# Modelling Word Burstiness in Natural Language: A Generalised Polya Process for Document Language Models in Information Retrieval

arXiv: https://arxiv.org/abs/1708.06011 · HTML: https://mlsanity.com/paper/1708.06011/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 20 Aug 2017.

## Built on by (most cited first, 2 of 2)

- [Counter Turing Test CT^2: AI-Generated Text Detection is Not as Easy as You May Think -- Introducing AI Detectability Index](https://mlsanity.com/paper/2310.05030/) (2023)
- [Common TF-IDF variants arise as key components in the test statistic of a penalized likelihood-ratio test for word burstiness](https://mlsanity.com/paper/2604.00672/) (2026)

## Abstract (arXiv)

We introduce a generalised multivariate Polya process for document language modelling. The framework outlined here generalises a number of statistical language models used in information retrieval for modelling document generation. In particular, we show that the choice of replacement matrix M ultimately defines the type of random process and therefore defines a particular type of document language model. We show that a particular variant of the general model is useful for modelling term-specific burstiness. Furthermore, via experimentation we show that this variant significantly improves retrieval effectiveness over a strong baseline on a number of small test collections.