Embedding Lexical Features via Tensor Decomposition for Small Sample Humor Recognition

Zhenjie Zhao, Andrew Cattle, Evangelos Papalexakis, Xiaojuan Ma


Abstract
We propose a novel tensor embedding method that can effectively extract lexical features for humor recognition. Specifically, we use word-word co-occurrence to encode the contextual content of documents, and then decompose the tensor to get corresponding vector representations. We show that this simple method can capture features of lexical humor effectively for continuous humor recognition. In particular, we achieve a distance of 0.887 on a global humor ranking task, comparable to the top performing systems from SemEval 2017 Task 6B (Potash et al., 2017) but without the need for any external training corpus. In addition, we further show that this approach is also beneficial for small sample humor recognition tasks through a semi-supervised label propagation procedure, which achieves about 0.7 accuracy on the 16000 One-Liners (Mihalcea and Strapparava, 2005) and Pun of the Day (Yang et al., 2015) humour classification datasets using only 10% of known labels.
Anthology ID:
D19-1669
Volume:
Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP)
Month:
November
Year:
2019
Address:
Hong Kong, China
Venues:
EMNLP | IJCNLP
SIG:
SIGDAT
Publisher:
Association for Computational Linguistics
Note:
Pages:
6376–6381
Language:
URL:
https://aclanthology.org/D19-1669
DOI:
10.18653/v1/D19-1669
Bibkey:
Cite (ACL):
Zhenjie Zhao, Andrew Cattle, Evangelos Papalexakis, and Xiaojuan Ma. 2019. Embedding Lexical Features via Tensor Decomposition for Small Sample Humor Recognition. In Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP), pages 6376–6381, Hong Kong, China. Association for Computational Linguistics.
Cite (Informal):
Embedding Lexical Features via Tensor Decomposition for Small Sample Humor Recognition (Zhao et al., EMNLP 2019)
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PDF:
https://aclanthology.org/D19-1669.pdf