What is natural language processing? NLP explained

What is natural language processing?

Natural language processing (NLP) is machine learning technology that enables computers to understand, interpret and generate human language. At its best, it gives machines the ability to comprehend written and spoken language with the same accuracy as humans. NLP contains two subfields:

• Natural language understanding (NLU) focuses on understanding human language and its intended meaning, factoring in grammatical errors, typos and so on.

• Natural language generation (NLG) focuses on turning structured data into language that appears as if it were created by a human.

NLP plays a major role in numerous business processes, including sentiment analysis, language modelling, text summarisation and speech recognition. Because organisations continue to receive higher and higher volumes of data, technology that helps process large amounts of information is critical.

How natural language processing works

NLP has surged in popularity recently, but its origins date back to the dawn of the digital era. Alan Turing’s landmark test for assessing machine intelligence, which he originally called the Imitation Game but is now called the Turing Test, based success on whether a computer could believably impersonate a human in real-time written communication back in 1950. Ever since the creation of computers, we’ve wanted them to understand us. With the ongoing developments in NLP, we’re at a point where computers can now accurately reflect and amplify human intelligence.

Those breakthroughs have been enabled by artificial intelligence and, more specifically, machine learning and deep learning. At Workday, we define those AI terms as follows:

• Artificial intelligence (AI) is the ability for machines to perform tasks traditionally seen as requiring human intelligence. AI analyses and learns from data, recognises patterns and makes predictions.

• Machine learning (ML) is a subdiscipline of artificial intelligence. ML models rely on data and self-modifying methods to identify patterns, make predictions and interpret data sets. Those models can then continuously refine themselves to generate stronger outcomes.

• Deep learning (DL) is a subset of machine learning based on artificial neural networks. These neural networks attempt to mirror the processes of the human brain by using multiple layers of algorithms. Data scientists use deep learning to model complex patterns and relationships within unstructured data.

NLP combines computational linguistics with machine learning methods and deep learning models. Using large data sets, NLP breaks language data into shorter chunks called tokens, separating the words in a sentence into distinct units. This process, known as tokenisation, is at the cornerstone of all NLP technology. Afterwards, the computer attempts to understand the relationship between these tokens to interpret the meaning and intent of the sentence.

What are natural language processing tasks?

NLP tasks are the rule-based methods that NLP technology uses to interpret the meaning of voice or text data. Also known as NLP techniques, these will vary between solutions. Here are five of the most common statistical methods NLP uses to best understand human language.

Part of speech tagging, also known as grammatical tagging, is the process that labels each word in a text. These labels cover basic grammar, including verbs, adjectives and nouns. In doing so, NLP tools can identify the relationship between the words in context.

Word sense disambiguation helps to avoid confusion when a word has multiple meanings. By carrying out semantic analysis, NLP is able to use context to determine which definition makes the most sense. For example, it can clarify whether the word “bark” refers to the sound a dog makes or a tree’s outer layer.

Sentiment analysis enables NLP solutions to understand more subjective words found in the text. Often these are words that carry an emotional weight when used in a specific context, such as anger or doubt. In doing so, NLP can determine whether the sentence is positive, negative or neutral.

Named entity recognition involves detecting words and phrases as unique names. Once NLP detects them, it sorts each name into categories, including people, places, companies and more.

Speech recognition turns voice data into text. Any product that uses vocal prompts, from your GPS to your smartphone, has to break words down into clear sounds. Ensuring this works across different accents and languages is particularly difficult.

Use cases for natural language processing

The modern world of work is full of new challenges, many of which are only solvable with AI and NLP. At Workday, we’ve already embedded NLP across our products, including our continuous listening platform Workday Peakon Employee Voice and our chatbot Workday Assistant. Here are just a few examples of business-practical applications of NLP.

Enable natural conversations with chatbots

More than ever, employees expect quick and accurate answers to their questions. Spending hours looking through guides or days waiting for a response from a human contact can lead to friction. That’s where NLP can enable natural conversations between a user and a chatbot to promote a more positive journey. Automated chatbots can help direct users to the right answers fast, ensuring they only escalate their issue when it’s necessary.

Summarise long portions of text

Sifting through large documents, email chains and employee comments can be time-consuming. Since NLP technology can infer contextual meaning, it can also succinctly summarise high volumes of language data. For example, in Workday Peakon Employee Voice, managers can view summaries of a variety of different topics. Our NLP software uses extractive summarisation to select portions of text from related comments, providing managers with top-level insights sourced directly from employee feedback.

Generate actionable topics from feedback

Employee feedback is essential to improve employee engagement, but identifying recurring topics from thousands of comments can be tedious. NLP automatically surfaces useful insights from employee feedback in real time and across multiple languages. Unlike other employee engagement technologies, Semantic Intelligence, our NLP software, creates topics unique to your company, avoiding the need to focus only on predefined categories or words.

Use semantic search to surface insights

Sifting through hundreds, thousands or even millions of comments for useful insights can be a daunting process. With our semantic search functionality, Workday Peakon Employee Voice users can surface relevant comments based on their search query. NLP factors in the contextual meaning of the search phrase to find suitable related comments. In that way, you can quickly identify what’s important to employees – in their own words.

What are the benefits of natural language processing?

As the potential business use cases for NLP continue to grow, so does the potential business value. Fortune Business Insights projects that the global NLP market will increase from $24.10 billion in 2023 to $112.28 billion by 2030. More importantly, the organisations driving that growth are already seeing major business benefits.

A Forrester Total Economic Impact™ Study commissioned by Workday analysed five companies that used Workday Peakon Employee Voice over three years. Each of these companies had identified employee experience as a major strategic initiative. Here are some of the major findings taken from their three-year analysis, given as an aggregate:

• 244% return on investment achieved

• $7.5 million savings due to improved attrition rate

• 4% improved attrition rate of frontline workers

• 504 hours saved by human resources (HR) employees in designing and distributing surveys

• 1,584 hours of manager time saved on survey analysis

• 75% of time saved for HR teams on survey design and distribution

• 50% of time saved for managers thanks to real-time survey results

Evidently, the benefits of an employee listening platform with NLP embedded at its core are far-reaching. By automating key aspects of the employee survey process, businesses can improve the experience for employees and their people leaders alike. For organisations looking to retain top talent, NLP is critical for quickly identifying important workplace issues, discovering the causes of turnover and digging deeper into employee sentiment.

What is the future of natural language processing?

According to The Workforce Institute, 75% of employees don’t feel heard when it comes to the important issues. These include diversity and inclusion, benefits, safety, time-off requests and manager effectiveness. Worse, 40% don’t have confidence that their feedback will lead to actionable change. Looking to the future, businesses that don’t have NLP embedded across different functions will face increased attrition and burnout.

At Workday, we believe the organisations that succeed moving forward will be those that listen to their people. That’s why NLP is at the heart of our continuous listening platform, Workday Peakon Employee Voice. With NLP, you can surface employee insights when they matter, driving meaningful change at every level of the business.

The modern world is defined by sudden upheavals and transformations. To ensure your business keeps pace with change, it’s essential that you’re able to understand and cater to employee needs. Our NLP technology provides people leaders with workplace trends and actionable information, delivered in employees' own words. By tackling any issues at their source, you create lasting change across the employee experience – and ultimately, a thriving employee culture.

For more information on how Workday is driving innovation in our NLP solutions, read about our advancements with AI.