For centuries, scholars have explored the deep links among human languages. In this paper, we present a class of probabilistic models that use these links as a form of naturally occurring supervision. These models allow us to substantially improve performance for core text processing tasks, such as morphological segmentation, part-of-speech tagging, and syntactic parsing. Besides these traditional NLP tasks, we also present a multilingual model for the computational decipherment of lost languages. 1. Overview Electronic text is currently being produced at a vast and unprecedented scale across the languages of the world. Natural Language Processing (NLP) holds out the promise of automatically analyzing this growing body of text. However, over the last several decades, NLP research efforts have focused on the English language, often neglecting the thousands of other languages of the world (Bender, 2009). Most of these languages are currently beyond the reach of NLP technology due to several factors. One of these is simply the lack of the kinds of hand-annotated linguistic resources that have helped propel the performance of English language systems. For complex tasks of linguistic analysis, hand-annotated corpora can be prohibitively time-consuming and expensive to produce. For example, the most widely used annotated corpus in the English language, the Penn Treebank (Marcus et al., 1994), took years for a team of professional linguists to produce. It is unrealistic to expect such resources to ever exist for the majority of the world’s languages.