A new survey has found that the pandemic has led to companies speeding up the adoption of digital technology by several years, with many of these changes here for the long haul.
One technology that has been making unprecedented waves during this time is Natural Language Processing. According to MarketsandMarkets the natural language processing (NLP) market size will grow from USD 10.2 billion in 2019 to USD 26.4 million by 2024.
Although the technology is not new, the growth in smart devices, rapid advancements in cloud computing, and easier access to big data make it an increasingly critical investment for enterprises.
For those novices amongst us, let us explain. NLP is a subset of artificial intelligence (AI) and is how computers try to comprehend human language.
The human language is complex, with thousands of languages that all have their own rules. Traditionally, the unstructured nature of language data means businesses could not search, analyze, and store it like structured transactional information. However, with 90% of daily data defined as unstructured, organizations are potentially missing a huge opportunity to enhance their operations.
Powerful NLP tools and algorithms allow businesses to unlock all of their data assets and establish a competitive advantage in a digitally-heavy ecosystem that is accelerating due to the Covid-19 pandemic.
For businesses, the most popular application of NLP is on the customer-facing side of the organization. Beyond chatbots, which are becoming a critical aspect of customer service, NLP helps analyze communications and discover behavioral patterns to lead future development.
For example, social media can offer a wealth of data on competitors, market trends, and consumer demographics. Obtaining the right information at the right time allows companies to streamline their social marketing efforts, delivering the optimal message.
At an internal level, NLP algorithms can extract data from resumes or emails to identify the perfect candidates for recruitment teams. Potentially new employees can be matched against desirable attributes of an existing resource (based on performance reviews) and offer a greater chance of hiring the right people. Analysis of employee feedback, surveys, and interviews can generate alerts for managers and help develop recommendations for staff retention and other critical HR metrics.
Some longstanding industries, like the legal sector, are document-heavy. Research is at the core of the legal profession and online databases will store information about millions of cases. Legal professionals need to understand countless contracts, policies, reports, and papers to augment their work on a case. The research process can be complex, time-consuming, and costly, which is where NLP comes in.
NLP can use context and previous queries to predict what an attorney needs in their search. Lawyers can use simple English to complete a search, and the NLP algorithms will suggest relevant clauses and cases. Each time something is clicked, it remembers the result for future use.
In one study, NLP models could predict with 79% accuracy how the European Court of Human Rights would rule on a case. The attorneys can then better tailor their arguments so as to either support or combat the prediction.
Another primary use case for NLP is in engineering and manufacturing. With tasks like machine maintenance, NLP can analyze data from safety reports, previous repairs, and documentation. Using the information along with additional metadata, NLP can tell engineers precisely where to look, recommending solutions that will improve their efficiency.
There are several ways of using NLP in retail. The US retailer Nordstrom is using algorithms to analyze data gathered via feedback forms and customer surveys. From the analysis, they could see that in-store customers find it hard to locate salespeople as they didn’t wear uniforms. After giving staff brightly colored t-shirts, Nordstrom achieved a 30-point jump in sales staff effectiveness.
Other retailers are experimenting with humanoid robots, touch-screen assistants, and online robots. For example, Nescafe uses the humanoid robot, Pepper, to sell coffee. The bots can detect emotion and behaviors to respond appropriately while using NLP to know the context of customer questions.
Every business will need to do some kind of forecasting, whether it be budgetary or making vital decisions. Typically, this is based on transactional data and doesn’t make the most of the opportunity that NLP can offer.
For example, LenddoEFL helps financial institutions to assess the creditworthiness of an individual using NLP and machine learning. The software using NLP to analyze the user’s digital footprint, including browsing history, social media, demographic, and geolocation data. Machine learning algorithms ingest the information and forecast the future creditworthiness of a customer.
Sigmoidal helps banks and investment firms to automate the task of mining for information on market trends. The software will collate data from news, documents, and social media and then classify it into the most relevant information for each investor. Patterns in customer investment and news data allow the platform to offer personalized investment advice, predicting the most probable future behaviors.
Healthcare is an industry that is screaming out for NLP applications. The vast volume of information contained in medical records means that institutions are swamped with unorganized and unstructured data.
NLP can process unstructured data from different sources such as electronic medical records, literature, and social media, allowing analytics systems to interpret it. After converting the information into a structured format, health systems can gain valuable insights and classify patients.
A study from 2018 used Twitter data to predict indications of imminent suicide attempts. Healthcare professionals in the US are keen to understand suicide as rates continue to rise. NLP algorithms created a system with a 70% prediction rate and only a 10% false-positive rate. Those postings with fewer emojis in texts and more angry and sad tweets raise alerts.
SignAll leverages AI to turn sign language into text, helping organizations increase accessibility for the deaf and hard of hearing. Combining computer vision, machine learning, and NLP algorithms, SignAll uses innovative technology for accurate translation that also uses body movements, facial expressions, and hand/finger shapes.
Livox is an app for people with disabilities that works using NLP. It has several algorithms that can adjust to every disability. For example, IntelliTouch corrects the imperfect touch of people with motor disabilities. The Livox Natural Conversation uses NLP to allow non-verbal people to answer questions similar to Google or Siri. Using keywords, the app can trigger events that make it easier for a person with a disability to learn.
As companies continue to invest and experiment with NLP to unlock their hidden data, the market will continue to grow. The examples in this post show how far-reaching the technology can be, with applications spanning many verticals. The self-learning nature of algorithms means NLP can only improve and is a firm part of AI’s future.