NLP, NLU, and NLG: The World of a Difference
How often have you noticed tech enthusiasts confuse Natural Language Understanding (NLU) and Natural Language Generation (NLG)? NLP and NLG are interrelated and sound similar and are sometimes used interchangeably. Both NLP and NLG are separate branches of AI and precisely subsets of NLP. In this post, we are defining NLP, NLU, and NLG to highlight the differences between them.
Natural Learning Processing (NLP)
NLP is just one fragment nestled under the big umbrella called artificial intelligence or AI. This branch of AI fuses different languages including computational linguistics, and rule-based modeling of human language, along with machine learning, statistical, and deep learning models. The combination of these technologies enables computers to understand human language which could be in the form of voice data or just text. With this, the computer will also be capable of understanding the writer or speaker’s intent and sentiment.
Voice-operated GPS, customer service chatbots, digital assistants, and speech-to-text dictation software are some of the most common examples of NLP. Alexa could be a more popular example here. Typically, NLP enables the computer to perform the following tasks:
- Respond to spoken commands.
- Translate text from one language to another.
- Summarize huge text volumes in real time.
Human language is vivid and ambiguous and it encapsulates a wide range of emotions and so, it can get a little tricky as well as exhausting to accurately write software that perfectly captures the real meaning of the voice data or a text. The human language is filled with a myriad of variations like sarcasm, idioms, homophones, metaphors, etc, and breaking them down or embedding them as is into software is a herculean task.
To assist the computers in correctly inferring the voice command or written command, NLP uses tasks like speech recognition, speech tagging, word sense disambiguation, sentiment analysis, natural language generation, etc.
Natural Language Understanding
As we mentioned earlier, NLG is a subset of NLP and it tries to understand the meaning of a sentence using syntactic and semantic analysis. The syntactic analysis looks at the grammar and the structure of a sentence and semantics, on the other hand, infers the intended meaning. With the help of relevant ontology and a data structure, NLU offers the relationship between words and phrases. For humans, this comes quite naturally, but in the case of machines, a combination of the above analysis helps them to understand the meaning of several texts. NLU is more helpful in data mining to assess consumer behavior and attitude. With sentiment analysis, brands can tap the social media domain to monitor the customer’s feedback through negative and positive comments. By closely observing the negative comments, businesses successfully identify and address the pain points.
Natural Language Generation
While NLU is more focused on understanding language and sentence construction, NLG is more about enabling computers to write. In broader terms, natural language generation focuses more on creating a human language text response based on the set of data input. With the help of text-to-speech services, the text response can be converted into a speech format.
To phrase appropriate responses, NLG considers language rules which are based on semantics, morphology, lexicons, and syntax. There are 3 stages involved:
- Text planning: This stage focuses on formulating general content and ordering them logically.
- Sentence planning: Punctuations, text flow, and para breaks are the highlights of this stage while additionally adding pronouns or conjunctions wherever required.
- Realization: Grammatical accuracy is the most important part of this stage. Also, at this stage, it is double-checked that punctuation and conjugation rules are followed.
Understanding the Difference between NLP, NLU, and NLG
NLP, as we discussed earlier is a branch of AI however, both NLU and NLG are sub-branches of NLP. While NLP tries to understand a command via voice data or text, NLU on the other hand helps facilitate a dialog with the computer through natural language. Both NLU and NLP are capable of understanding human language; NLU can interact with even untrained individuals to decipher their intent. Sure, NLU is programmed in a way that it can understand the meaning even if there are human errors such as mispronunciations or transposed words. Though NLG is also a subset of NLP, there is a more distinct difference when it comes to human interaction. Usually, computer-generated content is straight, robotic, and lacks any kind of engagement. The primary role of NLG is to make the response more fluid, engaging, and interesting as an actual human would do. It does so by identifying the crux of the document and then using NLP to respond in the user’s native language. Based on a set of data about a particular event, NLG can automatically generate a new article about the same.
In a nutshell, here is the summary of NLP vs. NLG vs. NLU.
NLP converts unstructured data into a structured format to help computers clearly understand speech and written commands and produce relevant responses. While NLU closely follows machine reading comprehension through grammar and context to correctly understand the meaning of a sentence, NLG has a strong focus on sentence construction in English or other languages based on a specific data set.
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