Poststemming

Poststemming is a post-processing step applied after an initial stemming operation in natural language processing and information retrieval. The goal is to address limitations of stemming, such as over-stemming, under-stemming, or loss of discriminative information, by refining the tokens produced by the stemmer.

Techniques used in poststemming include suffix restoration, where common inflectional endings are re-attached to stems based on language rules or dictionaries; contextual disambiguation, where part-of-speech or surrounding words guide which surface form is most appropriate; and hybrid approaches that combine stemming with lemmatization or lightweight morphological analysis to balance speed and accuracy.

Applications of poststemming appear in search engines, text classification, and cross-lingual information retrieval, where preserving some surface-form information after stemming can improve precision or recall. For example, after generating stemmed tokens, a poststemming step might re-expand ambiguous stems to multiple candidate surface forms or adjust term weights to reflect their likely original forms.

Challenges include the risk of reintroducing noise, increased computational overhead, and the need for language-specific resources. Poststemming is generally seen as a compromise between fast stemming and more accuracy-oriented lemmatization; it is not a replacement for lemmatization but rather a supplementary step in certain pipelines.

Relation to other concepts: stemming reduces words to root forms; lemmatization maps to canonical lemmas; poststemming sits between these approaches as a refinement stage aiming to improve downstream tasks without full morphological analysis.