Join GitHub today
GitHub is home to over 28 million developers working together to host and review code, manage projects, and build software together.Sign up
grammar induction libraries that take advantage of predictability effects http://homepages.inf.ed.ac.uk/s0930006/
Fetching latest commit…
Cannot retrieve the latest commit at this time.
|Failed to load latest commit information.|
This is an implementation of CCM and DMV (e.g. [1,2]), with extensions to run on two input streams. The library is called "predictabilityParsing" because one of the streams will be words, and the other will be (a quantization of) word duration. The idea is to learn something about syntactic structure by exploiting predictability effects (e.g. ). That is why this library is called "predictabilityParsing."  (2002). Klein, D., & Manning, C. A Generative Constituent-Context Model for Improved Grammar Induction, Dan Klein and Chris Manning, In Proceedings of the Association for Computational Linguistics (ACL).  (2004). Klein, D., & Manning, C. Corpus-Based Induction of Syntactic Structure: Models of Dependency and Constituency, In Proceedings of the Association for Computational Linguistics (ACL).  (2006). Gahl, S., Garnsey, S., Fisher, C. & Matzen, L. "That sounds unlikely": Syntactic probabilities affect pronunciation. Proceedings of the 28th Annual Conference of the Cognitive Science Society. There are a few elaborations on these models as well.