Homonym Detection For Humor Recognition In Short Text

Sven van den Beukel, Lora Aroyo


Abstract
In this paper, automatic homophone- and homograph detection are suggested as new useful features for humor recognition systems. The system combines style-features from previous studies on humor recognition in short text with ambiguity-based features. The performance of two potentially useful homograph detection methods is evaluated using crowdsourced annotations as ground truth. Adding homophones and homographs as features to the classifier results in a small but significant improvement over the style-features alone. For the task of humor recognition, recall appears to be a more important quality measure than precision. Although the system was designed for humor recognition in oneliners, it also performs well at the classification of longer humorous texts.
Anthology ID:
W18-6242
Volume:
Proceedings of the 9th Workshop on Computational Approaches to Subjectivity, Sentiment and Social Media Analysis
Month:
October
Year:
2018
Address:
Brussels, Belgium
Venue:
WASSA
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
286–291
Language:
URL:
https://aclanthology.org/W18-6242
DOI:
10.18653/v1/W18-6242
Bibkey:
Cite (ACL):
Sven van den Beukel and Lora Aroyo. 2018. Homonym Detection For Humor Recognition In Short Text. In Proceedings of the 9th Workshop on Computational Approaches to Subjectivity, Sentiment and Social Media Analysis, pages 286–291, Brussels, Belgium. Association for Computational Linguistics.
Cite (Informal):
Homonym Detection For Humor Recognition In Short Text (van den Beukel & Aroyo, WASSA 2018)
Copy Citation:
PDF:
https://aclanthology.org/W18-6242.pdf