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Title: MATCH: Metadata-Aware Text Classification in A Large Hierarchy
Multi-label text classification refers to the problem of assigning each given document its most relevant labels from a label set. Commonly, the metadata of the given documents and the hierarchy of the labels are available in real-world applications. However, most existing studies focus on only modeling the text information, with a few attempts to utilize either metadata or hierarchy signals, but not both of them. In this paper, we bridge the gap by formalizing the problem of metadata-aware text classification in a large label hierarchy (e.g., with tens of thousands of labels). To address this problem, we present the MATCH solution—an end-to-end framework that leverages both metadata and hierarchy information. To incorporate metadata, we pre-train the embeddings of text and metadata in the same space and also leverage the fully-connected attentions to capture the interrelations between them. To leverage the label hierarchy, we propose different ways to regularize the parameters and output probability of each child label by its parents. Extensive experiments on two massive text datasets with large-scale label hierarchies demonstrate the effectiveness of MATCH over the state-of-the-art deep learning baselines.  more » « less
Award ID(s):
1741317 1956151 1704532
NSF-PAR ID:
10279805
Author(s) / Creator(s):
; ; ; ;
Date Published:
Journal Name:
WWW '21: The Web Conference 2021
Volume:
2021
Issue:
1
Page Range / eLocation ID:
3246 to 3257
Format(s):
Medium: X
Sponsoring Org:
National Science Foundation
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