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    <titleInfo>
        <title>Hashtag Sense Clustering Based on Temporal Similarity</title>
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    <name type="personal">
        <namePart type="given">Giovanni</namePart>
        <namePart type="family">Stilo</namePart>
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    <abstract>Hashtags are creative labels used in micro-blogs to characterize the topic of a message/discussion. Regardless of the use for which they were originally intended, hashtags cannot be used as a means to cluster messages with similar content. First, because hashtags are created in a spontaneous and highly dynamic way by users in multiple languages, the same topic can be associated with different hashtags, and conversely, the same hashtag may refer to different topics in different time periods. Second, contrary to common words, hashtag disambiguation is complicated by the fact that no sense catalogs (e.g., Wikipedia or WordNet) are available; and, furthermore, hashtag labels are difficult to analyze, as they often consist of acronyms, concatenated words, and so forth. A common way to determine the meaning of hashtags has been to analyze their context, but, as we have just pointed out, hashtags can have multiple and variable meanings. In this article, we propose a temporal sense clustering algorithm based on the idea that semantically related hashtags have similar and synchronous usage patterns.</abstract>
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    <identifier type="doi">10.1162/COLI_a_00277</identifier>
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        <date>2017-04</date>
        <detail type="volume"><number>43</number></detail>
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%0 Journal Article
%T Hashtag Sense Clustering Based on Temporal Similarity
%A Stilo, Giovanni
%A Velardi, Paola
%J Computational Linguistics
%D 2017
%8 April
%V 43
%N 1
%I MIT Press
%C Cambridge, MA
%F stilo-velardi-2017-hashtag
%X Hashtags are creative labels used in micro-blogs to characterize the topic of a message/discussion. Regardless of the use for which they were originally intended, hashtags cannot be used as a means to cluster messages with similar content. First, because hashtags are created in a spontaneous and highly dynamic way by users in multiple languages, the same topic can be associated with different hashtags, and conversely, the same hashtag may refer to different topics in different time periods. Second, contrary to common words, hashtag disambiguation is complicated by the fact that no sense catalogs (e.g., Wikipedia or WordNet) are available; and, furthermore, hashtag labels are difficult to analyze, as they often consist of acronyms, concatenated words, and so forth. A common way to determine the meaning of hashtags has been to analyze their context, but, as we have just pointed out, hashtags can have multiple and variable meanings. In this article, we propose a temporal sense clustering algorithm based on the idea that semantically related hashtags have similar and synchronous usage patterns.
%R 10.1162/COLI_a_00277
%U https://aclanthology.org/J17-1005
%U https://doi.org/10.1162/COLI_a_00277
%P 181-200

Markdown (Informal)

[Hashtag Sense Clustering Based on Temporal Similarity](https://aclanthology.org/J17-1005) (Stilo & Velardi, CL 2017)

• Hashtag Sense Clustering Based on Temporal Similarity (Stilo & Velardi, CL 2017)

ACL

• Giovanni Stilo and Paola Velardi. 2017. Hashtag Sense Clustering Based on Temporal Similarity. Computational Linguistics, 43(1):181–200.