How Does Natural Language Processing Work?

Here’s how Natural Language Processing works in a nutshell. First, text analytics functions (usually low-level) are used to break down phrases into specific parts, which includes verbal speech for example. NLP then deciphers the meaning of these specific parts, analyzes the sentiment, and further categorizes the message being portrayed.

You can see examples of NLP most prevalent through chatbots, voice assistants, and automated emails. NLP in Robotic Process Automation (RPA) can help analyze structured, unstructured, and semi-structured data, and extract it for further analysis. Through AI-driven practices, RPA vendors must scale their NLP capabilities to keep up with the demand and bandwidth of automation. Especially in automating end-to-end processes, NLP can provide a humanistic touch to enhance the user experience, which can have a major impact in various functions/industries.

What is Natural Language Processing?

Natural Language Processing (NLP), is a form of technology that allows computers to give meaning to user inputs, such as human language and action.

Practical Applications for NLP in RPA

  • Support Ticket Processes: Within enterprise-level organizations, multiple trouble tickets are submitted and processed every day. A properly configured and trained NLP system can determine which topics, solutions, or specific products are mentioned and send the ticket to the right department. It can also determine the urgency of the ticket based on sentiment analysis.

  • Automated Invoice Processing: An OCR Engine paired with NLP can also break down and categorize documents. Through this solution, documents can be scanned, data can be extracted, and streamlined into another system. Using AI, errors can also be recognized in these documents, which can prompt human users to make corrections if need be.

  • Analyze Contracts: Statements and contractual agreements, often grandiose in nature, can encompass important sections that may be skipped over by the human eye. Applying NLP and OCR capabilities to contract analysis can help organizations stay in compliance while alleviating much (if not all) of the manual tracking involved.

  • Document Processing: Processing documents manually can take hours away from an employee’s precious time, especially if the files are unique in nature. Text analytic automations can significantly streamline this process. When paired with NLP, data values and relationships between them can be understood, diagnosed, and categorized into the appropriate places.

Benefits of Integrating NLP in RPA

  • Streamline urgent requests
  • Enhance customer experience
  • Enhance security measures
  • Mitigate critical errors
  • Save hours routing conversations

As a global provider of RPA technology, UiPath is leading the charge for intelligent automation practices, including Natural Language Processing integration. Through their continuous tools release cycles, UiPath is committed to their AI-driven journey, which encompass more NLP and Machine Learning capabilities. Here are some ways NLP will be involved in future UiPath RPA developments:

  • Cognitive Enhancements: Through language detection and sentiment analysis, the scope of knowledge-based automaton processes is significantly enhanced. RPA bots can understand the meaning of emotion through voice/text-based language, rendering accurate and streamlined decision making.

  • Unattended Robotics: With autonomous automation, RPA bots can manage and oversee other RPA bots. These AI-driven “robotic managers” can significantly reduce automation costs and meet service levels by synchronizing queued workflows.

  • Computer Vision: UiPath bots have intelligent capabilities to view screen elements through contextual relationships, mimicking the precision of human behavior. This makes implementations two times faster with a more stable production and enhanced capabilities.

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