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    <titleInfo>
        <title>Utilizing Temporal Information for Taxonomy Construction</title>
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        <namePart type="given">Luu</namePart>
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        <namePart type="given">Cheung</namePart>
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    <abstract>Taxonomies play an important role in many applications by organizing domain knowledge into a hierarchy of ‘is-a’ relations between terms. Previous work on automatic construction of taxonomies from text documents either ignored temporal information or used fixed time periods to discretize the time series of documents. In this paper, we propose a time-aware method to automatically construct and effectively maintain a taxonomy from a given series of documents preclustered for a domain of interest. The method extracts temporal information from the documents and uses a timestamp contribution function to score the temporal relevance of the evidence from source texts when identifying the taxonomic relations for constructing the taxonomy. Experimental results show that our proposed method outperforms the state-of-the-art methods by increasing F-measure up to 7%–20%. Furthermore, the proposed method can incrementally update the taxonomy by adding fresh relations from new data and removing outdated relations using an information decay function. It thus avoids rebuilding the whole taxonomy from scratch for every update and keeps the taxonomy effectively up-to-date in order to track the latest information trends in the rapidly evolving domain.</abstract>
    <identifier type="citekey">tuan-etal-2016-utilizing</identifier>
    <identifier type="doi">10.1162/tacl_a_00117</identifier>
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        <url>https://aclanthology.org/Q16-1039</url>
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        <date>2016</date>
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%0 Journal Article
%T Utilizing Temporal Information for Taxonomy Construction
%A Tuan, Luu Anh
%A Hui, Siu Cheung
%A Ng, See Kiong
%J Transactions of the Association for Computational Linguistics
%D 2016
%V 4
%I MIT Press
%C Cambridge, MA
%F tuan-etal-2016-utilizing
%X Taxonomies play an important role in many applications by organizing domain knowledge into a hierarchy of ‘is-a’ relations between terms. Previous work on automatic construction of taxonomies from text documents either ignored temporal information or used fixed time periods to discretize the time series of documents. In this paper, we propose a time-aware method to automatically construct and effectively maintain a taxonomy from a given series of documents preclustered for a domain of interest. The method extracts temporal information from the documents and uses a timestamp contribution function to score the temporal relevance of the evidence from source texts when identifying the taxonomic relations for constructing the taxonomy. Experimental results show that our proposed method outperforms the state-of-the-art methods by increasing F-measure up to 7%–20%. Furthermore, the proposed method can incrementally update the taxonomy by adding fresh relations from new data and removing outdated relations using an information decay function. It thus avoids rebuilding the whole taxonomy from scratch for every update and keeps the taxonomy effectively up-to-date in order to track the latest information trends in the rapidly evolving domain.
%R 10.1162/tacl_a_00117
%U https://aclanthology.org/Q16-1039
%U https://doi.org/10.1162/tacl_a_00117
%P 551-564

Markdown (Informal)

[Utilizing Temporal Information for Taxonomy Construction](https://aclanthology.org/Q16-1039) (Tuan et al., TACL 2016)

• Utilizing Temporal Information for Taxonomy Construction (Tuan et al., TACL 2016)

ACL

• Luu Anh Tuan, Siu Cheung Hui, and See Kiong Ng. 2016. Utilizing Temporal Information for Taxonomy Construction. Transactions of the Association for Computational Linguistics, 4:551–564.