---
title: "The Pilot Corpus of the English Semantic Sketches (2022)"
description: "The authors build semantic sketches instead. 11 references."
authors: ["Maria Petrova", "Maria Ponomareva", "Alexandra Ivoylova"]
year: 2022
venue: "arXiv:2505.17733 · In Computational Linguistics and Intellectual Technologies: Proceedings of the International Conference \"Dialogue 2022\". Moscow, Russia. 2022"
source: "https://arxiv.org/abs/2505.17733"
doi: "10.48550/arxiv.2505.17733"
arxiv: "2505.17733"
licence: "CC BY 4.0 (arXiv)"
licence_url: "http://creativecommons.org/licenses/by/4.0/"
summary_licence: "Elamur Cosmic License"
canonical: "https://research.elamur.ai/free/en/papers/petrova-2025-pilot-corpus-english-semantic-sketches/"
---
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# The Pilot Corpus of the English Semantic Sketches

**Authors:** Maria Petrova, Maria Ponomareva and Alexandra Ivoylova · **Year:** 2022 · **Venue:** arXiv:2505.17733 · In Computational Linguistics and Intellectual Technologies: Proceedings of the International Conference "Dialogue 2022". Moscow, Russia. 2022 · **Licence:** [CC BY 4.0 (arXiv)](http://creativecommons.org/licenses/by/4.0/)

**Links:** [Publisher's page](https://arxiv.org/abs/2505.17733) · [PDF](https://arxiv.org/pdf/2505.17733) · [DOI 10.48550/arxiv.2505.17733](https://doi.org/10.48550/arxiv.2505.17733) · [arXiv 2505.17733](https://arxiv.org/abs/2505.17733)

## Summary
The paper tackles a weakness of classic word sketches, such as those in Sketch Engine. These group a verb's dependents by syntactic relation and blend all of the verb's senses into one picture. The authors build *semantic sketches* instead. For each separate verb sense, a sketch lists the semantic roles the verb takes and ranks the most typical words filling each role. The roles and senses come from the Compreno parser's full semantic analysis, so a sketch reflects neighbours in the parse graph rather than in a linear word window. The new English pilot corpus contains 100 hand-selected sketches built from a mixed-genre corpus, and each is paired with a Russian sketch for the same semantic class. The authors use these pairs for contrastive analysis, for example how English *pour* divides its objects compared with Russian *лить*/*сыпать*. They also classify the typical defects of automatically built sketches. The resource gives an interpretable, sense-aware view of verb meaning that can support word sense disambiguation (WSD) and cross-language lexicography.

## Key points
- **English corpus:** 100 manually vetted sketches, built from technical, news and fiction texts. These texts contain about 14 million verb-headed syntactic links.
- **Pairing and verb selection:** The English sketches are matched with 84 Russian sketches, giving 113 English–Russian pairs. Verbs were drawn from the same semantic classes as the earlier polysemous Russian corpus.
- **Frequency thresholds:** An English verb needed at least 200 links. The Russian threshold was 2000, on a dataset of more than 36 million links.
- **Display and data source:** Each role normally shows its eight most frequent fillers. Senses and roles come from Compreno's thesaurus-like semantic hierarchy.
- **Error types identified:**
  - sparse ("empty") role columns;
  - slots belonging to a different sense;
  - wrong fillers caused by frequent homonyms outscoring correct analyses;
  - preposition ambiguity, e.g. a *for*-phrase expressing distance misfiled as Purpose;
  - statistical fluctuations between languages.
- **Availability:** The corpus is on GitHub, with plans to grow it and add more languages.

## Interesting ideas
- Build a separate role-by-filler profile for each verb sense rather than for each lemma, so that typical arguments describe a meaning instead of a word form.
- Take a verb's context from its typed dependency neighbours in the parse graph instead of a fixed linear window. This keeps the representation interpretable and filters out irrelevant nearby tokens.
- Read aggregated sketches as a diagnostic of parser behaviour, since misplaced fillers expose systematic role-labelling mistakes. An example is a distance phrase landing under Purpose.
- Separate truly narrow slots from sparse data: verbs acting like lexical functions legitimately have very few fillers, while other gaps simply reflect too little data.
- Compare same-sense verbs across languages by the semantic features of their object fillers, as with liquid versus granular objects for *pour* compared with *лить*/*сыпать*.

## For Mova

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

- **I1** (world): Compiling such per-sense filler tables from parsed text could give verb classes explicit selectional preferences that the world layer can inspect and cite.
- **I2** (frames): Role assignment could check candidate arguments against filler statistics gathered over UD/frame edges, staying fully explainable with no bag of words.
- **I3** (learn): Scanning compiled role/filler tables for outliers could surface candidate self-induced repair rules for frequent misclassifications.
- **I4** (world): Marking each slot as "closed by nature" or "under-observed" would prevent the knowledge base from drawing strong conclusions from thin evidence.
- **I5** (back): Translation through the meta-world could choose the target verb from the features of the object entity rather than from a one-to-one lexical mapping.

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

📚 Shelves (2): [🎭 Arts and Humanities](https://md.research.elamur.ai/free/en/shelves/arts-and-humanities/) · 17,450 works · [🗣️ Language and Linguistics](https://md.research.elamur.ai/free/en/shelves/arts-and-humanities/language-and-linguistics/) · 17,296 works

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## Cite

Cite as: Maria Petrova, Maria Ponomareva and Alexandra Ivoylova. 2022. The Pilot Corpus of the English Semantic Sketches. arXiv:2505.17733 · In Computational Linguistics and Intellectual Technologies: Proceedings of the International Conference "Dialogue 2022". Moscow, Russia. 2022.

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

```bibtex
@misc{petrova2022pilot,
  title = {{The Pilot Corpus of the English Semantic Sketches}},
  author = {Maria Petrova, Maria Ponomareva and Alexandra Ivoylova},
  year = {2022},
  doi = {10.48550/arxiv.2505.17733},
  eprint = {2505.17733},
  archivePrefix = {arXiv},
  url = {https://doi.org/10.48550/arxiv.2505.17733}
}
```

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## References

11 references, 3 of them with a card of ours. ↗ leads to the work, «find» searches for one we have no id for yet.

1. KV Anisimovich, K Yu Druzhkin, KA Zuev, FR Minlos, MA Petrova, and VP Selegei. 2012. Syntactic and semantic parser based on abbyy compreno linguistic technologies. // Proc Dialogue, Russian International Conference on Computational Linguistics, P 91–103. [find](https://scholar.google.com/scholar?q=KV+Anisimovich%2C+K+Yu+Druzhkin%2C+KA+Zuev%2C+FR+Minlos%2C+MA+Petrova%2C+and+VP+Selegei%2E+2012%2E+Syntactic+and+semantic+parser+based+on+abbyy+compreno+linguistic+technologies%2E+%2F%2F+Proc+Dialogue%2C+Russian+Internatio)
2. Eva Van Assche, Wouter Duyck, and Robert J Hartsuiker. 2012. Bilingual word recognition in a sentence context. Frontiers in psychology, 3:174. [↗](https://doi.org/10.3389/fpsyg.2012.00174)
3. Curtis A Becker. 1980. Semantic context effects in visual word recognition: An analysis of semantic strategies. Memory & cognition, 8(6):493–512. [↗](https://doi.org/10.3758/bf03213769)
4. Piotr Bojanowski, Edouard Grave, Armand Joulin, and Tomas Mikolov. 2017. [Enriching word vectors with subword information](https://md.research.elamur.ai/free/en/papers/bojanowski-2017-enriching-word-vectors-subword-information/). Transactions of the Association for Computational Linguistics, 5:135–146. [↗](https://doi.org/10.1162/tacl_a_00051)
5. Julia Detkova, Valeriy Novitskiy, Maria Petrova, and Vladimir Selegey. 2020. Differential semantic sketches for russian internet-corpora. // Proc Dialogue, Russian International Conference on Computational Linguistics, Moscow. [find](https://scholar.google.com/scholar?q=Julia+Detkova%2C+Valeriy+Novitskiy%2C+Maria+Petrova%2C+and+Vladimir+Selegey%2E+2020%2E+Differential+semantic+sketches+for+russian+internet%2Dcorpora%2E+%2F%2F+Proc+Dialogue%2C+Russian+International+Conference+on+Computat)
6. Jacob Devlin, Ming-Wei Chang, Kenton Lee, and Kristina Toutanova. 2019. [BERT: Pre-training of deep bidirectional transformers for language understanding](https://md.research.elamur.ai/free/en/papers/devlin-2019-bert-pre-training-deep-bidirectional/). // Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers), P 4171–4186, Minneapolis, Minnesota, June. Association for Computational Linguistics.
7. Adam Kilgarriff, Vít Baisa, Jan Bušta, Miloš Jakubíček, Vojtěch Kovář, Jan Michelfeit, Pavel Rychlỳ, and Vít Suchomel. 2014. The sketch engine: ten years on. Lexicography, 1(1):7–36. [find](https://scholar.google.com/scholar?q=Adam+Kilgarriff%2C+V%C3%ADt+Baisa%2C+Jan+Bu%C5%A1ta%2C+Milo%C5%A1+Jakub%C3%ADc%CC%8Cek%2C+Vojte%CC%8Cch+Kov%C3%A1r%CC%8C%2C+Jan+Michelfeit%2C+Pavel+Rychly%CC%80%2C+and+V%C3%ADt+Suchomel%2E+2014%2E+The+sketch+engine%3A+ten+years+on%2E+Lexicography%2C+1%281%29%3A7%E2%80%9336%2E)
8. Tomas Mikolov, Ilya Sutskever, Kai Chen, Greg Corrado, and Jeffrey Dean. 2013. [Distributed representations of words and phrases and their compositionality](https://md.research.elamur.ai/free/en/papers/mikolov-2013-distributed-representations-words-phrases-their/). // Neural and Information Processing System (NIPS). [↗](https://doi.org/10.48550/arxiv.1310.4546)
9. MA Petrova. 2014. The compreno semantic model: the universality problem. International Journal of Lexicography, 27(2):105–129. [↗](https://doi.org/10.1093/ijl/ect038)
10. Maria Ponomareva, Maria Petrova, Julia Detkova, Oleg Serikov, and Maria Yarova. 2021. Semsketches-2021: experimenting with the machine processing of the pilot corpus. // Proc Dialogue, Russian International Conference on Computational Linguistics, Moscow. [find](https://scholar.google.com/scholar?q=Maria+Ponomareva%2C+Maria+Petrova%2C+Julia+Detkova%2C+Oleg+Serikov%2C+and+Maria+Yarova%2E+2021%2E+Semsketches%2D2021%3A+experimenting+with+the+machine+processing+of+the+pilot+corpus%2E+%2F%2F+Proc+Dialogue%2C+Russian+Interna)
11. James R Tweedy, Robert H Lapinski, and Roger W Schvaneveldt. 1977. Semantic-context effects on word recognition: Influence of varying the proportion of items presented in an appropriate context. Memory & Cognition, 5(1):84–89. https://github.com/dialogue-evaluation/SemSketches/tree/main/data/task_2 [↗](https://doi.org/10.3758/bf03209197)

*Updated 1 November 2026*