Stemming is a technique in NLP that helps in reducing words to their root form. It mainly involves removing the suffix from words, such as plurals or verb conjugations. This enables us to group together words with similar meanings and improves search relevance and text analysis. However, one limitation of stemming is that it can sometimes lead to incorrect reductions. For example, 'running' and 'runner' both get reduced to 'run', although they represent different concepts. Additionally, stemmers can struggle with irregular words or those from different languages. Overall, stemming is a useful tool, but it's important to be aware of its limitations and consider other approaches like lemmatization if precision is crucial.

Stemming is widely used in NLP to normalize text and improve text analysis processes. By reducing words to their root form, stemming helps to overcome variations present in words due to different tenses, cases, or plural forms. This allows systems to treat words like 'run', 'ran', and 'running' as the same base word, simplifying analysis tasks. However, stemming has its limitations. It can lead to the loss of some grammatical context, as the process doesn't consider word meanings or parts of speech. Furthermore, overly aggressive stemming can generate false positives or create problems in tasks like sentiment analysis. So, while stemming is a valuable technique, it's crucial to be cautious in its application and consider specific requirements of each use case.