| Conclusions and future work | The models presented here derive their predictions by modelling predicate-argument plausibility through the intermediary of latent variables . |
| Conclusions and future work | We also anticipate that latent variable models will prove effective for learning selectional preferences of semantic predicates (e. g., FrameNet roles) where direct estimation from a large corpus is not a viable option. |
| Related work | In Rooth et al.’s model each observed predicate-argument pair is probabilistically generated from a latent variable , which is itself generated from an underlying distribution on variables. |
| Related work | The use of latent variables , which correspond to coherent clusters of predicate-argument interactions, allow probabilities to be assigned to predicate-argument pairs which have not previously been observed by the model. |
| Related work | The work presented in this paper is inspired by Rooth et al.’s latent variable approach, most directly in the model described in Section 3.3. |
| Results | Latent variable models that use EM for inference can be very sensitive to the number of latent variables chosen. |
| Three selectional preference models | Each model has at least one vocabulary of Z arbitrarily labelled latent variables . |
| Three selectional preference models | fzn is the number of observations where the latent variable 2 has been associated with the argument type n, fzv is the number of observations where 2 has been associated with the predicate type 2) and fzr is the number of observations where 2 has been associated with the relation 7“. |
| Three selectional preference models | In Rooth et al.’s (1999) selectional preference model, a latent variable is responsible for generating both the predicate and argument types of an observation. |
| Introduction | An HMM is a generative probabilistic model that generates each word 5137; in the corpus conditioned on a latent variable Y}. |
| Introduction | Each Y; in the model takes on integral values from 1 to K, and each one is generated by the latent variable for the preceding word, Y};_1. |
| Introduction | In response, we introduce latent variable models of word spans, or sequences of words. |
| Inference | In order to do so, we need to integrate out all the other latent variables in our model. |
| Inference | To do so tractably, we use Gibbs sampling to draw each latent variable conditioned on our current sample of the others. |
| Inference | Even with a large number of sampling rounds, it is difficult to fully explore the latent variable space for complex unsupervised models. |