Learn More About Natural Language Understanding (NLU) Software
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.
Who Uses Natural Language Understanding Software?
NLU has applications across nearly every industry. Some industries that benefit from NLU applications include financial services, cybersecurity, recruiting, customer service, energy, and regulation.
Marketing: NLU-powered marketing applications help marketers identify content trends, shape content strategy, and personalize marketing content.
Finance: Financial services institutions are increasing their use of NLU-powered applications to stay competitive with others in the industry who are doing the same. Some examples may include trawling through thousands of insurance claims and identifying ones with a high potential to be fraudulent. The process is similar, and the machine learning algorithm can digest the data to achieve the desired outcome quicker.
Human resources: Resumes are long and filled with words. As such, natural language understanding technology can help recruiters comb through large amounts of resumes and other text data to better understand candidates.
What are the Alternatives to Natural Language Understanding Software?
Alternatives to natural language understanding software can replace this type of software, either partially or completely:
Machine learning software: Natural language understanding (NLU) software is specifically connected to and used for text data. If one is looking for more general-use machine learning algorithms, machine learning software would be a good category to pursue.
Text analysis software: NLU software is geared toward incorporating NLU capabilities into other applications or systems. Text analysis software, however, is an all-purpose solution built to analyze any text data. Businesses looking to focus on analyzing their text data, such as from surveys, review sites, social media, and customer service tools, can leverage text analysis software to achieve this goal. This software enables businesses to consolidate and analyze their text data within a single platform.
Software Related to Natural Language Understanding Software
Related solutions that can be used together with natural language understanding software include:
Chatbots software: Businesses looking for an off-the-shelf conservational AI solution can leverage chatbots. Tools specifically geared toward chatbot creation helps companies use chatbots off the shelf, with little to no development or coding experience necessary.
Bot platforms software: Companies looking to build their own chatbot can benefit from bot platforms, which are tools used to build and deploy interactive chatbots. These platforms provide development tools such as frameworks and API toolsets for customizable bot creation.
Intelligent virtual assistants (IVAs): Businesses that want conversational AI with strong natural language understanding capabilities should consider IVAs. IVAs understand a range of different intents from a singular utterance and can even understand responses they are not explicitly programmed to using natural language processing (NLP). With the use of machine learning and deep learning, IVAs can grow intelligently and understand a wider vocabulary and colloquial language, as well as provide more precise and correct responses to requests.
Challenges with Natural Language Understanding Software
Software solutions can come with their own set of challenges.
Data preparation: A potential concern is preparing the data to be ingested by the NLU tool. The data needs to be stored properly, whether that is in a database or data warehouse. Users may require IT or a dedicated admin to ensure the text analytics tool can consume the data.
Automation pushback: One of the biggest potential issues with machine learning-powered applications, such as NLU, lies in removing humans from processes. This is particularly problematic when looking at emerging technologies like self-driving cars. By completely removing humans from the product development lifecycle, machines are given the power to decide in life-or-death situations.
Data security: Companies must consider security options to ensure the correct users see the correct data. They must also have security options that allow administrators to assign verified users different levels of access to the platform.
Which Companies Should Buy Natural Language Understanding Software?
Pattern recognition can help businesses across industries. Effective and efficient predictions can help these businesses make data-informed decisions, such as dynamic pricing based upon a range of data points.
Retail: An e-commerce site can leverage an NLU application programming interface (API) to create rich, personalized experiences for every user.
Entertainment: Media organizations can leverage NLU to comb through their scripts and other content to catalog and categorize their material.
Finance: Financial institutions can analyze contracts and conduct sentiment analysis and named entity recognition to better understand these documents and to scale operations.
How to Buy Natural Language Understanding Software
Requirements Gathering (RFI/RFP) for Natural Language Understanding Software
If a company is just starting out and looking to purchase their first NLU software, wherever they are in the buying process, g2.com can help select the best machine learning software for them.
Taking a holistic overview of the business and identifying pain points can help the team create a checklist of criteria. The checklist serves as a detailed guide that includes both necessary and nice-to-have features, including budget, features, number of users, integrations, security requirements, cloud or on-premises solutions, and more. Depending on the scope of the deployment, it might be helpful to produce an RFI, a one-page list with a few bullet points describing what is needed from a machine learning platform.
Compare Natural Language Understanding Software Products
Create a long list
From meeting the business functionality needs to implementation, vendor evaluations are an essential part of the software buying process. For ease of comparison, after the demos are complete, it helps to prepare a consistent list of questions regarding specific needs and concerns to ask each vendor.
Create a short list
From the long list of vendors, it is advisable to narrow down the list of vendors and come up with a shorter list of contenders, preferably no more than three to five. With this list in hand, businesses can produce a matrix to compare the features and pricing of the various solutions.
Conduct demos
To ensure the comparison is thoroughgoing, the user should demo each solution on the shortlist with the same use case and datasets. This will allow the business to evaluate like for like and see how each vendor stacks up against the competition.
Selection of Natural Language Understanding Software
Choose a selection team
Before getting started, it's crucial to create a winning team that will work together throughout the entire process, from identifying pain points to implementation. The software selection team should consist of members of the organization who have the right interest, skills, and time to participate in this process. A good starting point is to aim for three to five people who fill roles such as the main decision maker, project manager, process owner, system owner, or staffing subject matter expert, as well as a technical lead, IT administrator, or security administrator. In smaller companies, the vendor selection team may be smaller, with fewer participants multitasking and taking on more responsibilities.
Negotiation
Prices on a company's pricing page are not always fixed (although some companies will not budge). It is imperative to open up a conversation regarding pricing and licensing. For example, the vendor may be willing to give a discount for multi-year contracts or for recommending the product to others.
Final decision
After this stage, and before going all in, it is recommended to roll out a test run or pilot program to test adoption with a small sample size of users. If the tool is well used and well received, the buyer can be confident that the selection was correct. If not, it might be time to go back to the drawing board.
What Does Natural Language Understanding Software Cost?
NLU software is generally available in different tiers, with the more entry-level solutions costing less than the enterprise-scale ones. The former will usually lack features and may have caps on usage. Vendors may have tiered pricing, in which the price is tailored to the users’ company size, the number of users, or both. This pricing strategy may come with some degree of support, either unlimited or capped at a certain number of hours per billing cycle.
Once set up, they do not often require significant maintenance costs, especially if deployed in the cloud. As these platforms often come with many additional features, businesses looking to maximize the value of their software can contract third-party consultants to help them derive insights from their data and get the most out of the software.
Return on Investment (ROI)
Businesses decide to deploy machine learning software with the goal of deriving some degree of ROI. As they are looking to recoup the losses that they spent on the software, it is critical to understand the costs associated with it. As mentioned above, these platforms typically are billed per user, which is sometimes tiered depending on the company size.
More users will naturally translate into more licenses, which means more money. Users must consider how much is spent and compare that to what is gained, both in terms of efficiency as well as revenue. Therefore, businesses can compare processes between pre- and post-deployment of the software to better understand how processes have been improved and how much time has been saved. They can even produce a case study (either for internal or external purposes) to demonstrate the gains they have seen from their use of the platform.
Natural Language Understanding Software Trends
Automation
With the adoption of NLU and the automation of repetitive tasks, businesses can deploy their human workforce to more creative projects. For example, if a machine learning algorithm automatically displays personalized advertisements based on a user’s text, the human marketing team can work on producing creative material.
Voice technology
Voice is a primal method of interacting with others. It is only natural that we now converse with our machines using our voice and that the platforms for said voicebots have seen great success. Voice makes technology feel more human and allows people to trust it more. Voice will prove to be a crucial natural interface that mediates human communication and relationships with devices within an AI-powered world.
Artificial intelligence (AI)
AI is quickly becoming a promising feature of many, if not most, types of software. With machine learning, end users can identify patterns in data, allowing them to make sense of content and help them understand what they are seeing. This pattern recognition is fueling the rise of more powerful, contextually-aware chatbots.
