# Document Classification by Inversion of Distributed Language Representations

arXiv: https://arxiv.org/abs/1504.07295 · HTML: https://mlsanity.com/paper/1504.07295/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 27 Apr 2015.

## Built on by (most cited first, 4 of 4)

- ["Average" Approximates "First Principal Component"? An Empirical Analysis on Representations from Neural Language Models](https://mlsanity.com/paper/2104.08673/) (2021)
- [Semantic maps and metrics for science Semantic maps and metrics for science using deep transformer encoders](https://mlsanity.com/paper/2104.05928/) (2021)
- [Temporal Sequencing of Documents](https://mlsanity.com/paper/2311.02578/) (2023)
- [Validating and Exploring Large Geographic Corpora](https://mlsanity.com/paper/2403.08198/) (2024)

## Abstract (arXiv)

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.