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4 Differences between NLP and NLU

  • Vanshika Kaushik
  • Sep 29, 2021
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While giving Alexa a command to play your favourite song have you ever paused for a while and questioned yourself “how is it even possible?”. Has it ever happened that a youtube comment left you sceptical and you found it marked as a tag “this comment might be unsuitable”, if you are still wondering how can computers do all that today.


Lemme break it down for you its NLP, that has simplified and even paved a way for human machine oral interaction. Listening to your commands, check! Conveying them to the machine check! 


Just a simple voice command of play a workout playlist can get you grooving, if you are still wondering that NLP made the machine understand your real emotions well you might be wrong because yes NLP does the processing but it cannot understand you entirely, understanding the real intent of your message is a part of NLU stuck up don’t be just follow the blog to understand the key differences between NLP and NLU. 


But first what is actually NLP?



What is NLP?



In simple words Natural Language Processing is a branch of artificial intelligence that gives machines the ability to understand human text and spoken words. Think of it like this: the way you are talking to your friend today, is the same way you are interacting with a machine and it has become possible because of Natural Language Processing. 


NLP converts the “written text” into structured data; parsing, speech recognition and part of speech tagging are a part of NLP. NLP breaks down the language into small and understable chunks that are possible for machines to understand.


NLP can be thought of as anything that is related to words, speech, written text, or anything similar. Google, use NLP to show the desired search results. 


Models in NLP are usually sequential models, they process the queries and can modify each other.


Lemmatization and Stemming (this process involves converting the word into its base form), tokenization (splitting the whole text into the list of tokens), Named Entity Recognition (identification of categories and their classification)  are some NLP techniques that are employed for extracting information. 


(Related Blog: Natural Language Processing: A Discussion for NLP Working and Applications)


Follow this podcast to know more about NLP.


You must have understood the basics of NLP. Now let’s understand the meaning of NLU. 


(Related Blog: NLP Guide For Beginners)



What is NLU?



NLU stands for Natural Language Understanding, it is a subfield of Natural Language Processing (NLP). NLU uses software to understand the input. Input can be in the form of sentences, text, or speech.


NLU analyzes the data. It enables computers to understand different human languages. 


NLU analyzes the data to determine its actual meaning. NLU uses various algorithms for converting human speech into structured data that can be understood by computers. 


With the help of NLU, and machine learning computers can analyze the data. They understand the hidden intent of a sentence via NLU. NLU enables understanding of complex data. 


These days major IT companies like Amazon, Apple, Google, Microsoft, are building chatbots using NLU. NLU is primarily concerned about pulling the intent of the sentence, it derives the actual meaning of the sentence.


(Must Check: Syntactic Analysis: an Overview)


Textual entailment (shows direct relationship between text fragments) is a part of NLU. NLU deals with the actual understanding of the machine. NLU smoothens the process of human machine interaction; it bridges the gap between data processing and data analysis.


Let’s understand the key differences between these data processing and data analyzing future technologies. 

This image describes natural language processing and natural language understanding

Visual representation of NLP and NLU


Differences between NLP and NLU


While NLP converts the raw data into structured data for its processing, NLU enables the computers to understand the actual intent of structured data. NLP is capable of processing simple sentences,NLP cannot process the real intent or the actual meaning of complex sentences. Lets understand this via an example.


I need some cookies. While NLP will have to process simple sentences like this. NLU has to understand things like I’m craving for something sweet, I might need a cookie break to relieve stress. 


  1. Syntax v/s Semantics 


Natural Language Processing is primarily concerned with the “syntax of the language”. NLP will focus on the structure of the language, and its presentation. It will focus on other grammatical aspects of the written language; tokenization, lemmatization and stemming are some ways to extract information from a particular text.


(Must Check: Top 12 Natural Processing Languages (NLP) Libraries with Python)


Natural language Understanding is mainly concerned with the meaning of language. NLU doesn’t focus on the word formation or punctuation in a sentence. Its prime objective is to bring out the actual intent of the speaker. It analyzes the real intention of the speaker. 


Let's take a look at the following sentences Samaira is salty as her parents took away her car. This sentence will be processed by NLP as Samaira tastes salty though the actual intent of the sentence is Samaira is angry. 


For the second sentence: How was your day ? Hey come on spill the tea! this sentence will be processed by NLP as spilling the tea on the surface. It is only via NLU the computer will understand the real intent of the language i.e. for the given sentence spilling tea means sharing details of the day. 

This image describes the differences between nlp and nlu

 Differences in processing queries NLP v/s NLU


  1. Processing Data v/s Understanding Data


The main task of NLP is data conversion. NLP converts the unstructured data into structured data. When an individual gives a voice command to the machine it is broken into smaller parts and later it is processed. 


When given a query of what is the meaning of artificial intelligence, it will automatically break down the sentence in the form of tokens and will process the sentence as what-is-the-meaning-of-artificial intelligence (word tokenization). 


While NLP will process the query NLU will decipher the meaning of the query. NLU will use techniques like sentiment analysis and sarcasm detection to understand the meaning of the sentence. It will show the query based on its understanding of the main intent of the sentence. 


(Related Blog: Top 10 Examples of NLP)


  1. Applications NLP v/s NLU


Both NLP and NLU are used in multiple industries. NLP can be used for information extraction, it is used by many big companies for extracting particular keywords. By putting a keyword based query NLP can be used for extracting product’s specific information.


From the million records NLP can selectively choose the relevant one based on the individual’s query. Text extraction can be used for “extracting required information’ in the shortest timespan. 


(Must Check: Review-based Recommendations System)


NLP is also used in autocorrect. Ginger, Grammartool, Language tool are some companies that use NLP for autocorrection. NLP can also be used for text summarization.


(Related Blog: Top 10 Applications for Natural Language Processing (NLP)


 NLU is used for sarcasm detection. Sarcasm detection is an important tool that is employed for the assessment of human’s emotions. NLU can be used to understand the sarcasm that is camouflaged in the form of normal sentences. 


NLU can also be used in sentiment analysis (understanding the emotions of disgust, anger, and sadness). 


NLU is also used for intent classification. Intent Classification means understanding customer emotions from emails, tweets, and chats. Intent classification is helpful for research in market intelligence; it can also be used for understanding customer behaviour before launching a particular product in the market. 



  1. Translation v/s Transcreation


Translations have become an integral part of everyday life. For travellers the easiest part is simply using google translate to know about the key terminologies of the countries they are living in. 


Translation and Transcreation are two completely different terms, while NLP can be used for making translations NLU can be used for transcreation. 


Translation means the literal word to word translation of sentences, NLP can be used for translation but when it comes to phrases and idioms the translations process fails miserably in situations like that transcreation is used. 


Transcreation ensures that every line in the sentence is not converted directly into the desired language. 


It ensures that the main meaning of the sentence is conveyed in the targeted language without word by word translation. NLU can be used for transcreation. It conveys the meaning of the sentence in the targeted language without word by word translation.


 For complex situations NLU is helpful. It can be used for advanced level translations. Translation tools like Google Translate uses NLP and NLU for supporting translation. 


Learn more about the differences between NLP and NLU via this video 


Summing Up


Both NLP and NLU have a myriad of applications. NLP and NLU are so closely related that at times these terms are used interchangeably. NLU is a small part of the larger concept of NLP. 


While Natural Language Processing is concerned with the linguistic aspect of a language Natural Language Understanding is concerned about its intent. In a nutshell NLP and NLU are mostly used together in a combination. Though different to an extent their correlation is what is driving the change in various modern day industries. 

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