Stemming

Stemming is a text preprocessing technique used in text analysis and natural language processing. The main goal of stemming is to reduce words to their root form or stem.

Here are some key points about stemming:

It helps normalize words to their common base form. For example, "playing", "played", "plays" are all stemmed to the root word - "play".
This allows counting or grouping words like plural nouns, verbs in different tenses, and derived adjectives or adverbs the same way even if they have different suffixes.
Popular stemming algorithms include Porter's stemmer, Lancaster stemmer and Snowball stemmer. They apply language-specific rules and heuristics to chop off endings.
Stemming improves performance for applications like search engines, information retrieval and text analysis by reducing the dictionary size and consolidating variants of the same root.
However, stemming can also increase ambiguity as words with different meanings may get conflated. For example, "organize", "organization" would get stemmed to "organ".
Stemming is faster but crude compared to lemmatization which uses vocabulary and morphologic analysis to return the canonical form of a word accurately.
Stemming works best on formal texts and may not perform well on informal language with slang and abbreviations.

In summary, stemming is a fast and simple NLP technique that reduces words to a common base form by stripping suffixes using heuristic rules. It helps improve search and analytics but can also introduce errors.