| Abstract | We present a nonparametric Bayesian model that jointly induces morpheme segmentations of each language under consideration and at the same time identifies cross-lingual morpheme patterns, or abstract morphemes. |
| Conclusions and Future Work | We started out by posing two questions: (i) Can we exploit cross-lingual patterns to improve unsupervised analysis? |
| Introduction | In this paper we investigate two questions: (i) Can we exploit cross-lingual correspondences to improve unsupervised language |
| Introduction | Our results indicate that cross-lingual patterns can indeed be exploited successfully for the task of unsupervised morphological segmentation. |
| Model | The model is fully unsupervised and is driven by a preference for stable and high frequency cross-lingual morpheme patterns. |
| Model | Our model utilizes a Dirichlet process prior for each language, as well as for the cross-lingual links (abstract morphemes). |
| Multilingual Morphological Segmentation | In the following section, we describe a model that can model both generic cross-lingual patterns (fy and 19-), as well as cognates between related languages (ktb for Hebrew and Arabic). |