Natural Language Processing: Intelligent Automation with NLP and RPA

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

What is Natural Language Processing (NLP)?

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. To understand its meaning, it must learn to interpret this data. Like a human, an algorithm must acquire each language through training. To do this, it is fed large amounts of data. On this basis, the algorithm recognizes patterns within the data, learns to understand words and sentences in their context, and formulates adequate 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 computer-driven generation of text or speech content, while Language Understanding is about understanding dialects, imprecision, and nuances in linguistic communication.

Search engines, Chatbots and Co.: Application areas of NLP

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

  • Search engines: Google and Co. use search engine robots that search the Internet and output users sorted based on keywords. To do this, the search engine must be able to understand and contextualize your search query. This becomes particularly clear in the case of ambiguous terms. For example, the term “NLP” stands not only for Machine Learning-driven language processing, but also for the theory of neurolinguistic programming. What you are searching for is only revealed by the context of the search history and other factors.
  • Translations: Online translation tools have gotten better and better in recent years. The reason is the use of NLP technology and Deep Learning algorithms. Once you have a term translated, you are part of a feedback loop for the algorithm. As a result, the algorithm gets better and better.
  • Chatbots: Chatbots communicate with their users based on text input. For the chatbot to understand this input, it must have NLP technology and have been trained accordingly.

Natural Language Processing: Frameworks, Tools & Libraries

Developing your own NLP solution does not make sense. 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 realizing chatbots, social listening applications and customer service monitoring.
  • Google Cloud Translation: API for translations from Basis for NLP.

NLP and RPA: The key to Intelligent Automation

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

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

This is where Natural Language Processing (NLP) comes into play. NLP makes even unstructured data types accessible for automatic processing by a software robot.

Example: Automatic invoice filing

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

An NLP tool can extract the desired data from invoices regardless of the form of the invoice because it understands the words and phrases of the invoice in context. So in this case, combining RPA with NLP allows for additional automation:

  • Software robot recognizes incoming invoice and stores it
  • NLP extracts the essential data and stores it in a structured format
  • Software robot transfers the now structured data into CRM

NLP & RPA: 3 use cases of Intelligent Automation

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

Pre-sorting customer inquiries

Digitally anchored companies receive many requests from their customers every day. By using a trained NLP software robot, requests can be sorted, clustered, prioritized and assigned to specific users. Customer service staff spend less time managing and more time responding to customer inquiries.

Digitize and process physical invoices

Despite digitization, many companies still send their invoices by mail. For the recipient, this means additional administrative work. The invoice must be collected, scanned and transferred to the system. OCR software with NLP integration allows this process to be automated. Invoices are scanned and processed by the OCR engine before a software robot passes the data to another system.

Sentiment analysis

Social media has made the voice of customers more powerful than ever before. Reviews on sales platforms, reactions to posts on social networks, or conversations in forums contain a lot of valuable data that companies can harness to improve their own offerings. Manual analysis of these data volumes in their entirety is not very effective because of the volume and dynamics involved. However, with RPA and NLP, sentiment analysis can be largely automated. Companies can use an automated crawler to monitor and automatically extract content from a channel. Now, the data and its relationships with each other can be examined and categorized with NLP-based text analysis.

Outlook: RPA and NLP revolutionize the process landscape

Robotic Process Automation is a growth market. According to Gartner, the market has doubled between 2019 and 2021. More and more companies are experimenting with software robots – but company-wide, usually only a handful of robots are used. But as experience grows, so does the demand for Robotic Process Automation. Instead of small, delimited tasks, decision-makers are focusing on complex process chains. Natural Language Processing (NLP) plays an important role in this. It closes the gap in the processing of unstructured data such as e-mails, contracts and support messages and thus allows 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 sustainably strengthen the positioning of one’s own company.

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