---
title: "SimVerb-3500 — Gerz 2016"
description: "Verbs are central to sentence meaning, yet intrinsic benchmarks for word representations have mostly targeted nouns or included only a few verb pairs."
authors: ["Daniela Gerz", "Ivan Vulić", "Felix Hill", "Roi Reichart", "Anna Korhonen"]
year: 2016
venue: "Proceedings of the 2016 Conference on Empirical Methods in Natural Language Processing"
source: "https://aclanthology.org/D16-1235/"
doi: "10.18653/v1/d16-1235"
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/gerz-2016-simverb-3500-large-scale-evaluation/"
---
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# SimVerb-3500: A Large-Scale Evaluation Set of Verb Similarity

**Authors:** [Daniela Gerz](https://md.research.elamur.ai/free/en/authors/daniela-gerz/), [Ivan Vulić](https://md.research.elamur.ai/free/en/authors/ivan-vulic/), [Felix Hill](https://md.research.elamur.ai/free/en/authors/felix-hill/), [Roi Reichart](https://md.research.elamur.ai/free/en/authors/roi-reichart/), [Anna Korhonen](https://md.research.elamur.ai/free/en/authors/anna-korhonen/) · **Year:** 2016 · **Venue:** Proceedings of the 2016 Conference on Empirical Methods in Natural Language Processing · **Licence:** [CC BY 4.0 (ACL Anthology)](https://creativecommons.org/licenses/by/4.0/)

**Links:** [Publisher's page](https://aclanthology.org/D16-1235/) · [PDF](https://aclanthology.org/D16-1235.pdf) · [DOI 10.18653/v1/d16-1235](https://doi.org/10.18653/v1/d16-1235) · [OpenAlex](https://openalex.org/W2510413766)

**Citations:** 218 citations (openalex-snapshot · 2026-10-14) · [97](#cited-by) in our library, of the 140884 papers whose bibliographies we read

## Summary
Verbs are central to sentence meaning, yet intrinsic benchmarks for word representations have mostly targeted nouns or included only a few verb pairs. On the existing verb subset of SimLex-999, automatic systems already score above inter-annotator agreement. To address this, the authors built SimVerb-3500, a set of 3,500 verb pairs with human similarity ratings on a 0–10 scale. Annotators followed the SimLex-999 guidelines, which separate similarity from relatedness and ask that antonyms receive low scores. Pairs were sampled so the set covers every normed verb in the USF free-association norms and includes several members of each top-level VerbNet class. This ties the dataset to resources such as WordNet, PropBank and FrameNet. The authors benchmarked several representation models, including SGNS with bag-of-words and dependency contexts, symmetric-pattern vectors, non-distributional vectors, Paragram and Paragram+CF. These systems remain clearly below human agreement. Raw-text distributional models perform especially poorly on rare and highly polysemous verbs. The resource makes statistically robust, fine-grained comparisons of verb representations possible.

## Key points
- SimVerb-3500 has 3,500 rated pairs covering 827 verb types from USF, and every one of the 101 top-level VerbNet classes is represented by no fewer than 3 verbs.
- SimLex-999 has only 222 verb pairs and 170 verb lemmas, and it leaves 39 top-level VerbNet classes uncovered. On that subset, the best system (ρ = 0.727, Mrkšić et al., 2016) slightly exceeds human agreement (ρ = 0.717).
- Each pair carries a WordNet-derived relation label. Examples include 373 no-relation pairs, 306 synonym pairs and 800 hyper/hyponym pairs.
- Model scores differ noticeably across VerbNet classes, for example psychological-state verbs (class 31) versus motion verbs (class 51). Paragram+CF is the strongest model overall in these breakdowns.
- Annotator rating spread is about 1.3 regardless of verb frequency or polysemy. It is lower for antonyms (0.86) and unrelated pairs (0.92), which suggests that humans, unlike models, are largely unaffected by frequency and ambiguity.

## Interesting ideas
- **Sample pairs from association norms.** Drawing pairs from free-association data yields both truly similar and merely related pairs. This forces a benchmark to test whether models can tell the two apart, and it would work for any language with association norms.
- **Anchor coverage in a lexical taxonomy.** Guaranteeing a minimum number of items per class of a lexicon such as VerbNet makes the benchmark representative by design. It also enables per-class error analysis and links the data to other linked resources.
- **Report results by subset.** Breaking scores down by frequency band, polysemy, lexical relation and semantic class shows where a model fails instead of hiding weaknesses in one global number. The same practice suits treebank or LAS evaluations.
- **Use rating variance as a diagnostic.** Comparing the average standard deviation of human ratings across subsets separates genuinely hard items from items that only models find hard.
- **Check whether the metric fits each subset.** Spearman's ρ is unsuitable for groups like antonyms, whose internal ranking is arbitrary because all should score near zero. Verifying that a rank-based metric suits each subset before reporting it is a useful general habit.

*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/)

Topics: [Word sense disambiguation](https://md.research.elamur.ai/free/en/topics/word-sense-disambiguation/) · [Semantic role labeling](https://md.research.elamur.ai/free/en/topics/semantic-role-labeling/)

## Cited by

Cited by

97 papers of our library cite it (of the 140884 whose bibliographies we read); 6 of them have a page here, 91 not yet.

- [Analysis Methods in Neural Language Processing: A Survey](https://md.research.elamur.ai/free/en/papers/belinkov-2019-analysis-methods-neural-language-processing/)
- [Interpreting Pretrained Contextualized Representations via Reductions to Static Embeddings](https://md.research.elamur.ai/free/en/papers/bommasani-2020-interpreting-pretrained-contextualized-representations-reductions/)
- [WordNet under Scrutiny: Dictionary Examples in the Era of Large Language Models](https://md.research.elamur.ai/free/en/papers/lex-2024-almeman-wordnet-examples-llm/)
- [One Word Is Not Enough: Simple Prompts Improve Word Embeddings](https://md.research.elamur.ai/free/en/papers/ranjan-2026-one-word-is-not-enough/)
- [Prompting Instruction-tuned LLMs for Semantic Similarity Values](https://md.research.elamur.ai/free/en/papers/snelder-2026-prompting-instruction-tuned-llms-semantic/)
- [On the Limitations of Unsupervised Bilingual Dictionary Induction](https://md.research.elamur.ai/free/en/papers/sogaard-2018-limitations-unsupervised-bilingual-dictionary-induction/)

## Cite

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

```bibtex
@inproceedings{gerz2016simverb,
  title = {{SimVerb-3500: A Large-Scale Evaluation Set of Verb Similarity}},
  author = {Gerz, Daniela and Vulić, Ivan and Hill, Felix and Reichart, Roi and Korhonen, Anna},
  booktitle = {Proceedings of the 2016 Conference on Empirical Methods in Natural Language Processing},
  year = {2016},
  doi = {10.18653/v1/d16-1235},
  url = {https://doi.org/10.18653/v1/d16-1235}
}
```

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36. ICLR. John Wieting, Mohit Bansal, Kevin Gimpel, and Karen Livescu. 2015. From paraphrase database to compositional paraphrase model and back. Transactions of the ACL, 3:345–358. [find](https://scholar.google.com/scholar?q=ICLR%2E+John+Wieting%2C+Mohit+Bansal%2C+Kevin+Gimpel%2C+and+Karen+Livescu%2E+2015%2E+From+paraphrase+database+to+compositional+paraphrase+model+and+back%2E+Transactions+of+the+ACL%2C+3%3A345%E2%80%93358%2E) [our card](https://md.research.elamur.ai/free/en/papers/wieting-2015-paraphrase-database-compositional-paraphrase-model/)