HiTR: Hierarchical Topic Model Re-estimation for Measuring Topical Diversity of Documents

TL;DR

This paper introduces HiTR, a hierarchical re-estimation method that enhances topic models by removing general and impure topics, leading to more accurate measurement of document topical diversity.

Contribution

The paper proposes a novel hierarchical re-estimation process to improve topic model quality for better diversity measurement, addressing generality and impurity issues.

Findings

HiTR outperforms existing methods on PubMed dataset.

Re-estimation reduces topic impurity and generality.

Improved interpretability of topic models.

Abstract

A high degree of topical diversity is often considered to be an important characteristic of interesting text documents. A recent proposal for measuring topical diversity identifies three distributions for assessing the diversity of documents: distributions of words within documents, words within topics, and topics within documents. Topic models play a central role in this approach and, hence, their quality is crucial to the efficacy of measuring topical diversity. The quality of topic models is affected by two causes: generality and impurity of topics. General topics only include common information of a background corpus and are assigned to most of the documents. Impure topics contain words that are not related to the topic. Impurity lowers the interpretability of topic models. Impure topics are likely to get assigned to documents erroneously. We propose a hierarchical re-estimation process aimed at removing generality and impurity. Our approach has three re-estimation components: (1) document re-estimation, which removes general words from the documents; (2) topic re-estimation, which re-estimates the distribution over words of each topic; and (3) topic assignment re-estimation, which re-estimates for each document its distributions over topics. For measuring topical diversity of text documents, our HiTR approach improves over the state-of-the-art measured on PubMed dataset.