A Bayesian Model for Discovering Typological Implications

A standard form of analysis for linguistic typology is the universal implication. These implications state facts about the range of extant languages, such as “if objects come after verbs, then adjectives come after nouns.” Such implications are typically discovered by painstaking hand analysis over a small sample of languages. We propose a computational model for assisting at this process. Our model is able to discover both well-known implications as well as some novel implications that deserve further study. Moreover, through a careful application of hierarchical analysis, we are able to cope with the well-known sampling problem: languages are not independent.

• Type: Conference
• Year: 2007
• Where: ACL
• Authors: Hal Daumé III, Lyle Campbell
• Keywords: ACL 2007, Computational Linguistics, Implications State Facts, Such Implications, Well-known Implications