This article surveys work on Unsupervised Learning of Morphology. We define
Unsupervised Learning of Morphology as the problem of inducing a description (of some …

We present two methods for unsupervised segmentation of words into morpheme-like units.
The model utilized is especially suited for languages with a rich morphology, such as …

We present a model family called Morfessor for the unsupervised induction of a simple
morphology from raw text data. The model is formulated in a probabilistic maximum a …

This work presents an algorithm for the unsupervised learning, or induction, of a simple
morphology of a natural language. A probabilistic maximum a posteriori model is utilized …

This paper presents MED, the main system of the LMU team for the SIGMORPHON 2016
Shared Task on Morphological Reinflection as well as an extended analysis of how different …

Morphological reinflection is the task of generating a target form given a source form, a
source tag and a target tag. We propose a new way of modeling this task with neural …

During visual word processing readers identify chunks of co-occurring letters and code for
their typical position within words. Using an artificial script, we examined whether these …

We propose an algorithm to automatically induce the morphology of inflectional languages
using only text corpora and no human input. Our algorithm combines cues from orthography …

In this thesis I present various algorithms for the unsupervised machine learning of aspects
of natural languages using a variety of statistical models. The scientific object of the work is …

We present an inference algorithm that organizes observed words (tokens) into structured
inflectional paradigms (types). It also naturally predicts the spelling of unobserved forms that …