Natural language processing is shaping intelligent automation

This article was contributed by Pascal Bornet & Rachael Churchill. The content of this article is inspired by Pascal’s book Intelligent Automation.

Natural language processing is the name usually given to computers’ ability to perform linguistic tasks — although in practice it includes more than just language processing (understanding text and speech) but also includes language generation (creating text and speech).

Natural language processing (NLP) is one component of intelligent automation, a set of related technologies that enable computers to automate knowledge work and augment the productivity of people who work with their minds. The other components of intelligent automation are computer vision (interpreting images and videos, such as in self-driving cars or medical diagnostics), thinking & learning (for example, evolving strategies and making decisions based on data), and execution (interacting with the physical world or with existing software, and chaining the other capabilities together into automated pipelines).

Below are just some applications of natural language processing that are being deployed today and how they can help your business.

Natural language processing technologies

Chatbots and cognitive agents

Chatbots and cognitive agents are used to answer questions, look up information, or schedule appointments, without needing a human agent in the loop.

Simple chatbots can be programmed with a basic set of rules (“if the user says X, you say Y”); more advanced chatbots or “cognitive agents” use deep learning to learn from conversations and improve themselves, and can be mistaken for humans.

Many chatbots are text-based, interacting with users via instant messaging or SMS, but some use voice and even video. Notable examples are ANZ Bank’s “Jamie” chatbot, which guides customers through the bank’s services, and Google Duplex, which can make phone calls to book hair appointments or restaurant tables, even speak to unsuspecting receptionists who don’t know it’s a bot.

Unstructured information management

Unstructured information management (UIM) platforms are used to process large amounts of unstructured data and extract meaning from them without the need for lots of manual keyword search queries, which are time-consuming and error-prone. They are a vital component of natural language processing and process unstructured documents such as journal articles, patents, contracts, and health records, and build a structured, searchable knowledge base. They can also classify the data and look for clusters and trends within it.

Sentiment analysis

Sentiment analysis uses natural language processing to extract sentiments, such as approval or disapproval of a brand, from unstructured text such as tweets.

Speech analytics

Speech analytics is a component of natural language processing that combines UIM with sentiment analysis. It’s used by call centers to turn text chats and transcriptions of phone conversations into structured data and analyze them using sentiment analysis. This can all be done in real-time, giving call center agents live feedback and suggestions during a call, and alerting a manager if the customer is unhappy.

Machine translation

Machine translation is an enormously powerful application of NLP. Currently, it is usually not powerful enough to produce fully grammatical and idiomatic translations, but it can give you the gist of a web page or email in a language you don’t speak. 500 million people each day use Google Translate to help them understand text in over 100 languages.

Information classification

Information classification or categorization is used for spam filtering, among other things. It works using the same kind of machine-learning model that’s used to classify X-rays and other medical images into healthy and diseased, or used by self-driving cars to decide whether something is a stop sign. Rather than being programmed with explicit rules, the computer is given a large amount of training data in the form of known spam emails and known legitimate emails, and it extracts its own evidence-based rules from them for classifying new emails.

Components of natural language processing that can help your business

Chatbots and cognitive agents

Chatbots and cognitive agents can improve your bottom line by replacing call center staff for straightforward customer queries, and augmenting human call center agents for more complex queries, allowing you to expand your customer base and market share and improve customer satisfaction without needing to employ and train more agents.

Unstructured information management

Unstructured information management platforms allow you to automate a lot of research work: for example, lawyers can use them to run intelligent queries over existing patents or case law, and medical researchers can use them in drug discovery or look for relevant gene interactions in the literature. Rather than spending time poring over reams of documents, a human researcher can quickly review the suggestions and insights provided by the UIM platform, making them more productive overall and freeing up their time and mental energy for the more creative and high-level aspects of the job.

Sentiment analysis

You can use sentiment analysis to perform automatic real-time monitoring of consumer reactions to your brand, especially in response to a new product launch or ad campaign, which will help you to tailor your future products and services accordingly. It can also automatically alert you to any eruptions of criticism or negativity about your brand on social media, without the need for human staff actively monitoring channels 24/7,  so that you can respond in time to avert a PR crisis.

Speech analytics

Speech analytics can augment the skills of your call center staff, improving customer satisfaction without the expense and opportunity cost of additional training. You can also use speech analytics to detect conversation patterns that lead to successful sales, or opportunities for cross-selling or up-selling based on customer behavior. This can help elevate mediocre telesales agents into star salespeople, enabling them to share and deploy the talents of their more skilled colleagues, making a significant impact on your top line without any expenditure on recruitment or training.

Machine translation

Machine translation can allow you to read relevant articles which your competitors might not have seen if they’re published in a minority language, to share knowledge internationally across your business, and to communicate with international colleagues or suppliers without the overhead of a human translator (although for communicating with customers it may still be advisable to employ one in order to make a good impression).

Information classification

Information classification has a variety of useful applications. As well as saving you time and irritation by filtering out spam, this technology can be used to automate domain-specific classification tasks. For example, it could categorize and tag the products in a catalog, making it easier for customers to browse and purchase them; or it could filter social media posts for hate speech, mitigating legal and reputational risks without needing a large team of human moderators; or it could categorize support tickets and automatically forward them to the correct person, saving manual effort and improving overall response times.

Natural language processing: a case study

This is an example from my own experience of the benefits of using cognitive agents to improve customer satisfaction and reduce employee turnover.

A hotel chain employed a team of 240 customer care agents to deal with over 20,000 customer interactions per day, including phone calls, email, and social media. The team’s morale was low due to the high pressure and workload, and employee turnover was 40%. This had a knock-on effect on the quality of customer service, which was rated less than five out of 10.

The company deployed an omnichannel cognitive agent to interact with customers across email, social media, and voice calls. The cognitive agent was designed to look and behave similarly to human agents, and used machine learning to improve itself and learn from its previous conversations. It could also recognize users based on biometric information, such as voice or facial recognition, and it could autonomously process changes in systems.

After three months, the customer satisfaction rating had improved from five out of 10 to nine out of 10, employee turnover had decreased by over 70%, and the human team members were under less pressure and were able to focus on more complex and higher value-add interactions requiring greater relational skills.

Language is how humans naturally communicate, so computer interfaces that can understand natural language are more powerful and easier to use than those that require clicking buttons, typing commands, or learning to program, and it’s important to understand the components of natural language processing. Natural language interfaces are the next step in the evolution of human-computer interaction, from simple tools to machines capable of event-driven and automated processes, potentially even leading to a kind of symbiosis between humans and machines.

This article was contributed by Pascal Bornet & Rachael Churchill. The content of this article is inspired by Pascal’s book on Amazon, Intelligent Automation.