---
title: "Breaking Bad — paper by Mohammad (2025)"
description: "This paper fills a gap in affect resources. 48 references, cited by 3 papers here. Among them: 1."
authors: ["Saif M. Mohammad"]
year: 2025
venue: "Proceedings of the 14th International Joint Conference on Natural Language Processing and the 4th Conference of the Asia-Pacific Chapter of the Association for Computational Linguistics"
source: "https://aclanthology.org/2025.ijcnlp-long.107/"
doi: "10.18653/v1/2025.ijcnlp-long.107"
licence: "CC BY 4.0 (ACL Anthology)"
licence_url: "https://creativecommons.org/licenses/by/4.0/"
summary_licence: "Elamur Cosmic License"
canonical: "https://research.elamur.ai/free/en/papers/mohammad-2025-breaking-bad-norms-valence-arousal/"
---
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# Breaking Bad: Norms for Valence, Arousal, and Dominance for over 10k English Multiword Expressions

**Authors:** [Saif M. Mohammad](https://md.research.elamur.ai/free/en/authors/saif-mohammad/) · **Year:** 2025 · **Venue:** [Proceedings of the 14th International Joint Conference on Natural Language Processing and the 4th Conference of the Asia-Pacific Chapter of the Association for Computational Linguistics](https://md.research.elamur.ai/free/en/venues/ijcnlp/) ([IJCNLP 2025](https://md.research.elamur.ai/free/en/venues/ijcnlp/2025/)) · **Publisher:** [The Asian Federation of Natural Language Processing and The Association for Computational Linguistics](https://md.research.elamur.ai/free/en/publishers/the-asian-federation-of-natural-language-processing-and-the-association-for/) · **Licence:** [CC BY 4.0 (ACL Anthology)](https://creativecommons.org/licenses/by/4.0/)

**Links:** [Publisher's page](https://aclanthology.org/2025.ijcnlp-long.107/) · [PDF](https://aclanthology.org/2025.ijcnlp-long.107.pdf) · [DOI 10.18653/v1/2025.ijcnlp-long.107](https://doi.org/10.18653/v1/2025.ijcnlp-long.107) · [OpenAlex](https://openalex.org/W7138376537)

**Citations:** 1 citations (OpenAlex, 2026-10-14) · [3](https://md.research.elamur.ai/free/en/papers/mohammad-2025-breaking-bad-norms-valence-arousal/cited-by/) in our library, of the 149,439 papers whose bibliographies we read

## Summary
This paper fills a gap in affect resources. Lexicons rating words for valence, arousal and dominance (VAD) already exist, but no large human-rated VAD resource covered multiword expressions (MWEs), whose meaning often cannot be built from their parts. The author took about 10.5k frequent English MWEs from the Muraki et al. (2023a) list and roughly 25k additional unigrams. The unigrams include MWE constituents and words that became common after 2018, such as quarantine. Crowd workers rated these terms on a seven-point scale for each dimension. The pipeline used pilot-tested questionnaires, hidden check questions, removal of outlier annotators, and averaging into scores from −1 to 1. The resulting ratings are very consistent, with split-half correlations of 0.96–0.99. Merged with the 2018 lexicon, they form the NRC VAD Lexicon v2. The paper uses the new resource to study how strongly different MWE types (idioms, noun compounds, verb particle constructions) carry emotion, and how far an MWE's VAD can be predicted from its words. The lexicon is free for research, with machine translations into more than 100 languages.

## Key points
- NRC VAD Lexicon v2 has 55,133 terms in total: 44,928 words and 10,205 MWEs. The new MWE-VAD portion adds 25,089 words and 10,073 MWEs to v1, which had 19,839 words and 132 MWEs.
- Split-half reliability is r = 0.99 for valence, 0.98 for arousal and 0.96 for dominance.
- The source for the new unigrams was Brysbaert et al.'s (2019) Prevalence dataset.
  - A word was kept only if at least 70% of respondents knew it.
  - Common person and city names were removed, as were words already in v1.
- Annotation design:
  - Ratings ran from −3 to +3 on Amazon Mechanical Turk.
  - Instructions asked raters to judge the predominant sense of a term and what most English speakers would agree with.
  - Gold questions gave immediate pop-up feedback, and work could be rejected if the error rate exceeded about 20%.
- The v2 terms have since fed other resources, including the NRC WorryWords Lexicon and the NRC Words of Warmth (WoW) Lexicon.

## Interesting ideas
- Rating MWEs alongside their constituent words lets you measure directly how far a phrase's affect departs from its parts, which identifies non-compositional expressions empirically.
- Asking raters what most speakers would agree with, rather than what they personally feel, steers annotations toward shared norms and reduces individual noise.
- Split-half reliability checks whether aggregated labels are stable: split the annotators randomly into two halves and correlate the scores each half produces.
- Seeding the task with hidden items whose answers are already known, then dropping workers who miss too many, filters unreliable contributors without manual review.
- The paper warns against judging the affect of a single sentence from word-level scores and recommends comparative, aggregate use instead.

## For Mova

How these ideas could serve Mova, our small open language model — its files, stages and tests:

- **I1** (world): Store these MWEs as single knowledge entries with a compositionality flag, so the world builder does not assemble the meaning of an idiom like "breaking bad" from its parts.
- **I2** (learn): Phrase LLM teacher prompts for values and connotations as consensus questions, so silver labels reflect community judgement rather than a single idiosyncratic reading.
- **I3** (learn): Split the witness parsers or teacher samples into two halves and correlate their outputs, giving a cheap stability score before trusting a silver dataset.
- **I4** (learn): Mix known-answer probes into every LLM teacher batch as negative controls, and automatically demote tiers or runs that fail them.
- **I5** (frames): Treat lexicon VAD only as a weak prior in irony or implicature detection, and let the frame-level situation override it when the two clash.

*Our summary of the paper, in our own words — not its text. The work belongs to its authors; read it at the links above.*

## Institutions

- [National Research Council Canada](https://md.research.elamur.ai/free/en/government/national-research-council-canada/) — Government body, Ottawa, Canada · 59 works · 21,677 citations

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🧭 Topics (2): [English](https://md.research.elamur.ai/free/en/topics/english/) · 2,263 works · [🧷 Idioms & multiword expressions](https://md.research.elamur.ai/free/en/topics/multiword-expressions/) · 217 works

## Cite

Cite as: Saif M. Mohammad. 2025. Breaking Bad: Norms for Valence, Arousal, and Dominance for over 10k English Multiword Expressions. Proceedings of the 14th International Joint Conference on Natural Language Processing and the 4th Conference of the Asia-Pacific Chapter of the Association for Computational Linguistics.

The paper itself, not this page. BibTeX from the library's record of the work.

```bibtex
@inproceedings{mohammad2025breaking,
  title = {{Breaking Bad: Norms for Valence, Arousal, and Dominance for over 10k English Multiword Expressions}},
  author = {Saif M. Mohammad},
  booktitle = {Proceedings of the 14th International Joint Conference on Natural Language Processing and the 4th Conference of the Asia-Pacific Chapter of the Association for Computational Linguistics},
  year = {2025},
  doi = {10.18653/v1/2025.ijcnlp-long.107},
  url = {https://doi.org/10.18653/v1/2025.ijcnlp-long.107}
}
```

- [← previous Bootstrapping AI: Interdisciplinary Approaches to Assessing OCR Quality in English-Language Historical Documents](https://md.research.elamur.ai/free/en/papers/backer-2025-bootstrapping-ai-interdisciplinary-approaches-assessing/)
- [next → CAFE: Retrieval Head-based Coarse-to-Fine Information Seeking to Enhance Multi-Document QA Capability](https://md.research.elamur.ai/free/en/papers/peng-2025-cafe-retrieval-head-based-coarse/)
- [↑ back to papers of 2025](https://md.research.elamur.ai/free/en/papers/2025/page/11/)

## References

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*Updated 1 November 2026*