Guessing morphology from terms and corpora

Abstract

This study proposes an algorithm for automatically acquiring morphological Iinks between words. This algorithm relies on the concurrent use of a corpus and a list of multi-word terms, and does not require any prior linguistic knowledge. The four steps of the algorithm are (1) single-word truncation, (2) conflation of multi-word terms, (3) classification and filtering, and (4) clustering of contiation clasea. At each step a precise evaluation is performed in order to chose the optimal parameters. The final results indicate a clustering of 45% of the classes with a prectilon of 87Y0. The derivational knowledge acquired through this method can be used for conceiving a domain-oriented stemmer for scientific and technical corpora.