Topics and Label Propagation: Best of Both Worlds for Weakly Supervised Text Classification

TL;DR

This paper introduces a weakly supervised text classification method combining label propagation and topic modeling, requiring minimal supervision and achieving high accuracy by enriching document graphs with topic nodes.

Contribution

It presents a novel approach that integrates label propagation with topic modeling to reduce supervision in text classification tasks.

Findings

Effective on various datasets

Outperforms state-of-the-art weak supervision methods

Requires only minimal topic labels

Abstract

We propose a Label Propagation based algorithm for weakly supervised text classification. We construct a graph where each document is represented by a node and edge weights represent similarities among the documents. Additionally, we discover underlying topics using Latent Dirichlet Allocation (LDA) and enrich the document graph by including the topics in the form of additional nodes. The edge weights between a topic and a text document represent level of "affinity" between them. Our approach does not require document level labelling, instead it expects manual labels only for topic nodes. This significantly minimizes the level of supervision needed as only a few topics are observed to be enough for achieving sufficiently high accuracy. The Label Propagation Algorithm is employed on this enriched graph to propagate labels among the nodes. Our approach combines the advantages of Label Propagation (through document-document similarities) and Topic Modelling (for minimal but smart supervision). We demonstrate the effectiveness of our approach on various datasets and compare with state-of-the-art weakly supervised text classification approaches.