Photo by
DALL·E

What is Natural Language Understanding (NLU)?‍

Everything you need to know about NLUs whether you're a Developer, Researcher, or Business Owner.

Author
One AI
·
·
3 min read

The NLP market is predicted to reach over 43 billion USD by 2025, almost 14 times larger than in 2017.

Artificial Intelligence (AI) is the creation of intelligent software or hardware to replicate human behaviors in learning and problem-solving areas. Worldwide revenue from the AI market is forecasted to reach USD 126 billion by 2025, with AI expected to contribute over 10 percent to the GDP in North America and Asia regions by 2030. 

Because of its immense influence on our economy and everyday lives, it’s incredibly important to understand key aspects of AI, and potentially even implement them into our business practices. 

What is Natural Language Understanding?

Natural language understanding is a branch of AI that understands sentences using text or speech. NLU allows machines to understand human interaction by using algorithms to reduce human speech into structured definitions and concepts for understanding relationships. 

It recognizes entity and intent, then responds despite potential human error (ie: misspelling or mispronunciation). 

  • Intent Recognition: utilizes NLP and Machine Learning to understand the sentiment in text and speech
  • Entity Recognition: allows machines to understand crucial information by understanding two types of entities: numeric (numbers, currencies, and percentages) and names (people, businesses, and locations).
NLP vs NLU

NLP vs. NLU

Natural Language Understanding is a subset of Natural Language Processing (NLP) that assists machines in understanding the data imputed. NLP processes large amounts of human language data, and NLU helps machines understand the data. NLU allows whole processes such as decisions and actions to be taken. There are three linguistic levels to understanding language:

  • Syntax: understanding sentences and phrases, it checks the grammar and syntax of the text
  • Semantics: checks the intentions and meanings of the text
  • Pragmatic: understanding context to know what the text is trying to achieve.

For example, NLP allows speech recognition to capture spoken language in real-time, transcribe it, and return text- NLU goes an extra step to determine a user’s intent.

Natural Language Generation (NLG) is the next step that takes the language that has been processed (NLP) and understood (NLU), then produces written or spoken narratives from a data set. 

Popular Applications of NLU

  • Sentiment Analysis: NLUs allow companies to recognize subtle nuances in emotions and opinions and allow companies to gauge customer reactions. At the micro level, this application can allow businesses to gauge the tone behind an email. At the macro level, sentiment analysis can help turn abundant amounts of unstructured data into structured data through Collections.  
  • Chatbots & Virtual Assistants: NLUs empower chatbots to answer frequently asked questions, facilitate shipping, and provide personalized responses and recommendations to customers. Chatbots, at their best, provide 24-7 support to customers that can solve problems quickly. NLU can allow chatbots to understand the sentiment and form emotionally intelligent responses. 
  • Market Intelligence: With the massive amount of data available, NLU applications can collect existing CRM data from across the web and transform it into valuable business insights. 
  • Text Summarization: Automatic Summarization allows humans to go through large amounts of text and summarize it quickly and accurately. Researchers, developers, and businesses can save both time and effort by utilizing quick and grammatically correct summarizations.
  • Comprehensive Analysis: A sophisticated NLU solution can rely on in-depth amounts of data and analysis to help recognize entity and intent. NLUs allow applications to recognize emotion, effort, motive, intensity, etc, and make decisions as a result. 
  • Speech Recognition: Automatic Speech Recognition (ASR) systems utilize NLUs to convert speech to text. First, NLPs process the audio, and NLUs understand the language and intent of the user’s query. NLU takes text and structures it to create a semantic representation that NLG can use to form an appropriate human-like response. 
  • Machine Translation: Language Translation is only possible with NLUs. NLPs allow machines to learn and process human language data, NLU processes language and translates text while retaining the context and sentiment of the original text. 
  • Automatic Ticket Tagging: Text analysis services allow machines the ability to automatically understand the context and content of customer support tickets. This both saves support teams an abundance of time and allows them to prioritize critical tickets.
Popular Applications of NLU

NLU using the One AI Studio:

Considering the complexity of language, creating a tool that bypasses significant limitations such as interpretations and context can be ambitious and demanding. 

The One AI Language Studio effectively reduces developer efforts and time by providing a user-friendly, no-code application that can be applied to unstructured data with ease. Several different approaches and capabilities can be found in the studio, some of which include:

 

  1. Text Summarization: This capability summarizes long pieces of text into short and concrete reviews while retaining the important information and context from the original text. 
  2. Transcribe Audio: This skill takes audio and creates accurate transcriptions within minutes. From here, you can then use other skills to get more insights.
  3. Meaning-based Clustering: This skill takes a list of text entries and clusters together similarly-meaning texts to review, analyze, and understand large amounts of text entries
  4. Sentiment: Sentiments are used to detect and label parts of the text that have a positive or negative sentiment. On a large-scale, sentiment analysis is used to discover trends, understand social media posts, and handle customer relations.
  5. Proofreading: Proofreading is generally used for audio transcription and allows uses to filter errors and remove filler words that are typical with speech (such as like, um, yeah…)
  6. Emotions: The emotion skill recognizes emotions in text and identifies them as Happiness, Sadness, or Anger
  7. Highlights: This Skill takes text and points out key pieces of information and critical points

These capabilities, and more, allow developers to experiment with NLU and build pipelines for their specific use cases to customize their text, audio, and video data further.

Tutorial: Audio Transcription and Sentiment Analysis:

For today’s tutorial, we will be demonstrating One AI Language Studio’s NLU capabilities using the pipeline for Sentiment Analysis. Let’s head over to the One AI Studio (pictured below):

Audio Transcription and Sentiment Analysis

First, we’ll download our audio. We’ll be using the Oprah Winfrey 2018 Commencement Speech for today’s example. After copying the link- make your way to an Online Video Converter Website and convert the video to an MP3 file. 

Input the MP3 audio file into the Text Input of the Studio. One AI has automatically selected the ‘Transcribe Audio’ Skill, and after clicking ‘Proofread,’ to remove spoken errors and filler words, we can go ahead and select ‘Sentiments.’

Then click, ‘Run Pipeline,’ and view the results:

Audio Transcription and Sentiment Analysis

In the Code Editor:

The One AI studio also generates the code for the selected skill or skills. Copy the audio transcription code that was generated in the previous step.

To run the code in the local environment, first, install the One AI SDK package.

In the Code Editor:

The One AI studio also generates the code for the selected skill or skills. Copy the audio transcription code that was generated in the previous step.

To run the code in the local environment, first, install the One AI SDK package.

Import the package and add your API key:

```

import oneai

import base64

oneai.api_key = "[YOUR ONEAI API KEY]"

```

And then run this pipe line :

```

with open("AudioFile.mp3", "rb") as f:

   

    pipeline = oneai.Pipeline(

      steps = [

            oneai.skills.Transcribe(),

            oneai.skills.Proofread(),

            oneai.skills.Sentiments(),

      ]

    )

    Output = pipeline.run(f)

```

Conclusion:

Natural Language Understanding Applications are becoming increasingly important in the business world. NLUs require specialized skills in the fields of AI and machine learning and this can prevent development teams that lack the time and resources to add NLP capabilities to their applications. 

The One AI NLU Studio allows developers to combine NLU and NLP features with their applications in reliable and efficient ways. Check out the One AI Language Studio for yourself and see how easy the implementation of NLU capabilities can be.