# Pragmatic factors in image description: the case of negations

arXiv: https://arxiv.org/abs/1606.06164 · HTML: https://mlsanity.com/paper/1606.06164/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 20 Jun 2016.

## Built on by (most cited first, 5 of 5)

- [Pragmatics in the Era of Large Language Models: A Survey on Datasets, Evaluation, Opportunities and Challenges](https://mlsanity.com/paper/2502.12378/) (2025)
- [Underspecification in Scene Description-to-Depiction Tasks](https://mlsanity.com/paper/2210.05815/) (2022)
- [ANNA: Abstractive Text-to-Image Synthesis with Filtered News Captions](https://mlsanity.com/paper/2301.02160/) (2023)
- [A Large-Scale Multilingual Study of Visual Constraints on Linguistic Selection of Descriptions](https://mlsanity.com/paper/2302.04811/) (2023)
- [Exploring Descriptions of Movement Through Geovisual Analytics](https://mlsanity.com/paper/2204.09588/) (2022)

## Abstract (arXiv)

We provide a qualitative analysis of the descriptions containing negations (no, not, n't, nobody, etc) in the Flickr30K corpus, and a categorization of negation uses. Based on this analysis, we provide a set of requirements that an image description system should have in order to generate negation sentences. As a pilot experiment, we used our categorization to manually annotate sentences containing negations in the Flickr30K corpus, with an agreement score of K=0.67. With this paper, we hope to open up a broader discussion of subjective language in image descriptions.