NLP vs NLU vs NLGApril 5, 2021
Making our machines proficient in Natural language is the first step in making them as intelligent as human beings. In this context, Artificial Intelligence practitioners have been throwing the terms NLP, NLU, and NLG, at the general folks. These terms, sounding too much alike to the non-practitioners, are a source of imminent confusion for them. So to understand the difference between these terms, we need to have a closer look at them.
What is NLP?
NLP stands for Natural Language Processing and is one of the most exciting and popular Artificial Intelligence branches. It has evolved from computational linguistics – the branch of linguistics that takes advantage of tools and techniques of various fields like Computer Science, Linguistics, Anthropology, and others to analyze and synthesize a language.
Natural language inputs are unstructured in their format and converted to a structured one with the Natural language processing algorithms. Syntactical parsing of data helps in recognizing the Entities and their Intents. This structure allows machines to understand language with better ease and efficiency.
Applications of NLP
- Auto-complete in Search Engines
- Spelling correction
- Language Translator
- Grammar Checkers
- Split compound sentences into simple sentences
- Lexical Disambiguation by creating structural data
What is NLU?
NLU or Natural Language Understanding is a subset of the NLP engine. It comprises Semantic Analysis as it helps the machine understand the intended meaning and context of the text. The machines must establish relationships between various entities and intents in the data structure. Thus, NLU is that component of the NLP engine that accrues meaning based on the text machine processes.
Applications of NLU
- Sentiment Analysis
- Profanity Filtering
- Spam Filtering
- Speech Recognition
What is NLG?
It stands for Natural Language Generation. It is a significant part of the NLP and often leaves people awed when they witness it for the first time. After the machine has learned to read and understand the text, it essentially can take inputs in naturally written/spoken text.
The next logical step is to get the writing part correctly. Natural Language Generation helps the machine generate unstructured data closer to human language. NLG is based on the structured data generated by NLP and reflects the meaning derived by NLU.
Applications of NLG:
- Text Summarization
- Voice Assistants
- Image Captioning
It is rather hard and even impossible to distinguish use cases as using only NLP, NLU, or NLG. All the real-world applications of Natural Language Processing leverage the three heads of the NLP engine.
Future of NLP
NLP is an evolving field with its accuracy increasing just as much as its complexity. In the past, handwritten rules created the NLP models. As it was an insufficient approach, a shift to machine learning algorithms like Logistics Regression and SVM took place. Machine learning, although, was a hollow approach to NLP as it didn’t include NLU. As there was no understanding, there was no intelligence as well. But as Deep Learning became a part of NLP, multiple exciting trends came to the forefront. Some trends include:
- Sophistication in neural networks and the use of state-of-the-art NLP libraries like Spark NLP, spaCy, NLTK, etc., is bound to increase in the future. This rising excitement reflects in the increasing number of papers originally presented at the ACL conferences.
- Exponential growth in the availability of training data as more and more people go digital.
- Transfer learning decreasing the training time for models is another exciting trend. It would help in the growth of more NLP-based products in a lesser amount of time.
- Market intelligence monitoring
- Growth of intelligent chatbots.
- Multilingual NLP
- Sentiment Analysis on Social media.
- NLP models to start programming.
Frequently Asked Questions
Someone said Alexa is an example of NLU, while I think it’s an example of NLG. Who’s correct?
Both as well as None. Alexa, in its current form, uses both NLU and NLG. It uses NLU to attach a meaning to the spoken input you give, and once it understands and decides to respond, the response is generated using NLG.
I want to focus on NLP, but my friend says it has got no future, and I should go for Computer Vision instead. Is it so?
One can answer this question with the help of a market revenue statistic sourced from statistica.com. Worldwide market revenue for NLP in 2017 was 3185.7 million USD which rose is expected to be 43,289.9 million USD in 2025. Computer Vision’s market is immense, but NLP is not short of opportunities in any sense.