Discovering Potential Terminological Relationships from Twitter’s Timed Content

Mohammad Daoud, Daoud Daoud


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.
Anthology ID:
W16-5319
Volume:
Proceedings of the 5th Workshop on Cognitive Aspects of the Lexicon (CogALex - V)
Month:
December
Year:
2016
Address:
Osaka, Japan
Editors:
Michael Zock, Alessandro Lenci, Stefan Evert
Venue:
CogALex
SIG:
SIGLEX
Publisher:
The COLING 2016 Organizing Committee
Note:
Pages:
134–144
Language:
URL:
https://aclanthology.org/W16-5319
DOI:
Bibkey:
Cite (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.
Cite (Informal):
Discovering Potential Terminological Relationships from Twitter’s Timed Content (Daoud & Daoud, CogALex 2016)
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PDF:
https://aclanthology.org/W16-5319.pdf