XSTEM: An exemplar-based stemming algorithm

Stemming is the process of reducing related words to a standard form by removing affixes from them. For example, eating, eats, and eaten can be reduced to the standard form eat by removing the -ing, -s, and -en from each. Stemming is a foundational step in many text processing pipelines, including information retrieval and language modeling [2], and as a feature reduction step for classification or lexical transfer learning tasks [1]. Most stemming algorithms are either rule-based or corpus-based. Rule-based stemmers use a set of manually created rules to transform words to their base form, typically in conjunction with reference to a dictionary for handling exceptions or modulating their output in some fashion. Corpus-based stemmers typically employ statistical machine learning methods to equate word forms based on distributional regularities derived from large amounts of text. Although researchers have demonstrated impressive results with corpus-based approaches (e.g., [2, and references therein]), rule-based stemming implementa-I was introduced to exemplar theory by Keith Johnson in a phonetics seminar at Ohio State. His model is called XMOD, which, as he notes, is the best name [7]. The name XSTEM comes from that.