Minimally Supervised Morphological Analysis by Multimodal Alignment

This paper presents a corpus-based algorithm capable of inducing inflectional morphological analyses of both regular and highly irregular forms (such as broughtbring) from distributional patterns in large monolingual text with no direct supervision. The algorithm combines four original alignment models based on relative corpus frequency, contextual similarity, weighted string similarity and incrementally retrained inflectional transduction probabilities. Starting with no paired <inflection,root> examples for training and no prior seeding of legal morphological transformations, accuracy of the induced analyses of 3888 past-tense test cases in English exceeds 99.2% for the set, with currently over 80% accuracy on the most highly irregular forms and 99.7% accuracy on forms exhibiting non-concatenative suffixation. 1 Task Definition This paper presents an original and successful algorithm for the nearly unsupervised induction of inflectional morphological analyzers, with a focus on ...

• Type: Conference
• Year: 2000
• Where: ACL
• Authors: David Yarowsky, Richard Wicentowski
• Keywords: ACL 2000, ACL 2007, Inflectional Morphological Analyses, Inflectional Morphological Analyzers, Irregular Forms