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