What is Natural Language Understanding Software?

Natural language understanding, a subset of natural language processing (NLP), makes predictions or decisions based on text data. These learning algorithms can be embedded within applications to provide automated artificial intelligence (AI) features. A connection to a data source is necessary for the algorithm to learn and adapt over time. Pulling out actionable insights from numerical data housed in ERP systems, CRM software, or accounting software is one thing, but gaining insights from unstructured data sources is invaluable. Without dedicated software for this task, businesses must spend significant time and resources building natural language understanding models or haphazardly investigating the data. These algorithms may be developed with supervised learning or unsupervised learning. Supervised learning involves training an algorithm to determine a pattern of inference by feeding it consistent data to produce a repeated, general output. Human training is necessary for this type of learning. Unsupervised algorithms independently reach an output and are a feature of deep learning algorithms. Reinforcement learning is the final form of machine learning, which consists of algorithms that understand how to react based on their situation or environment. End users of intelligent applications may not be aware that an everyday software tool utilizes a machine learning algorithm to provide automation of some kind. Additionally, machine learning solutions for businesses may come in a machine learning as a service (MLaaS) model. What Does NLU Stand For? NLU stands for Natural Language Understanding, which is a subset of natural language processing (NLP). What Types of Natural Language Understanding Software Exist? Natural language understanding, at its core, allows machines to understand human language in spoken or written form. There are two key methods this can be accomplished. Machine learning-based systems Machine learning algorithms use statistical methods. They learn to perform tasks based on training data they are fed and adjust their methods as more data is processed. Using a combination of machine learning, deep learning, and neural networks, natural language processing algorithms hone their own rules through repeated processing and learning. Rules-based systems This system uses carefully designed linguistic rules. This approach was used early in the development of natural language processing and is still used.

What are the Common Features of Natural Language Understanding Software?

The following are some core features within natural language understanding software that can help users better understand text data: Part-of-speech (POS) tagging: With POS tagging, users can parse text by parts of speech. This can help break down sentences into component parts to understand them. Named entity recognition (NER): Sentences are comprised of various entities, from street names to surnames, places, and more. With NER, one can extract these entities. These extracted entities can then be fed into other systems automatically. Sentiment analysis: Language can be positive, negative, or neutral. Using sentiment analysis techniques, one can input text and be given the sentiment (positive or negative) of that text. Emotion detection: Similar to sentiment analysis, emotion detection can detect the emotion of human language, whether written or spoken. Despite the research supporting it, this method has come under scrutiny, and its veracity has been challenged.

What are the Benefits of Natural Language Understanding Software?

Natural language understanding is useful in many different contexts and industries. Application development: NLU drives the development of AI applications that streamline processes, identify risks, and improve effectiveness. Efficiency: NLU-powered applications are constantly improving because of the recognition of their value and the need to stay competitive in the industries in which they are used. They also increase the efficiency of repeatable tasks. A prime example of this can be seen in eDiscovery, where machine learning has created massive leaps in the efficiency with which legal documents are looked through, and relevant ones are identified. Scalability: Humans are great at analysis, but their analysis skills can break down when the amount of data is vast and when they need to produce results in record time. NLU-powered technology does not get stressed, pressured, or tired. It can analyze a (relatively) small amount of data or a large text corpus with ease, speed, and accuracy. This can be scaled across a business’ text datasets and various use cases. Discovering trends: NLU can do a great job at finding trends and patterns in text data. Through word clouds, graphs and charts, and more, NLU can provide users with deep insight into what is happening beneath the surface. Empowering non-technical users: Much NLU technology in the market is no-code or low-code, which allows non-technical users to benefit from the technology. Gone are the days when one needed to go to a data scientist or IT professional to understand language data.

Page 2 | Best Natural Language Understanding (NLU) Software: User Reviews from October 2026

Tungsten TotalAgility

The Industry’s Only Low‑Code, Integrated, End‑to‑End Intelligent Automation Solution Tungsten TotalAgility is a powerful all-in-one solution that combines document and process intelligence using the industry's leading capture, OCR, and process orchestration technology. Harness the Tungsten Intelligent Automation Platform, Tungsten TotalAgility, to go beyond AI-powered RPA by unlocking document intelligence, connecting disparate systems, and orchestrating human and digital workers to execute and automate workflows across your high-value business processes. • Document Intelligence: Apply cognitive capture and artificial intelligence to unstructured data to  automate and extract information and unlock data insights. • Process Orchestration: Orchestrate digital workflows in collaboration with users, systems , and data. • Connected Systems: Bring together your critical business systems— enterprise applications, legacy systems, mobile, chatbots, and more—across internal and external business processes. :: Successful Organizations Rely on TotalAgility :: Tungsten TotalAgility® streamlines building and deploying intelligent process automation so you can expand human and digital workforce capacity. Receive, execute, route, and report on workflow tasks from a single platform. Why customers choose TotalAgility? • Industry-leading document intelligence: Our intelligent document processing technology processes documents and data with the highest accuracy and speed. • Low-code automation: Powerful tools to build, deploy and accelerate enterprise automation. • End-to-end business process handling: A central intelligent automation platform to handle dynamic tasks, trigger automated rules and deploy on-demand workforce capacity. • Mobile engagement: Deliver enhanced customer experiences across any channel and any device.

Average Rating: 4.3/5.0

Total Reviews: 42

How Do G2 Users Rate Tungsten TotalAgility?

Summarization: 10.0/10 (Category avg: 9.0/10)
Language Detection: 10.0/10 (Category avg: 8.8/10)
Part of Speech Tagging: 10.0/10 (Category avg: 8.7/10)
Quality of Support: 8.3/10 (Category avg: 8.7/10)

Who Is the Company Behind Tungsten TotalAgility?

Seller: Tungsten Automation
Year Founded: 1985
HQ Location: Denver, CO
Twitter: @TungstenAI
6,445 Twitter followers
LinkedIn® Page: www.linkedin.com
1,599 employees on LinkedIn®

Who Uses This Product?

Top Industries: Banking, Information Technology and Services
Company Size: 53% Large, 31% Medium

What Are Recent G2 Reviews of Tungsten TotalAgility?

"Automation with Kofax KTA"

Rating: 4.5/5.0 stars

— Shahir A.

Best Natural Language Understanding (NLU) Software - Page 2

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