---
title: "Metaphor Identification Using Verb and Noun — Shutova 2010"
description: "The paper addresses automatic detection of metaphorical language in unrestricted text, specifically verbs used figuratively with a subject or direct…"
authors: ["Ekaterina Shutova", "Lin Sun", "Anna Korhonen"]
year: 2010
venue: "Proceedings of the 23rd International Conference on Computational Linguistics (Coling 2010)"
source: "https://aclanthology.org/C10-1113/"
licence: "CC BY-NC-SA 3.0 (ACL Anthology, before 2016)"
licence_url: "https://creativecommons.org/licenses/by-nc-sa/3.0/"
summary_licence: "Elamur Cosmic License"
canonical: "https://research.elamur.ai/free/en/papers/shutova-2010-metaphor-identification-verb-noun-clustering/"
---
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# Metaphor Identification Using Verb and Noun Clustering

**Authors:** [Ekaterina Shutova](https://md.research.elamur.ai/free/en/authors/ekaterina-shutova/), [Lin Sun](https://md.research.elamur.ai/free/en/authors/lin-sun/), [Anna Korhonen](https://md.research.elamur.ai/free/en/authors/anna-korhonen/) · **Year:** 2010 · **Venue:** Proceedings of the 23rd International Conference on Computational Linguistics (Coling 2010) · **Licence:** [CC BY-NC-SA 3.0 (ACL Anthology, before 2016)](https://creativecommons.org/licenses/by-nc-sa/3.0/)

**Links:** [Publisher's page](https://aclanthology.org/C10-1113/) · [PDF](https://aclanthology.org/C10-1113.pdf) · [OpenAlex](https://openalex.org/W1640287562)

**Citations:** 155 citations (openalex-snapshot · 2026-10-14) · [95](#cited-by) in our library, of the 140884 papers whose bibliographies we read

## Summary
The paper addresses automatic detection of metaphorical language in unrestricted text, specifically verbs used figuratively with a subject or direct object. Earlier systems relied on hand-built knowledge such as WordNet, or were limited to one predefined domain. The authors instead start from 62 annotated seed metaphors drawn from the BNC and expand them using unsupervised clustering.

The intuition is that abstract nouns sharing a source domain behave alike in corpora even when their meanings differ. For example, marriage and political regime are both talked about as machines. Noun clustering groups such target concepts, and verb clustering builds a lexicon of source-domain verbs. The system then searches the RASP-parsed BNC for verb–object and verb–subject pairs that combine the two sets.

Human judges rated the output, and the system reached a precision of 0.79. A comparison with a WordNet-based baseline showed that the method finds metaphors well beyond synonyms of the seeds. The authors present it as the first unsupervised approach to metaphor identification, one that needs no hand-crafted resources beyond the seed set.

## Key points
- **Seed set:** 62 single-word verb metaphors from Shutova (2010), covering verb–object pairs (e.g. *cast doubt*, *grasp theory*) and subject–verb pairs (e.g. *tension mounted*).
- **Corpus:** the whole BNC, using the RASP grammatical-relation output produced by Andersen et al. (2008).
- **Verb clustering:**
  - The approach follows Sun and Korhonen (2009), which scored 80.4 F-measure on 204 verbs from 17 Levin classes.
  - Here it is scaled to 1610 VerbNet verbs, each seen more than 150 times in the VALEX source data.
  - Features are subcategorization frames (SCFs) parameterized by selectional preferences (SPs), extracted with the Preiss et al. (2007) system, which recognizes 168 SCF types.
- **Noun clustering:**
  - Covers the 2000 most frequent BNC nouns.
  - Features are grammatical relations (subject, direct object, indirect object), normalized by the sum of feature values.
  - Both verbs and nouns are clustered with spectral clustering (MNCut) over a similarity matrix based on Jensen-Shannon divergence.
- **Motivation and result:** a cited corpus study found metaphors in 241 of 761 BNC sentences, and in 164 of those phrases the metaphor came from the verb. The system's precision was 0.79 by human judgement.

## Interesting ideas
- **Treat distributional "errors" as signal:** Distributional clustering tends to group abstract nouns by shared metaphorical source rather than by meaning. The authors exploit this side effect instead of fighting it, and the same reframing could help with other figurative or associative phenomena.
- **Bootstrap from a few seeds by generalizing both slots:** Clustering both the argument (target nouns) and the predicate (source verbs) turns a handful of examples into many new candidate pairs. This pattern suits low-resource languages where large annotated datasets are unavailable.
- **Restrict search to parsed grammatical relations:** Searching only within verb–subject and verb–object relations, rather than raw word co-occurrence, keeps candidates precise. The authors explicitly criticize bigram-based approaches for losing this information.
- **Use a synonymy baseline to test generalization:** Comparing against a WordNet baseline checks whether the method truly generalizes or merely rediscovers near-synonyms of the seeds. This is a useful sanity check for any seed-expansion method.
- **Watch for class imbalance in reported results:** The authors note that a related work's 95% accuracy barely beats a 92.9% majority-class baseline. Reporting such baselines is a simple safeguard against overstated results in imbalanced classification tasks.

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

Fields: [Natural language processing](https://md.research.elamur.ai/free/en/shelves/nlp/)

## Cited by

Cited by

95 papers of our library cite it (of the 140884 whose bibliographies we read); 6 of them have a page here, 89 not yet.

- [The Pragmatics behind Politics: Modelling Metaphor, Framing and Emotion in Political Discourse](https://md.research.elamur.ai/free/en/papers/cabot-2020-pragmatics-behind-politics-modelling-metaphor/)
- [ID10M-JAM: Stress-Testing Idiom Identification Under Challenging Context](https://md.research.elamur.ai/free/en/papers/hashiloni-2026-id10m-jam-stress-testing-idiom/)
- [Black Holes and White Rabbits: Metaphor Identification with Visual Features](https://md.research.elamur.ai/free/en/papers/shutova-2016-black-holes-white-rabbits-metaphor/)
- [ID10M: Idiom Identification in 10 Languages](https://md.research.elamur.ai/free/en/papers/tedeschi-2022-id10m-idiom-identification-10-languages/)
- [Metaphor Detection with Cross-Lingual Model Transfer](https://md.research.elamur.ai/free/en/papers/tsvetkov-2014-metaphor-detection-cross-lingual-model/)
- [Idiomatic Expression Identification using Semantic Compatibility](https://md.research.elamur.ai/free/en/papers/zeng-2021-idiomatic-expression-identification-semantic-compatibility/)

## Cite

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

```bibtex
@inproceedings{shutova2010metaphor,
  title = {{Metaphor Identification Using Verb and Noun Clustering}},
  author = {Shutova, Ekaterina and Sun, Lin and Korhonen, Anna},
  booktitle = {Proceedings of the 23rd International Conference on Computational Linguistics (Coling 2010)},
  year = {2010},
  url = {https://aclanthology.org/C10-1113/}
}
```

- [← previous Metaphor Corpus Annotated for Source - Target Domain Mappings](https://md.research.elamur.ai/free/en/papers/shutova-2010-metaphor-corpus-annotated-source-target/)
- [next → Modeling Morphosyntactic Agreement in Constituency-Based Parsing of Modern Hebrew](https://md.research.elamur.ai/free/en/papers/aclbulk-2010-tsarfaty-modeling-morphosyntactic-agreement-constituency-based/)
- [↑ back to papers of 2010](https://md.research.elamur.ai/free/en/papers/2010/)

## References

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