suffixstripping

Suffixstripping is the computational process of removing suffix morphemes from a word to yield a base form or stem. It is used in natural language processing and information retrieval to normalize inflected or derived forms so that related terms can be treated as equivalent.

In practice, suffixstripping is often implemented as a rule-based stemming technique. A system uses a predefined list of suffixes and a set of contextual rules to determine when and how to trim a suffix, sometimes replacing it with an alternative ending. Popular implementations include the Porter stemming algorithm and the Snowball framework, which provide language-specific rule sets. Suffixstripping can be applied to various languages, though English examples are the most widely discussed in early literature.

Common goals of suffixstripping include reducing different word forms to a common stem, improving search accuracy, and simplifying text for downstream analysis. For example, running, runs, and ran may be reduced to run; happiness and happier might be reduced depending on the rules in use. However, naive stripping can produce incorrect stems, leading to over-stemming (too aggressive reduction) or under-stemming (insufficient reduction).

Applications of suffixstripping include information retrieval, text mining, clustering, and preprocessing for machine learning models. It is often contrasted with lemmatization, which aims to map words to canonical dictionary forms, potentially preserving more semantic information. Challenges include handling irregular forms, language-specific morphology, and balancing precision with recall.