Document Classification by Inversion of Distributed Language Representations
Computation and Language
2015-07-27 v3 Information Retrieval
Applications
Abstract
There have been many recent advances in the structure and measurement of distributed language models: those that map from words to a vector-space that is rich in information about word choice and composition. This vector-space is the distributed language representation. The goal of this note is to point out that any distributed representation can be turned into a classifier through inversion via Bayes rule. The approach is simple and modular, in that it will work with any language representation whose training can be formulated as optimizing a probability model. In our application to 2 million sentences from Yelp reviews, we also find that it performs as well as or better than complex purpose-built algorithms.
Cite
@article{arxiv.1504.07295,
title = {Document Classification by Inversion of Distributed Language Representations},
author = {Matt Taddy},
journal= {arXiv preprint arXiv:1504.07295},
year = {2015}
}