@inproceedings{zhou-etal-2020-hierarchy,
title = "Hierarchy-Aware Global Model for Hierarchical Text Classification",
author = "Zhou, Jie and
Ma, Chunping and
Long, Dingkun and
Xu, Guangwei and
Ding, Ning and
Zhang, Haoyu and
Xie, Pengjun and
Liu, Gongshen",
booktitle = "Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics",
month = jul,
year = "2020",
address = "Online",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2020.acl-main.104",
doi = "10.18653/v1/2020.acl-main.104",
pages = "1106--1117",
abstract = "Hierarchical text classification is an essential yet challenging subtask of multi-label text classification with a taxonomic hierarchy. Existing methods have difficulties in modeling the hierarchical label structure in a global view. Furthermore, they cannot make full use of the mutual interactions between the text feature space and the label space. In this paper, we formulate the hierarchy as a directed graph and introduce hierarchy-aware structure encoders for modeling label dependencies. Based on the hierarchy encoder, we propose a novel end-to-end hierarchy-aware global model (HiAGM) with two variants. A multi-label attention variant (HiAGM-LA) learns hierarchy-aware label embeddings through the hierarchy encoder and conducts inductive fusion of label-aware text features. A text feature propagation model (HiAGM-TP) is proposed as the deductive variant that directly feeds text features into hierarchy encoders. Compared with previous works, both HiAGM-LA and HiAGM-TP achieve significant and consistent improvements on three benchmark datasets.",
}
<?xml version="1.0" encoding="UTF-8"?>
<modsCollection xmlns="http://www.loc.gov/mods/v3">
<mods ID="zhou-etal-2020-hierarchy">
<titleInfo>
<title>Hierarchy-Aware Global Model for Hierarchical Text Classification</title>
</titleInfo>
<name type="personal">
<namePart type="given">Jie</namePart>
<namePart type="family">Zhou</namePart>
<role>
<roleTerm authority="marcrelator" type="text">author</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Chunping</namePart>
<namePart type="family">Ma</namePart>
<role>
<roleTerm authority="marcrelator" type="text">author</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Dingkun</namePart>
<namePart type="family">Long</namePart>
<role>
<roleTerm authority="marcrelator" type="text">author</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Guangwei</namePart>
<namePart type="family">Xu</namePart>
<role>
<roleTerm authority="marcrelator" type="text">author</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Ning</namePart>
<namePart type="family">Ding</namePart>
<role>
<roleTerm authority="marcrelator" type="text">author</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Haoyu</namePart>
<namePart type="family">Zhang</namePart>
<role>
<roleTerm authority="marcrelator" type="text">author</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Pengjun</namePart>
<namePart type="family">Xie</namePart>
<role>
<roleTerm authority="marcrelator" type="text">author</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Gongshen</namePart>
<namePart type="family">Liu</namePart>
<role>
<roleTerm authority="marcrelator" type="text">author</roleTerm>
</role>
</name>
<originInfo>
<dateIssued>2020-jul</dateIssued>
</originInfo>
<typeOfResource>text</typeOfResource>
<relatedItem type="host">
<titleInfo>
<title>Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics</title>
</titleInfo>
<originInfo>
<publisher>Association for Computational Linguistics</publisher>
<place>
<placeTerm type="text">Online</placeTerm>
</place>
</originInfo>
<genre authority="marcgt">conference publication</genre>
</relatedItem>
<abstract>Hierarchical text classification is an essential yet challenging subtask of multi-label text classification with a taxonomic hierarchy. Existing methods have difficulties in modeling the hierarchical label structure in a global view. Furthermore, they cannot make full use of the mutual interactions between the text feature space and the label space. In this paper, we formulate the hierarchy as a directed graph and introduce hierarchy-aware structure encoders for modeling label dependencies. Based on the hierarchy encoder, we propose a novel end-to-end hierarchy-aware global model (HiAGM) with two variants. A multi-label attention variant (HiAGM-LA) learns hierarchy-aware label embeddings through the hierarchy encoder and conducts inductive fusion of label-aware text features. A text feature propagation model (HiAGM-TP) is proposed as the deductive variant that directly feeds text features into hierarchy encoders. Compared with previous works, both HiAGM-LA and HiAGM-TP achieve significant and consistent improvements on three benchmark datasets.</abstract>
<identifier type="citekey">zhou-etal-2020-hierarchy</identifier>
<identifier type="doi">10.18653/v1/2020.acl-main.104</identifier>
<location>
<url>https://aclanthology.org/2020.acl-main.104</url>
</location>
<part>
<date>2020-jul</date>
<extent unit="page">
<start>1106</start>
<end>1117</end>
</extent>
</part>
</mods>
</modsCollection>
%0 Conference Proceedings
%T Hierarchy-Aware Global Model for Hierarchical Text Classification
%A Zhou, Jie
%A Ma, Chunping
%A Long, Dingkun
%A Xu, Guangwei
%A Ding, Ning
%A Zhang, Haoyu
%A Xie, Pengjun
%A Liu, Gongshen
%S Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics
%D 2020
%8 jul
%I Association for Computational Linguistics
%C Online
%F zhou-etal-2020-hierarchy
%X Hierarchical text classification is an essential yet challenging subtask of multi-label text classification with a taxonomic hierarchy. Existing methods have difficulties in modeling the hierarchical label structure in a global view. Furthermore, they cannot make full use of the mutual interactions between the text feature space and the label space. In this paper, we formulate the hierarchy as a directed graph and introduce hierarchy-aware structure encoders for modeling label dependencies. Based on the hierarchy encoder, we propose a novel end-to-end hierarchy-aware global model (HiAGM) with two variants. A multi-label attention variant (HiAGM-LA) learns hierarchy-aware label embeddings through the hierarchy encoder and conducts inductive fusion of label-aware text features. A text feature propagation model (HiAGM-TP) is proposed as the deductive variant that directly feeds text features into hierarchy encoders. Compared with previous works, both HiAGM-LA and HiAGM-TP achieve significant and consistent improvements on three benchmark datasets.
%R 10.18653/v1/2020.acl-main.104
%U https://aclanthology.org/2020.acl-main.104
%U https://doi.org/10.18653/v1/2020.acl-main.104
%P 1106-1117
Markdown (Informal)
[Hierarchy-Aware Global Model for Hierarchical Text Classification](https://aclanthology.org/2020.acl-main.104) (Zhou et al., ACL 2020)
ACL
- Jie Zhou, Chunping Ma, Dingkun Long, Guangwei Xu, Ning Ding, Haoyu Zhang, Pengjun Xie, and Gongshen Liu. 2020. Hierarchy-Aware Global Model for Hierarchical Text Classification. In Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pages 1106–1117, Online. Association for Computational Linguistics.