@inproceedings{daoud-daoud-2016-discovering,
    title = "Discovering Potential Terminological Relationships from {T}witter{'}s Timed Content",
    author = "Daoud, Mohammad  and
      Daoud, Daoud",
    editor = "Zock, Michael  and
      Lenci, Alessandro  and
      Evert, Stefan",
    booktitle = "Proceedings of the 5th Workshop on Cognitive Aspects of the Lexicon ({C}og{AL}ex - V)",
    month = dec,
    year = "2016",
    address = "Osaka, Japan",
    publisher = "The COLING 2016 Organizing Committee",
    url = "https://preview.aclanthology.org/ingest-emnlp/W16-5319/",
    pages = "134--144",
    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."
}