# Learning Concept Abstractness Using Weak Supervision

arXiv: https://arxiv.org/abs/1809.01285 · HTML: https://mlsanity.com/paper/1809.01285/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 5 Sep 2018.

## Built on by (most cited first, 6 of 6)

- [Is BERT Blind? Exploring the Effect of Vision-and-Language Pretraining on Visual Language Understanding](https://mlsanity.com/paper/2303.12513/) (2023)
- [Mitigating Open-Vocabulary Caption Hallucinations](https://mlsanity.com/paper/2312.03631/) (2023)
- [Combining Pre-trained Word Embeddings and Linguistic Features for Sequential Metaphor Identification](https://mlsanity.com/paper/2104.03285/) (2021)
- [Automatic generation of a large dictionary with concreteness/abstractness ratings based on a small human dictionary](https://mlsanity.com/paper/2206.06200/) (2022)
- [ICC: Quantifying Image Caption Concreteness for Multimodal Dataset Curation](https://mlsanity.com/paper/2403.01306/) (2024)
- [Using Letter Positional Probabilities to Assess Word Complexity](https://mlsanity.com/paper/2404.07768/) (2024)

## Abstract (arXiv)

We introduce a weakly supervised approach for inferring the property of abstractness of words and expressions in the complete absence of labeled data. Exploiting only minimal linguistic clues and the contextual usage of a concept as manifested in textual data, we train sufficiently powerful classifiers, obtaining high correlation with human labels. The results imply the applicability of this approach to additional properties of concepts, additional languages, and resource-scarce scenarios.