  This paper focuses on unsupervised modeling of morphological families,
collectively comprising a forest over the language vocabulary. This formulation
enables us to capture edgewise properties reflecting single-step morphological
derivations, along with global distributional properties of the entire forest.
These global properties constrain the size of the affix set and encourage
formation of tight morphological families. The resulting objective is solved
using Integer Linear Programming (ILP) paired with contrastive estimation. We
train the model by alternating between optimizing the local log-linear model
and the global ILP objective. We evaluate our system on three tasks: root
detection, clustering of morphological families and segmentation. Our
experiments demonstrate that our model yields consistent gains in all three
tasks compared with the best published results.
