Lemmatization and Stemming techniques of NLP use lexical knowledge to get the correct base word. In lemmatization, the intermediate base form should have a meaning. In Stemming, the main aim is to reduce the derived words to their word stem and do not necessarily have a meaning.
For example: If we do lemmatization for words like Entitling and Entitled, we will get Entitle. If we do stemming for the words Entitling and Entitled, we will get Entitl.
Lemmatization is used when the meaning of words is important like in implementing Question answering application. Stemming is used when the meaning of words is not much important. Example: Spam Classification
In case of more detailed explanation, you can watch this video on NLP by python:
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