LDR at SemEval-2018 Task 3: A Low Dimensional Text Representation for Irony Detection

Bilal Ghanem, Francisco Rangel, Paolo Rosso


Abstract
In this paper we describe our participation in the SemEval-2018 task 3 Shared Task on Irony Detection. We have approached the task with our low dimensionality representation method (LDR), which exploits low dimensional features extracted from text on the basis of the occurrence probability of the words depending on each class. Our intuition is that words in ironic texts have different probability of occurrence than in non-ironic ones. Our approach obtained acceptable results in both subtasks A and B. We have performed an error analysis that shows the difference on correct and incorrect classified tweets.
Anthology ID:
S18-1086
Volume:
Proceedings of The 12th International Workshop on Semantic Evaluation
Month:
June
Year:
2018
Address:
New Orleans, Louisiana
Venue:
SemEval
SIGs:
SIGSEM | SIGLEX
Publisher:
Association for Computational Linguistics
Note:
Pages:
531–536
Language:
URL:
https://aclanthology.org/S18-1086
DOI:
10.18653/v1/S18-1086
Bibkey:
Cite (ACL):
Bilal Ghanem, Francisco Rangel, and Paolo Rosso. 2018. LDR at SemEval-2018 Task 3: A Low Dimensional Text Representation for Irony Detection. In Proceedings of The 12th International Workshop on Semantic Evaluation, pages 531–536, New Orleans, Louisiana. Association for Computational Linguistics.
Cite (Informal):
LDR at SemEval-2018 Task 3: A Low Dimensional Text Representation for Irony Detection (Ghanem et al., SemEval 2018)
Copy Citation:
PDF:
https://aclanthology.org/S18-1086.pdf