# A Unified Feature Representation for Lexical Connotations

arXiv: https://arxiv.org/abs/2006.00635 · HTML: https://mlsanity.com/paper/2006.00635/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 31 May 2020.

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

- [The Media Bias Taxonomy: A Systematic Literature Review on the Forms and Automated Detection of Media Bias](https://mlsanity.com/paper/2312.16148/) (2023)
- [ENTRUST: Argument Reframing with Language Models and Entailment](https://mlsanity.com/paper/2103.06758/) (2021)
- [From Theories on Styles to their Transfer in Text: Bridging the Gap with a Hierarchical Survey](https://mlsanity.com/paper/2110.15871/) (2021)
- [Adjusting Interpretable Dimensions in Embedding Space with Human Judgments](https://mlsanity.com/paper/2404.02619/) (2024)
- [Connecting the Dots: Evaluating Abstract Reasoning Capabilities of LLMs Using the New York Times Connections Word Game](https://mlsanity.com/paper/2406.11012/) (2024)
- [TIDE: Textual Identity Detection for Evaluating and Augmenting Classification and Language Models](https://mlsanity.com/paper/2309.04027/) (2023)

## Abstract (arXiv)

Ideological attitudes and stance are often expressed through subtle meanings of words and phrases. Understanding these connotations is critical to recognizing the cultural and emotional perspectives of the speaker. In this paper, we use distant labeling to create a new lexical resource representing connotation aspects for nouns and adjectives. Our analysis shows that it aligns well with human judgments. Additionally, we present a method for creating lexical representations that captures connotations within the embedding space and show that using the embeddings provides a statistically significant improvement on the task of stance detection when data is limited.