Natural Language Processing (NLP) is the buzzword these days, thanks to the rapid adoption of voice interfaces and AI-driven Human-Computer Interaction (HCI). It is set to grow tremendously as computers begin to understand the nuances of human language. It will offer big opportunities for businesses to serve customers better and increase employee productivity and efficiency.

What is NLP

Natural language processing is defined as a subfield of computer science, information engineering, and artificial intelligence. It is concerned with the interactions between computers and human (natural) languages and how to program computers to process and analyze natural language data.

The biggest challenges in NLP are in the areas of speech recognition, natural language understanding, and natural language generation. Now, advances in these areas are enabling more meaningful HCI. An example is Google’s Voice Assistant which can now make appointments and reservations for you via phone.

Experts also foresee a rise in voice-enabled search, which is an opportunity for businesses. Customers are likely to use voice-enabled search to seek information and engage with brands.  Using advanced NLP and AI, voice assistants will be able to ‘remember’ customers’ past queries and deliver much more relevant information that will ensure customer satisfaction and encourage loyalty.

NLP & Chatbots

NLP allows chatbots to get context and meaning in customer interactions.  Advanced NLP capabilities can identify spelling and grammatical errors, allowing chatbots to understand the customer’s intended message even if there are errors. Further, it can sense the emotion of the customer from the conversation and can alert human supervisors for personalized attention.

Sentiment Analysis

We use language to express our emotions. Natural Language Processing can now understand the emotion or sentiment that underlines human language. This is particularly useful as NLP can be used to monitor social media. This sentiment analysis is used by organizations to listen to what the customer is saying about their brands and get insights from social media.

Organizations can also combine this unstructured data analysis provided by NLP with structured data from surveys and focus group discussions to get more complete business insights and analysis. This allows them to make informed business decisions.

Email Filters

NLP can filter emails sent in by customers. For example, if a customer sends an email about a particular product or service, NLP can analyze the mood of the customer and accordingly alert customer service if immediate attention is required.  It can also be used to improve spam filters as phishing becomes more sophisticated.

NLP in Tax, Legal & Accounting Prep

Most of the Big 4 accounting firms along with legal firms are already using AI-driven NLP with robotic process automation (RPA) to rapidly complete certain laborious auditing tasks that would usually require employees several weeks to do.

It can be especially useful in reviewing documents. One firm uses AI and NLP to review lease accounting standards. For example, when companies have to manually examine thousands of leases to ensure that they comply with new regulations, they usually require large teams and several man hours over months combing through the details. This task can be accelerated using NLP to extract key information, with employees only required to validate the results, all of which saves the company time and money and ensures greater accuracy.

NLP in Healthcare

NLP can vastly improve healthcare delivery when used with the right set of tools. Many clinicians have already started using voice recognition-based dictation tools as an alternative to writing or typing patient notes. Healthcare clinics have reported that this improves the quality and accuracy of the documentation. Provided with the right sets of data and machine learning, NLP algorithms can also show potentially identify high-risk patients who may require enhanced care coordination.

Advances in NLP have benefits for industries across several verticals. In a data-driven environment, NLP will help improve not just the quality of human-computer interaction but also result in a better understanding of the customer.