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        <title>Discovering Potential Terminological Relationships from Twitter’s Timed Content</title>
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        <namePart type="given">Mohammad</namePart>
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        <name type="personal">
            <namePart type="given">Michael</namePart>
            <namePart type="family">Zock</namePart>
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        <name type="personal">
            <namePart type="given">Stefan</namePart>
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    <abstract>This paper presents a method to discover possible terminological relationships from tweets. We match the histories of terms (frequency patterns). Similar history indicates a possible relationship between terms. For example, if two terms (t1, t2) appeared frequently in Twitter at particular days, and there is a ‘similarity’ in the frequencies over a period of time, then t1 and t2 can be related. Maintaining standard terminological repository with updated relationships can be difficult; especially in a dynamic domain such as social media where thousands of new terms (neology) are coined every day. So we propose to construct a raw repository of lexical units with unconfirmed relationships. We have experimented our method on time-sensitive Arabic terms used by the online Arabic community of Twitter. We draw relationships between these terms by matching their similar frequency patterns (timelines). We use dynamic time warping as a similarity measure. For evaluation, we have selected 630 possible terms (we call them preterms) and we matched the similarity of these terms over a period of 30 days. Around 270 correct relationships were discovered with a precision of 0.61. These relationships were extracted without considering the textual context of the term.</abstract>
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%0 Conference Proceedings
%T Discovering Potential Terminological Relationships from Twitter’s Timed Content
%A Daoud, Mohammad
%A Daoud, Daoud
%Y Zock, Michael
%Y Lenci, Alessandro
%Y Evert, Stefan
%S Proceedings of the 5th Workshop on Cognitive Aspects of the Lexicon (CogALex - V)
%D 2016
%8 December
%I The COLING 2016 Organizing Committee
%C Osaka, Japan
%F daoud-daoud-2016-discovering
%X This paper presents a method to discover possible terminological relationships from tweets. We match the histories of terms (frequency patterns). Similar history indicates a possible relationship between terms. For example, if two terms (t1, t2) appeared frequently in Twitter at particular days, and there is a ‘similarity’ in the frequencies over a period of time, then t1 and t2 can be related. Maintaining standard terminological repository with updated relationships can be difficult; especially in a dynamic domain such as social media where thousands of new terms (neology) are coined every day. So we propose to construct a raw repository of lexical units with unconfirmed relationships. We have experimented our method on time-sensitive Arabic terms used by the online Arabic community of Twitter. We draw relationships between these terms by matching their similar frequency patterns (timelines). We use dynamic time warping as a similarity measure. For evaluation, we have selected 630 possible terms (we call them preterms) and we matched the similarity of these terms over a period of 30 days. Around 270 correct relationships were discovered with a precision of 0.61. These relationships were extracted without considering the textual context of the term.
%U https://aclanthology.org/W16-5319
%P 134-144

Markdown (Informal)

[Discovering Potential Terminological Relationships from Twitter’s Timed Content](https://aclanthology.org/W16-5319) (Daoud & Daoud, CogALex 2016)

• Discovering Potential Terminological Relationships from Twitter’s Timed Content (Daoud & Daoud, CogALex 2016)

ACL

• Mohammad Daoud and Daoud Daoud. 2016. Discovering Potential Terminological Relationships from Twitter’s Timed Content. In Proceedings of the 5th Workshop on Cognitive Aspects of the Lexicon (CogALex - V), pages 134–144, Osaka, Japan. The COLING 2016 Organizing Committee.