The right-side context of a non-terminal category -- the probability of generating a category to the right of the current constituent 's category -- corresponds directly to the category transitions used for the HMM supertagger of Garrette et al. ( 2014 ) .
weaklysupervised bayesian learning of a ccg supertagger
Dan Garrette Chris Dyer Jason Baldridge Noah A Smith
In order to encourage our model to choose trees in which the constituent labels `` fit '' into their supertag contexts , we want to bias our context parameters toward context categories that are combinable with the constituent label . The right-side context of a non-terminal category -- the probability of generating a category to the right of the current constituent 's category -- corresponds directly to the category transitions used for the HMM supertagger of Garrette et al. ( 2014 ) . Thus , the right-side context prior mean Î¸RCTX-0 can be biased in exactly the same way as t the HMM supertagger 's transitions : toward context supertags that connect to the constituent label .
The right-side context of a non-terminal category -- the probability of generating a category to the right of the current constituent 's category -- corresponds directly to the category transitions used for the HMM supertagger of @@CITATION .