---
title: "Epistemic Modality and Knowledge Attribution in Scientific — paper by Waard and Maat (2012)"
description: "The paper looks at how biology articles signal how sure the writer is about a statement and whose knowledge it is. 29 references, cited by 8 papers here."
authors: ["Anita de Waard", "Henk Pander Maat"]
year: 2012
venue: "Proceedings of the Workshop on Detecting Structure in Scholarly Discourse"
source: "https://aclanthology.org/W12-4306/"
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/waard-2012-epistemic-modality-knowledge-attribution-scientific/"
---
[Elamur Research](https://md.research.elamur.ai/free/en/) · [this page in HTML](https://research.elamur.ai/free/en/papers/waard-2012-epistemic-modality-knowledge-attribution-scientific/)

[home](https://md.research.elamur.ai/free/en/) / [papers](https://md.research.elamur.ai/free/en/papers/) / [2012](https://md.research.elamur.ai/free/en/papers/2012/) / Epistemic Modality and Knowledge Attribution in Scientific Discourse: A Taxonomy of Types and Overview of Features

# Epistemic Modality and Knowledge Attribution in Scientific Discourse: A Taxonomy of Types and Overview of Features

**Authors:** Anita de Waard and Henk Pander Maat · **Year:** 2012 · **Venue:** Proceedings of the Workshop on Detecting Structure in Scholarly Discourse · **Publisher:** [Association for Computational Linguistics](https://md.research.elamur.ai/free/en/publishers/association-for-computational-linguistics/) · **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/W12-4306/) · [PDF](https://aclanthology.org/W12-4306.pdf) · [OpenAlex](https://openalex.org/W147215453)

**Citations:** 38 citations (OpenAlex, 2026-10-14) · 8 in our library, of the 141,840 papers whose bibliographies we read

## Summary
The paper looks at how biology articles signal how sure the writer is about a statement and whose knowledge it is. The authors want to track the point where a claim becomes an accepted fact and to extract newly claimed findings from large collections of papers. They review hedging and speculation research from linguistics, genre studies, bioinformatics and sentiment analysis, and build a taxonomy with three dimensions. Value runs from lack of knowledge through hypothetical and probable to presumed true. Basis is reasoning, data, or unclear. Source is the author, another party, or none, and it can be stated explicitly or left implicit. They pair this with four kinds of surface cue: modal auxiliaries, adverbs/adjectives, reporting verbs and references. They then annotate 812 clauses from two biology papers by hand. Reporting verbs, often in a governing matrix clause like "These results suggest", turn out to be the dominant signal. This points to a simple, syntax-based route to finding authors' new claims automatically.

## Key points
- The taxonomy has three facets. Value is coded 0–3 (lack of knowledge, hypothetical, dubitative, doxastic), basis is reasoning, data or implicit, and source is author, named other or absent, each either explicit or implicit.
- A reporting verb either appears in the clause or governs it from a matrix clause in about half of value-3 statements, 90% of value-2 statements and a third of value-1 statements.
- Particular verbs cluster with particular levels:
  - *hypothesize* (5×), *think* and *suspect* go with hypothetical claims.
  - *suggest* (18×) and *indicate* (12×) go with probable claims.
  - *demonstrate* (15×) and *show* (24×) go with claims treated as true.
- 44 segments carried no epistemic evaluation at all, mostly goal statements and method descriptions. Of these, 16 still had a reporting verb, and 12 of those were to-infinitive goal clauses.
- In one paper (Zimmermann et al., 2005), 42 reporting clauses fit one sentence-initial slot template: connective, determiner, modifier, NP, modal, VP and preposition.

## Interesting ideas
- Certainty and attribution are treated as separate modifiers wrapped around a bare proposition, borrowing the holder/strength/polarity frame from sentiment analysis rather than labelling whole sentences as speculative.
- The governing matrix clause with a reporting verb is the main carrier of epistemic status, so the cue sits in syntax and not only in the vocabulary.
- Each reporting verb is mapped to a graded certainty level (suggest → probable, demonstrate → presumed true), giving a small, explainable lexicon instead of a learned score.
- Lack-of-knowledge statements use their own negated phrasings, such as "remains to be determined" or "is unknown". The authors suggest catching these with string patterns rather than the general cue set.
- Goal clauses share reporting verbs with claims but differ in verb form (to-infinitive), so both verb class and tense/aspect are needed to avoid false hits.

## For Mova

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

- **I1** (frames): A proposition frame could carry separate slots for commitment level, evidence basis and source holder, filled from the governing clause.
- **I2** (tree): Detecting a reporting or cognitive verb that heads a ccomp in the UD tree would mark the embedded clause as an evaluated proposition rather than an asserted fact.
- **I3** (world): When a claim enters the world of the text, it could be stored with a certainty value taken from its governing verb, so that "suggested" facts stay distinct from "shown" ones.
- **I4** (answer): A pattern list for open questions would let readers answer "is this known?" honestly instead of treating an unestablished point as false.
- **I5** (learn): Infinitival goal clauses would make a good negative control set for testing an epistemic-status detector built on reporting verbs.

*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

- [Utrecht University](https://md.research.elamur.ai/free/en/universities/utrecht-university/) — University, Utrecht, Netherlands · 21 works · 1,281 citations

📚 Shelves (4): [🎭 Arts and Humanities](https://md.research.elamur.ai/free/en/shelves/arts-and-humanities/) · 9,296 works · [🗣️ Language and Linguistics](https://md.research.elamur.ai/free/en/shelves/arts-and-humanities/language-and-linguistics/) · 9,206 works · [🤔 Philosophy](https://md.research.elamur.ai/free/en/shelves/arts-and-humanities/philosophy/) · 211 works · [📚 Literature and Literary Theory](https://md.research.elamur.ai/free/en/shelves/arts-and-humanities/literature-and-literary-theory/) · 340 works

🧭 Topics (1): [🧵 Discourse analysis](https://md.research.elamur.ai/free/en/topics/discourse-analysis/) · 394 works

## Cite

Cite as: Anita de Waard and Henk Pander Maat. 2012. Epistemic Modality and Knowledge Attribution in Scientific Discourse: A Taxonomy of Types and Overview of Features. Proceedings of the Workshop on Detecting Structure in Scholarly Discourse.

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

```bibtex
@inproceedings{waard2012epistemic,
  title = {{Epistemic Modality and Knowledge Attribution in Scientific Discourse: A Taxonomy of Types and Overview of Features}},
  author = {Anita {de Waard} and Henk Pander Maat},
  booktitle = {Proceedings of the Workshop on Detecting Structure in Scholarly Discourse},
  year = {2012},
  url = {https://aclanthology.org/W12-4306/}
}
```

- [← previous Lemmatisation as a Tagging Task](https://md.research.elamur.ai/free/en/papers/gesmundo-2012-lemmatisation-as-tagging-task/)
- [next → Tokenization: Returning to a Long Solved Problem – A Survey, Contrastive Experiment, Recommendations, and Toolkit](https://md.research.elamur.ai/free/en/papers/dridan-2012-tokenization-returning-long-solved-problem/)
- [↑ back to papers of 2012](https://md.research.elamur.ai/free/en/papers/2012/)

## References

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

1. Crompton, P. (1997) Hedging in Academic Writing: Some Theoretical Problems, Eng Spec Purposes, Vol. 16, No. 4, pp. 271-287,1997. [find](https://scholar.google.com/scholar?q=Crompton%2C+P%2E+%281997%29+Hedging+in+Academic+Writing%3A+Some+Theoretical+Problems%2C+Eng+Spec+Purposes%2C+Vol%2E+16%2C+No%2E+4%2C+pp%2E+271%2D287%2C1997%2E)
2. De Haan, F. (1999), Evidentiality and Epistemic Modality: Setting Boundaries. Southwest Journal of Linguistics 18.83-101. [find](https://scholar.google.com/scholar?q=De+Haan%2C+F%2E+%281999%29%2C+Evidentiality+and+Epistemic+Modality%3A+Setting+Boundaries%2E+Southwest+Journal+of+Linguistics+18%2E83%2D101%2E)
3. De Waard, A., Pander Maat, H. (2009). Categorizing Epistemic Segment Types in Biology Research Articles. Wkshp on Linguistic and Psycholinguistic Approaches to Text Structuring (LPTS 2009), September 21-23, 2009. [find](https://scholar.google.com/scholar?q=De+Waard%2C+A%2E%2C+Pander+Maat%2C+H%2E+%282009%29%2E+Categorizing+Epistemic+Segment+Types+in+Biology+Research+Articles%2E+Wkshp+on+Linguistic+and+Psycholinguistic+Approaches+to+Text+Structuring+%28LPTS+2009%29%2C+September+)
4. De Waard, 2012. Anita de Waard, 2012-05-23, "Overview of epistemic evaluation and cues from literature", V1, [http://hdl.handle.net/1902.1/18253](http://hdl.handle.net/1902.1/18253) [↗](http://hdl.handle.net/1902.1/18253)
5. Hengeveld, K. & Mackenzie, J. L. (2008), Functional Discourse Grammar: A Typologically-Based Theory of Language Structure. Oxford Univ. [find](https://scholar.google.com/scholar?q=Hengeveld%2C+K%2E+%26+Mackenzie%2C+J%2E+L%2E+%282008%29%2C+Functional+Discourse+Grammar%3A+A+Typologically%2DBased+Theory+of+Language+Structure%2E+Oxford+Univ%2E)
6. Hovy, E.H. (2011). Private correspondence. [find](https://scholar.google.com/scholar?q=Hovy%2C+E%2EH%2E+%282011%29%2E+Private+correspondence%2E)
7. Hyland, K. (1995). The Author in the Text: Hedging Scientific Writing. Hong Kong Papers In Linguistics And Language Teaching, 18 (1995). [find](https://scholar.google.com/scholar?q=Hyland%2C+K%2E+%281995%29%2E+The+Author+in+the+Text%3A+Hedging+Scientific+Writing%2E+Hong+Kong+Papers+In+Linguistics+And+Language+Teaching%2C+18+%281995%29%2E)
8. Hyland, K. (2005). Stance and engagement: a model of interaction in academic discourse. Discourse Studies, Vol 7(2): 173–192. [↗](https://doi.org/10.1177/1461445605050365)
9. Kilicoglu H., Bergler S. (2008). Recognizing speculative language in biomedical research articles: a linguistically motivated perspective. [find](https://scholar.google.com/scholar?q=Kilicoglu+H%2E%2C+Bergler+S%2E+%282008%29%2E+Recognizing+speculative+language+in+biomedical+research+articles%3A+a+linguistically+motivated+perspective%2E)
10. BMC Bioinformatics. 2008 Nov 19;9 Suppl 11:S10. Kim, S-M. [find](https://scholar.google.com/scholar?q=BMC+Bioinformatics%2E+2008+Nov+19%3B9+Suppl+11%3AS10%2E+Kim%2C+S%2DM%2E)
11. Hovy, E.H. (2004). Determining the Sentiment of Opinions. Proceedings of the COLING conference, Geneva, 2004. [our card](https://md.research.elamur.ai/free/en/papers/kim-2004-determining-sentiment-opinions/)
12. Lakoff G. (1972). Hedges: a study in meaning criteria and the logic of fuzzy concepts. [↗](https://doi.org/10.1007/bf00262952) [our card](https://md.research.elamur.ai/free/en/papers/lakoff-1973-hedges-study-meaning-criteria/)
13. Chicago Linguistics Society Papers 1972, 8:183-228. [find](https://scholar.google.com/scholar?q=Chicago+Linguistics+Society+Papers+1972%2C+8%3A183%2D228%2E)
14. Latour, B., Woolgar, S. (1979). Laboratory Life: The Social Construction of Scientific Facts. Beverly Hills: Sage Publications. ISBN 0-80-390993-4. [find](https://scholar.google.com/scholar?q=Latour%2C+B%2E%2C+Woolgar%2C+S%2E+%281979%29%2E+Laboratory+Life%3A+The+Social+Construction+of+Scientific+Facts%2E+Beverly+Hills%3A+Sage+Publications%2E+ISBN+0%2D80%2D390993%2D4%2E)
15. Light M., Qiu X.Y., Srinivasan P. (2004). The language of bioscience: facts, speculations, and statements in between. BioLINK 2004: Linking Biological Literature, Ontologies and Databases 2004:17-24. [find](https://scholar.google.com/scholar?q=Light+M%2E%2C+Qiu+X%2EY%2E%2C+Srinivasan+P%2E+%282004%29%2E+The+language+of+bioscience%3A+facts%2C+speculations%2C+and+statements+in+between%2E+BioLINK+2004%3A+Linking+Biological+Literature%2C+Ontologies+and+Databases+2004%3A17%2D24%2E)
16. Martín-Martín, P. (2008). The Mitigation Of Scientific Claims In Research Papers: A Comparative Study. Int Jnl of English 2008 8(2): 133-152. [↗](https://doi.org/10.6018/ijes.8.2.49201)
17. Medlock B., Briscoe T. (2007). Weakly supervised learning for hedge classification in scientific literature. ACL 2007:992-999. [our card](https://md.research.elamur.ai/free/en/papers/medlock-2007-weakly-supervised-learning-hedge-classification/)
18. Myers, G. (1992). ‘In this paper we report’: Speech acts scientific facts, Jnl of Pragmatlcs 17 (1992) 295-313 [find](https://scholar.google.com/scholar?q=Myers%2C+G%2E+%281992%29%2E+%E2%80%98In+this+paper+we+report%E2%80%99%3A+Speech+acts+scientific+facts%2C+Jnl+of+Pragmatlcs+17+%281992%29+295%2D313)
19. Salager-Meyer, F. (1994), Hedges and Textual Communicative Function in Medical English Written Discourse, English for Specific Purposes, Vol. 13, No. 2, PP. 149-170, 1994. [find](https://scholar.google.com/scholar?q=Salager%2DMeyer%2C+F%2E+%281994%29%2C+Hedges+and+Textual+Communicative+Function+in+Medical+English+Written+Discourse%2C+English+for+Specific+Purposes%2C+Vol%2E+13%2C+No%2E+2%2C+PP%2E+149%2D170%2C+1994%2E)
20. Sándor, Á. and de Waard, A (2012). Identifying Claimed Knowledge Updates in Biomedical Research Articles, Workshop on Detecting Structure in Scholarly Discourse at ACL 2012 (this workshop). [find](https://scholar.google.com/scholar?q=S%C3%A1ndor%2C+%C3%81%2E+and+de+Waard%2C+A+%282012%29%2E+Identifying+Claimed+Knowledge+Updates+in+Biomedical+Research+Articles%2C+Workshop+on+Detecting+Structure+in+Scholarly+Discourse+at+ACL+2012+%28this+workshop%29%2E)
21. Tang, H., Tan, S., Cheng, X. (2009), A survey on sentiment detection of reviews, Expert Systems with Applications 36 (2009) 10760–10773. [find](https://scholar.google.com/scholar?q=Tang%2C+H%2E%2C+Tan%2C+S%2E%2C+Cheng%2C+X%2E+%282009%29%2C+A+survey+on+sentiment+detection+of+reviews%2C+Expert+Systems+with+Applications+36+%282009%29+10760%E2%80%9310773%2E)
22. Thomas, S. and Hawes, Th. P. (1994). Reporting Verbs in Medical Journal Articles, English for Specific Purposes, 1994 13(2), pp. 129-148. [find](https://scholar.google.com/scholar?q=Thomas%2C+S%2E+and+Hawes%2C+Th%2E+P%2E+%281994%29%2E+Reporting+Verbs+in+Medical+Journal+Articles%2C+English+for+Specific+Purposes%2C+1994+13%282%29%2C+pp%2E+129%2D148%2E)
23. Thompson P., Venturi G., McNaught J, Montemagni S, Ananiadou S. (2008). Categorising modality in biomedical texts.. LREC 2008: Building and Evaluating Resources for Biomedical Text Mining 2008. [find](https://scholar.google.com/scholar?q=Thompson+P%2E%2C+Venturi+G%2E%2C+McNaught+J%2C+Montemagni+S%2C+Ananiadou+S%2E+%282008%29%2E+Categorising+modality+in+biomedical+texts%2E%2E+LREC+2008%3A+Building+and+Evaluating+Resources+for+Biomedical+Text+Mining+2008%2E)
24. Verstraete, J.-C. (2001). Jnl of Pragmatics 33 (2001). [find](https://scholar.google.com/scholar?q=Verstraete%2C+J%2E%2DC%2E+%282001%29%2E+Jnl+of+Pragmatics+33+%282001%29%2E)
25. Vincze, V., Szarvas, Farkas, Móra and Csirik, (2008). The BioScope corpus: biomedical texts annotated for uncertainty, negation and their scopes, BMC Bioinformatics 2008, 9 (Suppl 11):S9. [find](https://scholar.google.com/scholar?q=Vincze%2C+V%2E%2C+Szarvas%2C+Farkas%2C+M%C3%B3ra+and+Csirik%2C+%282008%29%2E+The+BioScope+corpus%3A+biomedical+texts+annotated+for+uncertainty%2C+negation+and+their+scopes%2C+BMC+Bioinformatics+2008%2C+9+%28Suppl+11%29%3AS9%2E)
26. Voorhoeve P.M., le Sage C., et. al (2006). A genetic screen implicates miRNA-372 and miRNA-373 as oncogenes in testicular germ cell tumors. [find](https://scholar.google.com/scholar?q=Voorhoeve+P%2EM%2E%2C+le+Sage+C%2E%2C+et%2E+al+%282006%29%2E+A+genetic+screen+implicates+miRNA%2D372+and+miRNA%2D373+as+oncogenes+in+testicular+germ+cell+tumors%2E)
27. Wilson, T. and Wiebe, J., (2003), Annotating Opinions in the World Press, 2003, SigDAIL Wilbur W.J., Rzhetsky A, Shatkay H (2006). New directions in biomedical text annotations: definitions, guidelines and corpus construction. [find](https://scholar.google.com/scholar?q=Wilson%2C+T%2E+and+Wiebe%2C+J%2E%2C+%282003%29%2C+Annotating+Opinions+in+the+World+Press%2C+2003%2C+SigDAIL+Wilbur+W%2EJ%2E%2C+Rzhetsky+A%2C+Shatkay+H+%282006%29%2E+New+directions+in+biomedical+text+annotations%3A+definitions%2C+guideline)
28. BMC Bioinformatics 2006, 7:356. [find](https://scholar.google.com/scholar?q=BMC+Bioinformatics+2006%2C+7%3A356%2E)
29. Zimmermann,N., Colyer, JL, Koch, LE and Rothenberg, ME. (2005). Analysis of the CCR3 promoter reveals a regulatory region in exon 1 that binds GATA-1, BMC Immunology 2005, 6:7-7. [find](https://scholar.google.com/scholar?q=Zimmermann%2CN%2E%2C+Colyer%2C+JL%2C+Koch%2C+LE+and+Rothenberg%2C+ME%2E+%282005%29%2E+Analysis+of+the+CCR3+promoter+reveals+a+regulatory+region+in+exon+1+that+binds+GATA%2D1%2C+BMC+Immunology+2005%2C+6%3A7%2D7%2E)

*Updated 24 October 2026*