Non-Compositionality in Sentiment: New Data and Analyses
Abstract
When natural language phrases are combined, their meaning is often more than the sum of their parts. In the context of NLP tasks such as sentiment analysis, where the meaning of a phrase is its sentiment, that still applies. Many NLP studies on sentiment analysis, however, focus on the fact that sentiment computations are largely compositional. We, instead, set out to obtain non-compositionality ratings for phrases with respect to their sentiment. Our contributions are as follows: a) a methodology for obtaining those non-compositionality ratings, b) a resource of ratings for 259 phrases -- NonCompSST -- along with an analysis of that resource, and c) an evaluation of computational models for sentiment analysis using this new resource.
Cite
@article{arxiv.2310.20656,
title = {Non-Compositionality in Sentiment: New Data and Analyses},
author = {Verna Dankers and Christopher G. Lucas},
journal= {arXiv preprint arXiv:2310.20656},
year = {2023}
}
Comments
Published in EMNLP Findings 2023; 13 pages total (5 in the main paper, 3 pages with limitations, acknowledgments and references, 5 pages with appendices)