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:
- 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)
- PDF:
- https://preview.aclanthology.org/emnlp-22-attachments/W16-5319.pdf