Natural Language Processing & RPA

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Natural Language Processing: Intelligent automation with NLP and RPA

Reading emails, answering customer queries and analyzing contracts – software robots can’t handle these tasks, or can they? Read here how software robots learn to speak through NLP and what great opportunities lie dormant in the combination of NLP and RPA.

What is Natural Language Processing?

Natural Language Processing (NLP) deals with the algorithmic processing of natural language. The technical basis of NLP is machine learning. For a computer, language is a collection of data points. In order to understand its meaning, it must learn to interpret this data. Like a human, an algorithm has to learn each language through training. To do this, it is fed with large amounts of data. On this basis, the algorithm recognizes patterns within the data, learns to understand words and sentences in their context and to formulate appropriate answers itself.

NLP was originally developed to teach computers to read. However, thanks to massive technological advances, NLP solutions now deal with all aspects of human communication: Language Generation is about the computer-controlled generation of text or language content, while Language Understanding is based on understanding dialects, imprecision and nuances in linguistic communication.

Search engines, chatbots and co.: areas of application for NLP

NLP is now firmly interwoven with many everyday digital applications. If you are active in the digital world, you have almost certainly already had contact with an NLP application:

  • Search engines: Google and co. use search engine robots to search the internet and output sorted results to users based on keywords. To do this, the search engine must be able to understand and contextualize your search query. This becomes particularly clear with ambiguous terms. The term “NLP”, for example, not only stands for machine-learning-based language processing, but also for the theory of neuro-linguistic programming. What you are looking for only becomes clear from the context of the search history and other factors.
  • Translations: Online translation tools have gotten better and better in recent years. The reason for this is the use of NLP technology and deep learning algorithms. As soon as you have a term translated, you are part of a feedback loop for the algorithm. This makes the algorithm better and better.
  • Chatbots: Chatbots communicate with their users on the basis of text input. In order for the chatbot to understand these inputs, it must have NLP technology and have been trained accordingly.

Natural Language Processing: Frameworks, Tools & Libraries

It does not make sense to develop your own NLP solution. The strength of modern NLP tools is based on training with large amounts of data over very long periods of time. Companies that want to use NLP therefore use frameworks that can be accessed via API.

  • Natural Language Toolkit: The NLTK is an open source NLP framework for Python applications.
  • SpaCy: Open source library for business applications.
  • Amazon Comprehend: NLP as a service for companies that want to implement practical applications.
  • IBM Watson: IBM Watson is a machine learning suite for the realization of chatbots, social listening applications and customer service monitoring.
  • Google Cloud Translation: API for translations based on NLP.
  • GPT-3: Generates something similar but unique based on multiple instances of the desired text.

NLP and RPA: the key to intelligent automation

Robotic Process Automation (RPA) refers to the robot-controlled automation of processes within a company. RPA is ideal for processes in which structured data has to be processed in the same way over and over again – for example, when implementing KYC policies, forwarding incoming orders to the warehouse or filing invoices.

Digitalization has made such tasks increasingly important: Data processing requires effective knowledge management. However, software robots are not intelligent, but work linearly according to a system of rules. The data must therefore follow a recurring pattern in order to be captured by the robot’s routine.

This is where natural language processing comes into play. Natural language processing also makes unstructured data types accessible for automatic processing by a software robot.

Example: Automatic invoice filing

A company uses a software robot to file invoices. This checks incoming emails for keywords (“invoice”), checks whether there is an attachment and files this attachment in the correct folder in the system. The invoice can then be entered into the CRM by an employee. Although the software robot can file attached invoices, it is not able to read data from the invoice and save it directly in the CRM or ERP. After all, invoices do not always follow the same structure.

An NLP tool can extract the invoices into the desired data regardless of the form of the invoice because it understands the words and phrases of the invoice in context. The combination of RPA with NLP therefore allows additional automation in this case:

  • Software robot recognizes incoming invoice and files it.
  • NLP extracts the essential data and saves it in a structured format.
  • Software robot transfers the now structured data to the CRM system.

NLP & RPA: 3 use cases of intelligent automation

RPA is an important technology for the automation of processes in companies. But it is only through the use of NLP that numerous use cases with complex processes can be realized:

Intelligent Process Automation (IPA)

Intelligent Process Automation (IPA) involves the application of artificial intelligence and related new technologies, including computer vision, cognitive automation and machine learning to RPA (Robotic Process Automation). Discover what it can do for your business.

Outlook: RPA and NLP are revolutionizing the process landscape

Robotic process automation is a growth market. According to Gartner, the market doubled between 2019 and 2023. More and more companies are experimenting with software robots – but usually only a handful of robots are used company-wide. But as experience grows, so does the demand for robot-supported process automation. Instead of small, delimited tasks, decision-makers are focusing on complex process chains. Natural language processing plays an important role here. It closes the gap in the processing of unstructured data such as e-mails, contracts and support messages and thus enables the seamless automation of complex processes.

NLP is therefore an important topic for all companies. The automation of complex processes along the value chain enables more efficient structures throughout the company – and thus offers the opportunity to strengthen the positioning of your own company in the long term.

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