Paper: Latent Dynamic Model with Category Transition Constraint for Opinion Classification

ACL ID C14-1128
Title Latent Dynamic Model with Category Transition Constraint for Opinion Classification
Venue International Conference on Computational Linguistics
Session Main Conference
Year 2014
Authors

Latent models for opinion classification are studied. Training a probabilistic model with a number of latent variables is found unstable in some cases; thus this paper presents how to construct a stable model for opinion classification by constraining classification transitions. The baseline model is a CRF classification model with plural latent variables, dynamically constructed from the dependency parsed tree. The aim of the baseline model is to have each latent variable convey a partial sentiment of the input sentence which is not explicitly given in the training data, and the complete sentiment of the sentence is computed by summing up such partial sentiment where those latent variables hold. Since such a conventional model has many degeneracies in principle, a model with a category tr...