NLP vs NLU vs. NLG: the differences between three natural language processing concepts
This sentence will be processed by NLP as Samaira tastes salty though the actual intent of the sentence is Samaira is angry. 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. NLP can be thought of as anything that is related to words, speech, written text, or anything similar.
If you enjoyed it please do remember to leave a few claps (it means a lot) and leave a follow if you enjoy my other content too. Or, if you have a lot of information from a market survey, you can use NLU to pull out statistical information and get a sense of what all the answers mean. While Natural Language Processing is concerned with the linguistic aspect of a language Natural Language Understanding is concerned about its intent. Though different to an extent their correlation is what is driving the change in various modern day industries.
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When an unfortunate incident occurs, customers file a claim to seek compensation. As a result, insurers should take into account the emotional context of the claims processing. As a result, if insurance companies choose to automate claims processing with chatbots, they must be certain of the chatbot’s emotional and NLU skills.
NLP has many subfields, including computational linguistics, syntax analysis, speech recognition, machine translation, and more. Some of the basic NLP tasks are parsing, stemming, part-of-speech tagging, language detection and identification of semantic relationships. If you ever diagrammed sentences in primary school then you have done this manually before. As already seen in the above information, NLU is a part of NLP and thus offers similar benefits which solve several problems. In other words, NLU helps NLP to achieve more efficient results by giving a human-like experience through machines. Our open source conversational AI platform includes NLU, and you can customize your pipeline in a modular way to extend the built-in functionality of Rasa’s NLU models.
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On our quest to make more robust autonomous machines, it is imperative that we are able to not only process the input in the form of natural language, but also understand the meaning and context—that’s the value of NLU. This enables machines to produce more accurate and appropriate responses during interactions. As humans, we can identify such underlying similarities almost effortlessly and respond accordingly. But this is a problem for machines—any algorithm will need the input to be in a set format, and these three sentences vary in their structure and format. And if we decide to code rules for each and every combination of words in any natural language to help a machine understand, then things will get very complicated very quickly. While natural language processing (NLP), natural language understanding (NLU), and natural language generation (NLG) are all related topics, they are distinct ones.
As such, it deals with lower-level tasks such as tokenization and POS tagging. Natural Language Processing focuses on the interaction between computers and human language. It involves the development of algorithms and techniques to enable computers to comprehend, analyze, and generate textual or speech input in a meaningful and useful way.
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