The aim of document clustering is to produce coherent clusters of similar documents. Although most document clustering algorithms perform well in specific knowledge domains, processing cross-domain document repositories is still a challenge. This difficulty can be attributed to word ambiguity and explained by the observation that monosemic words are more domainoriented than polysemic ones. Document clustering algorithms normally employ text normalization techniques, such as the Porter stemming algorithm. This paper describes a semantically enhanced text normalization algorithm developed for the purpose of improved document clustering. Corpus consistency achieved by the proposed algorithm is compared with the consistency produced by the Porter stemmer. The experimental evidence shows that semantic disambiguation improves clustering performance compared to traditional normalization methods.