This study investigates the performance of SigLIP, a state-of-the-art Vision-Language Model (VLM), in predicting labels for images depicting 1,278 concepts. Our analysis across 300 images per concept shows that the model frequently predicts the exact user-tagged labels, but similarly, it often predicts labels that are semantically related to the exact labels in various ways: synonyms, hypernyms, co-hyponyms, and associated words, particularly for abstract concepts. We then zoom into the diversity of the user tags of images and word associations for abstract versus concrete concepts. Surprisingly, not only abstract but also concrete concepts exhibit significant variability, thus challenging the traditional view that representations of concrete concepts are less diverse.

Concrete words refer to concepts that are strongly experienced through human senses (banana, chair, salt, etc.), whereas abstract concepts are less perceptually salient (idea, glory, justice, etc.). A clear definition of abstractness is crucial for the understanding of human cognitive processes and for the development of natural language applications such as figurative language detection. In this study, we investigate selectional preferences as a criterion to distinguish between concrete and abstract concepts and words: we hypothesise that abstract and concrete verbs and nouns differ regarding the semantic classes of their arguments. Our study uses a collection of 5,438 nouns and 1,275 verbs to exploit selectional preferences as a salient characteristic in classifying English abstract vs. concrete words, and in predicting their concreteness scores. We achieve an f1-score of 0.84 for nouns and 0.71 for verbs in classification, and Spearman’s ρ correlation of 0.86 for nouns and 0.59 for verbs.

In this paper, we propose a novel framework for sarcasm generation; the system takes a literal negative opinion as input and translates it into a sarcastic version. Our framework does not require any paired data for training. Sarcasm emanates from context-incongruity which becomes apparent as the sentence unfolds. Our framework introduces incongruity into the literal input version through modules that: (a) filter factual content from the input opinion, (b) retrieve incongruous phrases related to the filtered facts and (c) synthesize sarcastic text from the incongruous filtered and incongruous phrases. The framework employs reinforced neural sequence to sequence learning and information retrieval and is trained only using unlabeled non-sarcastic and sarcastic opinions. Since no labeled dataset exists for such a task, for evaluation, we manually prepare a benchmark dataset containing literal opinions and their sarcastic paraphrases. Qualitative and quantitative performance analyses on the data reveal our system’s superiority over baselines built using known unsupervised statistical and neural machine translation and style transfer techniques.