Scoring and Classifying Implicit Positive Interpretations: A Challenge of Class Imbalance

Chantal van Son, Roser Morante, Lora Aroyo, Piek Vossen


Abstract
This paper reports on a reimplementation of a system on detecting implicit positive meaning from negated statements. In the original regression experiment, different positive interpretations per negation are scored according to their likelihood. We convert the scores to classes and report our results on both the regression and classification tasks. We show that a baseline taking the mean score or most frequent class is hard to beat because of class imbalance in the dataset. Our error analysis indicates that an approach that takes the information structure into account (i.e. which information is new or contrastive) may be promising, which requires looking beyond the syntactic and semantic characteristics of negated statements.
Anthology ID:
C18-1191
Volume:
Proceedings of the 27th International Conference on Computational Linguistics
Month:
August
Year:
2018
Address:
Santa Fe, New Mexico, USA
Venue:
COLING
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
2253–2264
Language:
URL:
https://aclanthology.org/C18-1191
DOI:
Bibkey:
Cite (ACL):
Chantal van Son, Roser Morante, Lora Aroyo, and Piek Vossen. 2018. Scoring and Classifying Implicit Positive Interpretations: A Challenge of Class Imbalance. In Proceedings of the 27th International Conference on Computational Linguistics, pages 2253–2264, Santa Fe, New Mexico, USA. Association for Computational Linguistics.
Cite (Informal):
Scoring and Classifying Implicit Positive Interpretations: A Challenge of Class Imbalance (van Son et al., COLING 2018)
Copy Citation:
PDF:
https://aclanthology.org/C18-1191.pdf
Code
 cltl/positive-interpretations