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        <title>Self-supervised Topic Taxonomy Discovery in the Box Embedding Space</title>
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    <name type="personal">
        <namePart type="given">Yuyin</namePart>
        <namePart type="family">Lu</namePart>
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    <name type="personal">
        <namePart type="given">Hegang</namePart>
        <namePart type="family">Chen</namePart>
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    <name type="personal">
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    <name type="personal">
        <namePart type="given">Yanghui</namePart>
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    <abstract>Topic taxonomy discovery aims at uncovering topics of different abstraction levels and constructing hierarchical relations between them. Unfortunately, most prior work can hardly model semantic scopes of words and topics by holding the Euclidean embedding space assumption. What‘s worse, they infer asymmetric hierarchical relations by symmetric distances between topic embeddings. As a result, existing methods suffer from problems of low-quality topics at high abstraction levels and inaccurate hierarchical relations. To alleviate these problems, this paper develops a Box embedding-based Topic Model (BoxTM) that maps words and topics into the box embedding space, where the asymmetric metric is defined to properly infer hierarchical relations among topics. Additionally, our BoxTM explicitly infers upper-level topics based on correlation between specific topics through recursive clustering on topic boxes. Finally, extensive experiments validate high-quality of the topic taxonomy learned by BoxTM.</abstract>
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    <identifier type="doi">10.1162/tacl_a_00712</identifier>
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        <date>2024</date>
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            <start>1401</start>
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%0 Journal Article
%T Self-supervised Topic Taxonomy Discovery in the Box Embedding Space
%A Lu, Yuyin
%A Chen, Hegang
%A Mao, Pengbo
%A Rao, Yanghui
%A Xie, Haoran
%A Wang, Fu Lee
%A Li, Qing
%J Transactions of the Association for Computational Linguistics
%D 2024
%V 12
%I MIT Press
%C Cambridge, MA
%F lu-etal-2024-self
%X Topic taxonomy discovery aims at uncovering topics of different abstraction levels and constructing hierarchical relations between them. Unfortunately, most prior work can hardly model semantic scopes of words and topics by holding the Euclidean embedding space assumption. What‘s worse, they infer asymmetric hierarchical relations by symmetric distances between topic embeddings. As a result, existing methods suffer from problems of low-quality topics at high abstraction levels and inaccurate hierarchical relations. To alleviate these problems, this paper develops a Box embedding-based Topic Model (BoxTM) that maps words and topics into the box embedding space, where the asymmetric metric is defined to properly infer hierarchical relations among topics. Additionally, our BoxTM explicitly infers upper-level topics based on correlation between specific topics through recursive clustering on topic boxes. Finally, extensive experiments validate high-quality of the topic taxonomy learned by BoxTM.
%R 10.1162/tacl_a_00712
%U https://aclanthology.org/2024.tacl-1.77/
%U https://doi.org/10.1162/tacl_a_00712
%P 1401-1416

Markdown (Informal)

[Self-supervised Topic Taxonomy Discovery in the Box Embedding Space](https://aclanthology.org/2024.tacl-1.77/) (Lu et al., TACL 2024)

• Self-supervised Topic Taxonomy Discovery in the Box Embedding Space (Lu et al., TACL 2024)

ACL

• Yuyin Lu, Hegang Chen, Pengbo Mao, Yanghui Rao, Haoran Xie, Fu Lee Wang, and Qing Li. 2024. Self-supervised Topic Taxonomy Discovery in the Box Embedding Space. Transactions of the Association for Computational Linguistics, 12:1401–1416.