Expert System S.p.A.

07/29/2021 | Press release | Distributed by Public on 07/29/2021 16:56

What Is Natural Language Understanding?

Over the past year, 50 percent of major organizations have adopted artificial intelligence, according to a McKinsey survey. Beyond merely investing in AI and machine learning, leaders must know how to use these technologies to deliver value.

When considering AI capabilities, many think of natural language processing (NLP) - the process of breaking down language into a format that's understandable and useful for computers and humans. However, the stage where the computer actually 'understands' the information is called natural language understanding (NLU).

It's critical to understand that NLU and NLP aren't the same things; NLU is a subset of NLP. NLU is an artificial intelligence method that interprets text and any type of unstructured language data.

To maximize the potential of AI, start by understanding what NLU is, how it delivers value to businesses, and its associated challenges.

How does Natural Language Understanding Work?

NLU extracts context and meaning from natural language inputs. Essentially, it's how a machine understands user input and intent and 'decides' how to respond appropriately.

The intent in this context means a mapping between what a user says and what action an AI technology like a chatbot should take, according to the NCBI. How does this happen? A user input triggers a specific intent and may have parameters that specify detailed information about it. With NLU, the chatbot will use domain entities to extract value from these parameters.

Domain entity extraction involves sequential tagging, where parts of a sentence are extracted and tagged with domain entities. Basically, the machine reads and understands the text and 'learns' the user's intent based on grammar, context, and sentiment.

For example, consider the question: What is the weather like in Boston? The user intent is to learn the weather in Boston, and the entity value is Boston. As TowardsDataScience.com puts it, an intent relates to performing an action. Entities are the context for the action that needs to be performed.

Why Is Natural Language Understanding Important?

NLU underpins speech recognition software like iOS's Siri which works toward achieving human and computer understanding. For example, chatbots often provide answers to frequently asked questions. They perform such actions through different parts of NLU technology like:

  • feature extraction and classification
  • entity linking
  • knowledge management

NLU also captures and translates unstructured data. For example, many voice-activated devices allow users to speak naturally. With NLU, conversational interfaces can understand and respond to human language. They use techniques like segmenting words and sentences, recognizing grammar, and semantic knowledge to infer intent.

When deployed properly, AI-based technology like NLU can dramatically improve business performance. Sixty-three percent of companies report that AI has helped them increase revenue. Industries like sales and marketing, product and service development, and supply-chain management are the most common beneficiaries of this technology.

NLU has helped organizations across multiple different industries unlock value. For example, insurance organizations can use it to read, understand, and extract data from loss control reports, policies, renewals, and SLIPs. Banking and finance organizations can use NLU to improve customer communication and propose actions like accessing wire transfers, deposits, or bill payments. Life science and pharmaceutical companies have used it for research purposes and to streamline their scientific information management. NLU can be a tremendous asset for organizations across multiple industries by deepening insight into unstructured language data so informed decisions can be made.

The Challenges of Natural Language Understanding

NLU has plenty of room to evolve, and organizations should be mindful of its challenge areas. For example, many AI technologies still haven't perfected deciphering ambiguous human language. Day-to-day speech involves contextual words and phrases and multiple meanings for different words and phrases. These differences aren't easy for a machine to understand. For example, it might equate expressions like 'red tape' to tape that is physically red. It's also hard for a machine to interpret idioms like 'let the cat out of the bag.'

Machines may be able to read information, but comprehending it is another story. AI may not always fully grasp context and semantics. For example, 'moving' can mean physically moving objects or something emotionally resonant. Additionally, some AI struggles with filtering through inconsequential words to find relevant information. When people talk to each other, they can easily understand and gloss over mispronunciations, stuttering, or colloquialisms. Even though using filler phrases like 'um' is natural for human beings, computers have struggled to decipher their meaning.

Many machines have trouble understanding the subtleties of human language. If users deviate from the computer's prescribed way of doing things, it can cause an error message, a wrong response, or even inaction. However, solutions like the Expert.ai Platform have language disambiguation capabilities to extract meaningful insight from unstructured language data. Through a multi-level text analysis of the data's lexical, grammatical, syntactical, and semantic meanings, the machine will provide a human-like understanding of the text and information that's the most useful to you.

Takeaways for Natural Language Understanding

Organizations need artificial intelligence solutions that can process and understand large (or small) volumes of language data quickly and accurately. These solutions should be attuned to different contexts and be able to scale along with your organization.

Choosing an NLU capable solution will put your organization on the path to better, faster communication and more efficient processes. NLU technology should be a core part of your AI adoption strategy if you want to extract meaningful insight from your unstructured data.