are fast, consistent, and programmable, and can identify words and grammar to find meaning in large amounts of text.
Understanding the context behind human language
Most importantly: human language is complicated. And that means that computers need to work harder than we do to ensure that machine translation, speech recognition, and text data make sense.
As an example, can you spot the difference in sentiment between these two sentences:
“The service was outstanding.”
“I have an outstanding balance.”
You probably know, instinctively, that the first one is positive and the second one is a potential issue, even though they both contain the word outstanding at their core.
But without natural language processing, a software program wouldn’t see the difference; it would miss the meaning in the messaging here, aggravating customers and potentially losing business in the process. So there’s huge importance in being able to understand and react to human language.
What are the benefits of natural language processing?
Implementing software that can take advantage of machine learning methods can have huge benefits for businesses looking to streamline their customer support systems. Here are a few ways natural language processing (NLP) can lighten the load:
Computational linguistics and natural language processing can take an influx of data from a huge range of channels and organize it into actionable insight, in a fraction of the time it would take a human. Qualtrics XM Discover, for instance, can transcribe up to 1,000 audio hours of speech in just 1 hour.
Moreover, integrated software like this can handle the time-consuming task of tracking customer sentiment across every touchpoint and provide insight in an instant. In call centers, NLP allows automation of time-consuming tasks like post-call reporting and compliance management screening, freeing up agents to do what they do best.
Chatbots are a great way to allow customers to self-serve where possible, but if the bot in question can’t follow the conversation, you’ll only end up with angry customers.
Natural language processing and computational linguistics can make bots infinitely more capable, allowing them to speak with human-level understanding in any language, respond appropriately to positive or negative sentiment, and even derive meaning from emojis.
Curated customer service
Customer interactions aren’t always about a single topic. Thankfully, natural language processing can identify all topics and subtopics within a single interaction, with ‘root cause’ analysis that drives actionability. The same goes for different customer channels.
A fully-integrated experience management tool with natural language processing can scour everything from emails and phone calls to reviews on third-party websites, and learn where customers are finding friction – both on an individual basis and at scale – by analyzing human language.
Better call center management
For call center managers, a tool like Qualtrics XM Discover can listen to customer service calls, analyze what’s being said on both sides, and automatically score an agent’s performance after every call.
If they’re sticking to the script and customers end up happy you can use that information to celebrate wins. If not, the software will recommend actions to help your agents develop their skills.
When you’re automating customer service-related tasks through natural language processing, you’re collecting larger and larger human language datasets all the time, which makes it easier to analyze trends and perform historical analysis.
The speed of cross-channel text and call analysis also means you can act quicker than ever to close experience gaps. Real-time data can help fine-tune many aspects of the business, whether it’s frontline staff in need of support, making sure managers are using inclusive language, or scanning for sentiment on a new ad campaign.
How to bring NLP into your business
The best way to make use of natural language processing and machine learning in your business is to implement a software suite designed to take the complex data those functions work with and turn it into easy to interpret actions.
Experience management software tools like Qualtrics Experience iD and XM Discover make statistical natural language processing at scale useful to business managers by transforming vast quantities of customer service data and making it useful – with immediate results.
NLP: The Qualtrics way
We’ve developed a proprietary natural language processing engine that uses both linguistic and statistical algorithms. This hybrid framework makes the technology straightforward to use, with a high degree of accuracy when parsing and interpreting the linguistic and semantic information in text.
Powered by Clarabridge, Qualtrics’ technology uses a six-step, workflow-like process to identify and understand phrases, grammar, and the relationships among words, in a way that’s comparable to the way people assign meaning to things that they read. When paired with our sentiment analysis techniques, Qualtrics’ natural language processing powers the most accurate, sophisticated text analytics solution available.
Experience iD tracks customer feedback and data with an omnichannel eye and turns it into pure, useful insight – letting you know where customers are running into trouble, what they’re saying, and why. That’s all while freeing up customer service agents to focus on what really matters. Click below to learn more.