---
title: "Automatic Suggestions Help Extending — Straková 2026"
description: "Extending a semantic ontology with new word senses and concepts is a demanding task for human annotators."
authors: ["Jana Straková", "Eva Fučíková", "Zdenka Uresova", "Jan Hajič"]
year: 2026
venue: "Proceedings of the Fifteenth Language Resources and Evaluation Conference"
source: "https://aclanthology.org/2026.lrec-1.620/"
doi: "10.63317/4suozbd35x5o"
licence: "CC BY-NC 4.0 (ELRA, LREC proceedings)"
licence_url: "https://creativecommons.org/licenses/by-nc/4.0/"
summary_licence: "Elamur Cosmic License"
canonical: "https://research.elamur.ai/free/papers/strakova-2026-automatic-suggestions-help-extending-eventive/"
---
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# Automatic Suggestions Help Extending Eventive Ontology: A Case Study on SynSemClass

**Authors:** [Jana Straková](https://md.research.elamur.ai/free/authors/jana-strakova/), Eva Fučíková, [Zdenka Uresova](https://md.research.elamur.ai/free/authors/zdenka-uresova/), [Jan Hajič](https://md.research.elamur.ai/free/authors/jan-hajic/) · **Year:** 2026 · **Venue:** Proceedings of the Fifteenth Language Resources and Evaluation Conference · **Licence:** [CC BY-NC 4.0 (ELRA, LREC proceedings)](https://creativecommons.org/licenses/by-nc/4.0/)

**Links:** [Publisher's page](https://aclanthology.org/2026.lrec-1.620/) · [PDF](https://aclanthology.org/2026.lrec-1.620.pdf) · [DOI 10.63317/4suozbd35x5o](https://doi.org/10.63317/4suozbd35x5o) · [OpenAlex](https://openalex.org/W7160073607)

**Citations:** 0 citations (openalex-snapshot · 2026-10-14) · 1 in our library, of the 140673 papers whose bibliographies we read

## Summary
Extending a semantic ontology with new word senses and concepts is a demanding task for human annotators. This study looks at the Czech part of SynSemClass (SSC), a multilingual, hierarchical ontology of event and state concepts. Annotators assigned unprocessed verb valency frames from PDT-Vallex 4.5 to existing SSC 5.5 classes, or flagged frames that need a new class. They worked under three conditions: no assistance, suggestions grounded in sense-annotated sentences from PDT-C 2.0 (knowledge-driven), and suggestions built from surface-lemma contexts in the large raw corpus SYN v4 (corpus-driven). The suggestions came from a fine-tuned multilingual RemBERT classifier. The setups were evaluated with inter-annotator agreement and against a gold sample from an expert annotator.

Annotation proved hard in every setting. Machine suggestions brought statistically significant gains in both annotator consistency and agreement with the expert. The curated, frame-level source outperformed the much bigger lemma-based one, because lemma-only contexts mix senses and lose usefulness as ambiguity grows. These findings help guide how pre-annotation is used when building lexical-semantic resources, and they also show its limits.

## Key points
- **Suggestion model:** a fine-tuned RemBERT with 559M parameters scores a lemma in context against 1511 SSC classes, and annotators see the top 5 candidates. Lemmatization of SYN v4 was done with UDPipe.
- **Three annotation tasks:**
  - Task 1: highly ambiguous lemmas (about 17.85 frames per lemma), annotated without suggestions.
  - Task 2: PDT-C 2.0–based suggestions for both high- and low-ambiguity lemmas. The low-ambiguity part also received SYN v4 suggestions.
  - Task 3: low-ambiguity lemmas (about 1.51 frames per lemma), with SYN v4 suggestions only.
- **Ambiguity threshold:** a lemma counts as highly ambiguous if it has three or more valency frames in PDT-Vallex 4.5.
- **Frames shared across annotators and used in the evaluation:**

| Task | Shared frames | Annotators |
|---|---|---|
| Task 1 | 441 | 6 |
| Task 2 (high ambiguity) | 68 | 4 |
| Task 2 (low ambiguity) | 99 | 4 |
| Task 3 | 349 | 4 |

- **Main finding:** knowledge-driven suggestions from the small, curated corpus beat surface-lemma suggestions from the large raw corpus. Light verb constructions and erroneous frames were left out of the comparison.

*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: [Linguistics & languages](https://md.research.elamur.ai/free/shelves/linguistics/)

## Cite

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

```bibtex
@inproceedings{strakova2026automatic,
  title = {{Automatic Suggestions Help Extending Eventive Ontology: A Case Study on SynSemClass}},
  author = {Straková, Jana and Fučíková, Eva and Uresova, Zdenka and Hajič, Jan},
  booktitle = {Proceedings of the Fifteenth Language Resources and Evaluation Conference},
  year = {2026},
  doi = {10.63317/4suozbd35x5o},
  url = {https://doi.org/10.63317/4suozbd35x5o}
}
```

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