Learning label-label correlations in Extreme Multi-label Classification via Label Features

arXiv:2405.04545v1 Announce Type: new
Abstract: Extreme Multi-label Text Classification (XMC) involves learning a classifier that can assign an input with a subset of most relevant labels from millions of label choices. Recent works in this domain have increasingly focused on a symmetric problem setting where both input instances and label features are short-text in nature. Short-text XMC with label features has found numerous applications in areas such as query-to-ad-phrase matching in search ads, title-based product recommendation, prediction of related searches. …

abstract arxiv classification classifier correlations cs.lg domain features instances labels text text classification type via