Weakly supervised supertagging with grammar-informed initialization
Jason Baldridge · 2008
Much previous work has investigated weak supervision with HMMs and tag dictionaries for part-of-speech tagging, but there have been no similar investigations for the harder problem of supertagging.Here, I show that weak supervision for supertagging does work, but that it is subject to severe performance degradation when the tag dictionary is highly ambiguous.I show that lexical category complexity and information about how supertags may combine syntactically can be used to initialize the transition distributions of a first-order Hidden Markov Model for weakly supervised learning.This initialization proves more effective than starting with uniform transitions, especially when the tag dictionary is highly ambiguous.