Morphological analysis with FSTs: Speech and Language Processing 2nd Ed, Ch 3

Lecture: NLP 201 - Fall 2020 Oct 15

Related work of Chahuneau 2013 (section 6) gives a very quick overview

Goldwater et al 2005 - Interpolating Between Types and Tokens by Estimating Power-Law Generators “We show that taking a particular stochastic process – the Pitman-Yor process – as an adaptor justifies the appearance of type frequencies in formal analyses of natural language, and improves the performance of a model for unsupervised learning of morphology.”

Chahuneau et al 2013 - Knowledge-Rich Morphological Priors for Bayesian Language Models Combines a finite-state guesser (that was constructed in 3 hours) with Bayesian non-parametrics to learn the correct morphological analysis. Limitation: assumes each word has one best morphological analysis, taken out of context. This could be corrected with contextualized model, like a sequence model for the part-of-speech tags

Schwartz et al 2019 - Neural Polysynthetic Language Modelling

Lane Schwartz

FSAs and FSTs

Morphology