---
title: "Speculation and negation annotation in natural language texts:"
description: "The paper reflects on what building BioScope taught about annotating uncertainty and negation. 8 references, cited by 9 papers here."
authors: ["Veronika Vincze"]
year: 2010
venue: "Proceedings of the Workshop on Negation and Speculation in Natural Language Processing"
source: "https://aclanthology.org/W10-3105/"
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/vincze-2010-speculation-negation-annotation-natural-language/"
---
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# Speculation and negation annotation in natural language texts: what the case of BioScope might (not) reveal

**Authors:** [Veronika Vincze](https://md.research.elamur.ai/free/en/authors/veronika-vincze/) · **Year:** 2010 · **Venue:** Proceedings of the Workshop on Negation and Speculation in Natural Language Processing · **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/W10-3105/) · [PDF](https://aclanthology.org/W10-3105.pdf) · [OpenAlex](https://openalex.org/W1893328368)

**Citations:** 18 citations (OpenAlex, 2026-10-14) · [9](https://md.research.elamur.ai/free/en/papers/vincze-2010-speculation-negation-annotation-natural-language/cited-by/) in our library, of the 141,840 papers whose bibliographies we read

## Summary
The paper reflects on what building BioScope taught about annotating uncertainty and negation. Information extraction needs to keep factual statements apart from hedged or denied ones, and that requires suitably annotated corpora. BioScope is described as the largest resource with both negation and hedge cues and the only one that also marks linguistic scopes. It covers radiology reports, full biological papers and GENIA abstracts. Two linguistics students annotated it under the supervision of a linguist, who resolved disagreements. The author sets out the core guidelines: every cue has a scope that contains the cue, and cues are kept minimal while scopes are made maximal. She then goes through hard cases, including cues without a scope, ellipsis, multiword cues, overlapping and nested scopes, and ambiguous lexical items. BioScope is also compared with the event-centred GENIA Event corpus, and the two often disagree on what counts as uncertain. The lesson is that guidelines need to be fixed in advance but should keep evolving as the data reveals new cases.

## Key points
- BioScope contains three biomedical text types: radiological reports, full papers and GENIA abstracts. A further 15 full papers with hedge annotation served as the CoNLL-2010 Shared Task evaluation set.
- BioScope uses a single speculation category, whereas GENIA Event splits uncertainty into doubtful and probable. BioScope also leaves weasel words unannotated, while WikiWeasel targets them.
- Scopes must cover a continuous span and may not intersect. When two scopes would overlap, one of them (usually the negative one) is widened. Nested scopes are read as stacked operators, as in "possibly, no such event…".
- In one example sentence GENIA marks only 2 of 6 events as probable, while all 6 fall inside BioScope's speculative scope.
- Negated investigation verbs in the present perfect (e.g. *has not been described*) are treated as doubtful in GENIA but as negative in BioScope.

## Interesting ideas
- Mark the cue as the smallest unit that expresses hedging or negation, and stretch its scope to the largest syntactic unit, which gives a clear and repeatable boundary rule.
- A negative word can sit inside a hedge (*cannot be excluded*), so the mere presence of "not" does not make a sentence negated.
- Whether an item is a cue can depend on its construction (*indicate* versus *indicate that*) or its sense (*if*).
- An event sitting inside a speculative scope is not necessarily speculated itself. Only the asserted proposition carries the hedge, not every nominalised event embedded in it.
- Comparing two annotation schemes on the same sentences exposes systematic disagreements, such as the present-perfect negation case.

## For Mova

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

- **I1** (frames): annotate each negation or hedge cue on its tokens and assign as scope the maximal UD subtree of the clause it governs, so every scope decision can be traced to the tree.
- **I2** (frames): match multiword cue patterns before applying the single-word negation rule, so the phrase receives speculation rather than negation.
- **I3** (tree): decide cue status from the dependency configuration, for example whether a ccomp clause is present, rather than from the word form alone.
- **I4** (world): attach modality to the main asserted proposition and keep embedded events such as *changes* or *transcription* neutral, instead of marking everything in scope as uncertain.
- **I5** (learn): before merging silver data drawn from different guidelines, diff them on a shared sample to find convention clashes rather than treating them as noise.

*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

- [University of Szeged](https://md.research.elamur.ai/free/en/universities/university-of-szeged/) — University, Szeged, Hungary · 10 works · 1,373 citations

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## Cite

Cite as: Veronika Vincze. 2010. Speculation and negation annotation in natural language texts: what the case of BioScope might (not) reveal. Proceedings of the Workshop on Negation and Speculation in Natural Language Processing.

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

```bibtex
@inproceedings{vincze2010speculation,
  title = {{Speculation and negation annotation in natural language texts: what the case of BioScope might (not) reveal}},
  author = {Veronika Vincze},
  booktitle = {Proceedings of the Workshop on Negation and Speculation in Natural Language Processing},
  year = {2010},
  url = {https://aclanthology.org/W10-3105/}
}
```

- [← previous Fine-Grain Morphological Analyzer and Part-of-Speech Tagger for Arabic Text](https://md.research.elamur.ai/free/en/papers/sawalha-2010-fine-grain-morphological-analyzer-part/)
- [next → A Hedgehop over a Max-Margin Framework Using Hedge Cues](https://md.research.elamur.ai/free/en/papers/georgescul-2010-hedgehop-over-max-margin-framework/)
- [↑ back to papers of 2010](https://md.research.elamur.ai/free/en/papers/2010/page/2/)

## References

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

1. Richárd Farkas, Veronika Vincze, György Móra, János Csirik, and György Szarvas. 2010. [The CoNLL-2010 Shared Task: Learning to Detect Hedges and their Scope in Natural Language Text](https://md.research.elamur.ai/free/en/papers/farkas-2010-conll-2010-shared-task-learning/). In Proceedings of the Fourteenth Conference on Computational Natural Language Learning (CoNLL-2010): Shared Task, pages 1–12, Uppsala, Sweden, July. Association for Computational Linguistics.
2. Viola Ganter and Michael Strube. 2009. [Finding Hedges by Chasing Weasels: Hedge Detection Using Wikipedia Tags and Shallow Linguistic Features](https://md.research.elamur.ai/free/en/papers/ganter-2009-finding-hedges-chasing-weasels-hedge/). In Proceedings of the ACL-IJCNLP 2009 Conference Short Papers, pages 173–176, Suntec, Singapore, August. Association for Computational Linguistics.
3. Jin-Dong Kim, Tomoko Ohta, and Jun’ichi Tsujii. 2008. Corpus annotation for mining biomedical events from literature. BMC Bioinformatics, 9(Suppl 10). [↗](https://doi.org/10.1186/1471-2105-9-10)
4. Ben Medlock and Ted Briscoe. 2007. [Weakly Supervised Learning for Hedge Classification in Scientific Literature](https://md.research.elamur.ai/free/en/papers/medlock-2007-weakly-supervised-learning-hedge-classification/). In Proceedings of the ACL, pages 992– 999, Prague, Czech Republic, June.
5. Sampo Pyysalo, Filip Ginter, Juho Heimonen, Jari Björne, Jorma Boberg, Jouni Järvinen, and Tapio Salakoski. 2007. BioInfer: a corpus for information extraction in the biomedical domain. BMC Bioinformatics, 8(50). [↗](https://doi.org/10.1186/1471-2105-8-50)
6. Burr Settles, Mark Craven, and Lewis Friedland. 2008. Active learning with real annotation costs. In Proceedings of the NIPS Workshop on Cost-Sensitive Learning, pages 1–10. [find](https://scholar.google.com/scholar?q=Burr+Settles%2C+Mark+Craven%2C+and+Lewis+Friedland%2E+2008%2E+Active+learning+with+real+annotation+costs%2E+In+Proceedings+of+the+NIPS+Workshop+on+Cost%2DSensitive+Learning%2C+pages+1%E2%80%9310%2E)
7. Hagit Shatkay, Fengxia Pan, Andrey Rzhetsky, and W. John Wilbur. 2008. Multi-dimensional classification of biomedical text: Toward automated, practical provision of high-utility text to diverse users. Bioinformatics, 24(18):2086–2093. [↗](https://doi.org/10.1093/bioinformatics/btn381)
8. Veronika Vincze, György Szarvas, Richárd Farkas, György Móra, and János Csirik. 2008. The Bio-Scope Corpus: Biomedical Texts Annotated for Uncertainty, Negation and their Scopes. BMC Bioinformatics, 9(Suppl 11):S9. [find](https://scholar.google.com/scholar?q=Veronika+Vincze%2C+Gyo%CC%88rgy+Szarvas%2C+Richa%CC%81rd+Farkas%2C+Gyo%CC%88rgy+Mo%CC%81ra%2C+and+Ja%CC%81nos+Csirik%2E+2008%2E+The+Bio%2DScope+Corpus%3A+Biomedical+Texts+Annotated+for+Uncertainty%2C+Negation+and+their+Scopes%2E+BMC+Bioinformati)

*Updated 24 October 2026*