YASS

TLDR

Stemmers reduce words to stems to boost recall in information retrieval, but most rely on extensive language‑specific rules that exist only for a few languages, so statistical methods are employed when resources are scarce. We propose a clustering‑based method to discover equivalence classes of root words and their morphological variants. The method clusters the lexicon using defined string‑distance measures to form equivalence classes, then evaluates the resulting stemmer against Porter’s and Lovin’s on the AP and WSJ subcollections of the Tipster dataset with 200 queries. The clustering‑based stemmer achieves performance comparable to Porter’s and Lovin’s in average precision and relevant documents retrieved, and consistently improves retrieval for resource‑poor languages such as French and Bengali.

Abstract

Stemmers attempt to reduce a word to its stem or root form and are used widely in information retrieval tasks to increase the recall rate. Most popular stemmers encode a large number of language-specific rules built over a length of time. Such stemmers with comprehensive rules are available only for a few languages. In the absence of extensive linguistic resources for certain languages, statistical language processing tools have been successfully used to improve the performance of IR systems. In this article, we describe a clustering-based approach to discover equivalence classes of root words and their morphological variants. A set of string distance measures are defined, and the lexicon for a given text collection is clustered using the distance measures to identify these equivalence classes. The proposed approach is compared with Porter's and Lovin's stemmers on the AP and WSJ subcollections of the Tipster dataset using 200 queries. Its performance is comparable to that of Porter's and Lovin's stemmers, both in terms of average precision and the total number of relevant documents retrieved. The proposed stemming algorithm also provides consistent improvements in retrieval performance for French and Bengali, which are currently resource-poor.