Have you ever wondered how Siri or Google Maps acquired the ability to understand, interpret, and respond to your questions simply by hearing your voice? The technology behind this, known as natural language processing (NLP), is responsible for the features that allow technology to come close to human interaction.
In this article, we will go through real-life examples of NLP and ways in which you can potentially implement this technology in your business to optimize your processes. Let’s dig in!
NLP - What, Why & How
Natural language processing (NLP) is a branch of Artificial Intelligence or AI, that falls under the umbrella of computer vision. The NLP practice is focused on giving computers human abilities in relation to language, like the power to understand spoken words and text.
NLP combines rule-based modeling of human language called computational linguistics, with other models such as statistical models, Machine Learning, and deep learning. When integrated, these technological models allow computers to process human language through either text or spoken words. As a result, they can 'understand' the full meaning – including the speaker’s or writer's intention and feelings.
Some of the top benefits of NLP include:
- Being able to perform large-scale data analytics
- Cost reduction and streamlining of various processes (otherwise performed by humans)
- Increasing customer satisfaction
- In-depth marketing understanding
Let’s look at some of the most common use cases, starting with online search capabilities:
Search Engine Results
An example of NLP in action is search engine functionality. Search engines leverage NLP to suggest relevant results based on previous search history behavior and user intent.
When using Google, for example, the search engine predicts what you will continue typing based on popular searches, while also looking at the context and recognizing the meaning behind what you want to say (as opposed to the literal words being typed). It might feel like your thought is being finished before you get the chance to finish typing.
This could look like putting in a math equation and having a calculator come up, or even typing a flight number and receiving the flight status breakdown.
Smart Search and Predictive Text
Features like autocorrect, autocomplete, and predictive text are so embedded in social media platforms and applications that we often forget they exist. Autocomplete and predictive text predict what you might say based on what you've typed, finish your words, and even suggest more relevant ones, similar to search engine results.
Autocorrect can even change words based on typos so that the overall sentence's meaning makes sense. These functionalities have the ability to learn and change based on your behavior. For example, over time predictive text will learn your personal jargon and customize itself.
One of the most common NLP examples is translation. In the 1950s, Georgetown and IBM presented the first NLP-based translation machine, which had the ability to translate 60 Russian sentences to English automatically.
Translation applications available today use NLP and Machine Learning to accurately translate both text and voice formats for most global languages.
Data analysis has come a long way in interpreting survey results, although the final challenge is making sense of open-ended responses and unstructured text. NLP, with the support of other AI disciplines, is working towards making these advanced analyses possible.
Thanks to NLP, you can analyse your survey responses accurately and effectively without needing to invest human resources in this process.
An NLP customer service-oriented example would be using semantic search to improve customer experience. Semantic search is a search method that understands the context of a search query and suggests appropriate responses.
For example, if you're on an eCommerce website and search for a specific product description, the semantic search engine will understand your intent and show you other products that you might be looking for.
There are many eCommerce websites and online retailers that leverage NLP-powered semantic search engines. They aim to understand the shopper's intent when searching for long-tail keywords (e.g. women's straight leg denim size 4) and improve product visibility.
On average, retailers with a semantic search bar experience a 2% cart abandonment rate, which is significantly lower than the 40% rate found on websites with a non-semantic search bar.
Oftentimes, when businesses need help understanding their customer needs, they turn to sentiment analysis.
Sentiment analysis (also known as opinion mining) is an NLP strategy that can determine whether the meaning behind data is positive, negative, or neutral. For instance, if an unhappy client sends an email which mentions the terms “error” and “not worth the price”, then their opinion would be automatically tagged as one with negative sentiment.
By performing sentiment analysis, companies can better understand textual data and monitor brand and product feedback in a systematic way.
Let’s look at an example of NLP in advertising to better illustrate just how powerful it can be for business. If a marketing team leveraged findings from their sentiment analysis to create more user-centered campaigns, they could filter positive customer opinions to know which advantages are worth focussing on in any upcoming ad campaigns.
Sentiment Analysis is also widely used on Social Listening processes, on platforms such as Twitter. This helps organisations discover what the brand image of their company really looks like through analysis the sentiment of their users’ feedback on social media platforms.
Autocomplete & Autocorrect
The saviors for students and professionals alike – autocomplete and autocorrect – are prime NLP application examples. Autocomplete (or sentence completion) integrates NLP with specific Machine learning algorithms to predict what words or sentences will come next, in an effort to complete the meaning of the text.
NPL cross-checks text to a list of words in the dictionary (used as a training set) and then identifies any spelling errors. The misspelled word is then added to a Machine Learning algorithm that conducts calculations and adds, removes, or replaces letters from the word, before matching it to a word that fits the overall sentence meaning. Then, the user has the option to correct the word automatically, or manually through spell check.
Wondering what are the best NLP usage examples that apply to your life? Spellcheck is one of many, and it is so common today that it's often taken for granted. This feature essentially notifies the user of any spelling errors they have made, for example, when setting a delivery address for an online order.
From a corporate perspective, spellcheck helps to filter out any inaccurate information in databases by removing typo variations.
Email filters are common NLP examples you can find online across most servers.
Spam filters are where it all started – they uncovered patterns of words or phrases that were linked to spam messages. Since then, filters have been continuously upgraded to cover more use cases.
An NLP case study we can look at is Gmail's new classification system. This upgraded system categorizes emails into one of three groups (primary, social, or promotions) based on the email content. This is a convenient application of NLP that keeps Gmail users' inboxes under control while highlighting relevant and high-priority emails.
Levity offers its own version of email classification through using NLP. This way, you can set up custom tags for your inbox and every incoming email that meets the set requirements will be sent through the correct route depending on its content.
Chatbots are an NLP customer service application example. They are beneficial for eCommerce store owners in that they allow customers to receive fast, on-demand responses to their inquiries. This is important, particularly for smaller companies that don't have the resources to dedicate a full-time customer support agent.
Statista reports on a study from 2019, which mentioned that 65% of decision-makers in customer service believed that chatbots could understand customers' context well. What’s more, another 52% believed that chatbots could take a step further, and automate decision-making based on customer responses!
Among others, chatbots can be used to:
- Respond to pre-determined FAQs
- Schedule meetings and appointments
- Book tickets
- Process and track orders
- Cross and upsell
- Onboard new users or members
Smart assistants such as Google's Alexa use voice recognition to understand everyday phrases and inquiries.
They then use a subfield of NLP called natural language generation (to be discussed later) to respond to queries. As NLP evolves, smart assistants are now being trained to provide more than just one-way answers. They are capable of being shopping assistants that can finalize and even process order payments.
Siri, Alexa, or Google Assistant?
In the Western market, smart assistant household names include Apple's Siri, Amazon's Alexa, and Google Assistant. In Asia, Alibaba's Tmall is the current leader.
In terms of NLP capabilities, these smart assistants:
- Are able to create and edit shopping lists in online stores
- Can understand nuance (for example, the smart assistant will know what shopping list to add the item to)
- Can finalize purchases simply by hearing a single sentence.
These devices are trained by their owners and learn more as time progresses to provide even better and specialized assistance, much like other applications of NLP.
Social Media Monitoring
To better understand the applications of this technology for businesses, let's look at an NLP example.
Owners of larger social media accounts know how easy it is to be bombarded with hundreds of comments on a single post. It can be hard to understand the consensus and overall reaction to your posts without spending hours analyzing the comment section one by one.
Social media monitoring uses NLP to filter the overwhelming number of comments and queries that companies might receive under a given post, or even across all social channels. These monitoring tools leverage the previously discussed sentiment analysis and spot emotions like irritation, frustration, happiness, or satisfaction.
The tools will notify you of any patterns and trends, for example, a glowing review, which would be a positive sentiment that can be used as a customer testimonial.
This powerful NLP-powered technology makes it easier to monitor and manage your brand's reputation and get an overall idea of how your customers view you, helping you to improve your products or services over time.
Customer Service Automation
NLP customer service implementations are being valued more and more by organizations.
Customer service support centers and help desks tend to receive more inquiries than they can handle, and NLP solves this gap by automating responses to simple questions, allowing employees to focus on more complex tasks that require human interaction. NLP can also help you route the customer support tickets to the right person according to their content and topic. This way, you can save lots of valuable time by making sure that everyone in your customer service team is only receiving relevant support tickets.
Analyzing Converted Text
Certain subsets of AI are used to convert text to image, whereas NLP supports in making sense through text analysis. An example of this capability is OCR.
Optical Character Recognition
Optical Character Recognition (OCR) automates data extraction from text, either from a scanned document or image file to a machine-readable text. For example, an application that allows you to scan a paper copy and turns this into a PDF document. After the text is converted, it can be used for other NLP applications like sentiment analysis and language translation.
Google Translate is a basic example of OCR, whereby you can upload a file or even ‘write’ a word, and receive a text version back.
Voice Recognition, or Automatic Speech Recognition (ASR) and Speech-to-Text (STT), is a software capable of converting human speech to its analog form (acoustic sound waves), and from there to a digital form recognized by the machine. ASR works by:
- Breaking down the audio of a speech into individual sounds (called tokens)
- Analyzing each token
- Using algorithms to find the most likely word to fit in that particular language
- Converting the sounds into text.
A widespread example of speech recognition is the smartphone's voice search integration. This feature allows a user to speak directly into the search engine, and it will convert the sound into text, before conducting a search.
Natural Language Generation
Smart Assistants, customer service bots, and other customer-facing software all use a subset of NLP called natural language generation. As the name suggests, this technology uses data to produce narratives (either written or spoken). This is made possible through:
- Content analysis – data is filtered and from there the technology determines what should be included in the content. This stage identifies the main topics in the source document and any relationships between them.
- Data understanding – the data is then interpreted, patterns are identified and the context is formed. In this stage Machine Learning is usually used.
- Document structuring – based on the type of data that was interpreted, a documented plan is made and narrative structure is decided on.
- Sentence aggregation – Certain parts of sentences are combined in a method that summarizes the topic.
- Grammatical structuring – As a finishing touch, grammatical rules are applied to ensure the text sounds natural, and the sentences are written in a grammatically correct manner.
- Language presentation – Finally, the output is generated based on the format. For voice-powered assistants, the output would be audio.
NLP can seem like an abstract concept. Still, as we've seen in many NLP examples, it is a very useful technology that can significantly improve business processes – from customer service to eCommerce search results.
Regardless of the data volume tackled every day, any business owner can leverage NLP to improve their processes.
And yet, although NLP sounds like a silver bullet that solves all, that isn't the reality. Getting started with one process can indeed help us pave the way to structure further processes for more complex ideas with more data. Ultimately, this will lead to precise and accurate process improvement.