Modelling Word Burstiness in Natural Language: A Generalised Polya Process for Document Language Models in Information Retrieval
This work addresses the challenge of accurately modeling term-specific burstiness in information retrieval, offering a domain-specific improvement for document language models.
The authors tackled the problem of modeling word burstiness in natural language by introducing a generalized multivariate Polya process for document language models, which significantly improved retrieval effectiveness over a strong baseline on several small test collections.
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.