A Simpler, Intuitive Approach to Morpheme Induction

author: Samarth Keshava, Yale University
coauthor: Emily Pitler, Yale University
published: Feb. 25, 2007,   recorded: April 2006,   views: 3110


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We present a simple, psychologically plausible algorithm to perform unsupervised learning of morphemes. The algorithm is most suited to Indo-European languages with a concatenative morphology, and in particular English. We will describe the two approaches that work together to detect morphemes: 1) finding words that appear as substrings of other words, and 2) detecting changes in transitional probabilities. This algorithm yields particularly good results given its simplicity and conciseness: evaluated on a set of 532 human-segmented English words, the 252-line program achieved an F-score of 80.92% (Precision: 82.84% Recall: 79.10%).

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