---
title: "A Compositional Interpretation of Biomedical Event Factuality — paper by Kilicoglu et al. (2015)"
description: "The authors do not train a classifier. 49.4% F1. 28 references, cited by 3 papers here."
authors: ["Halil Kilicoglu", "Graciela Rosemblat", "Michael Cairelli", "Thomas Rindflesch"]
year: 2015
venue: "Proceedings of the Second Workshop on Extra-Propositional Aspects of Meaning in Computational Semantics (ExProM 2015)"
source: "https://aclanthology.org/W15-1303/"
doi: "10.3115/v1/w15-1303"
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/kilicoglu-2015-compositional-interpretation-biomedical-event-factuality/"
---
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# A Compositional Interpretation of Biomedical Event Factuality

**Authors:** [Halil Kilicoglu](https://md.research.elamur.ai/free/en/authors/halil-kilicoglu/), Graciela Rosemblat, Michael Cairelli and Thomas Rindflesch · **Year:** 2015 · **Venue:** [Proceedings of the Second Workshop on Extra-Propositional Aspects of Meaning in Computational Semantics (ExProM 2015)](https://md.research.elamur.ai/free/en/venues/workshop-on-extra-propositional-aspects-of-meaning-in-computational-semantics/) ([Workshop on Extra-Propositional Aspects of Meaning in Computational Semantics 2015](https://md.research.elamur.ai/free/en/venues/workshop-on-extra-propositional-aspects-of-meaning-in-computational-semantics/2015/)) · **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/W15-1303/) · [PDF](https://aclanthology.org/W15-1303.pdf) · [DOI 10.3115/v1/w15-1303](https://doi.org/10.3115/v1/w15-1303) · [OpenAlex](https://openalex.org/W2251161456)

**Citations:** 11 citations (OpenAlex, 2026-10-14) · 3 in our library, of the 141,840 papers whose bibliographies we read

## Summary
The paper asks whether a biomedical event reported in a MEDLINE abstract is presented as established, possible, or negated. The authors do not train a classifier. They apply a general framework for extra-propositional meaning in which every relation is a predication. Predications can nest inside each other, and each one carries a source and a scalar modality value between 0 and 1. A dictionary sorts embedding predicates into a fine-grained taxonomy (MODAL, RELATIONAL, VALENCE SHIFTER, PROPOSITIONAL) and gives each a prior value. An event's factuality is the product of the values of all operators whose scope covers it. Predications are built from dependency parses plus hand-written transformation rules. The output is then mapped to the certainty and polarity labels of the meta-knowledge corpus, which is built on GENIA events. With almost no use of training data, certainty-level results come close to a supervised state-of-the-art system. Negative polarity is clearly weaker. This suggests that treating negation as simply the bottom of a modal scale is not enough. The work shows that a transparent, rule-based composition can stand up well against machine learning on a semantic task.

## Key points
- Evaluation uses the meta-knowledge corpus (Thompson et al., 2011), which annotates GENIA event corpus events with Certainty Level (L1–L3), Polarity, Knowledge Type, Manner and Source. Only certainty and polarity are targeted here.
- On Analysis events, certainty accuracy is about 82%, against a 55% majority-class baseline. F1 for L3/L2/L1 is 86.2/73.3/79.6.
- On all events, F1 is 97.5/66.6/68.1 for L3/L2/L1. The supervised system of Miwa et al. (2012) reaches 97.6/66.5/74.9.
- Polarity results are mixed. Negative polarity reaches 49.4% F1 against Miwa et al.'s 63.4%. Positive polarity is close, at 97.5% vs 97.7%.
- In a graded evaluation, gold labels are mapped to numbers (L3-Positive=1 down to L3-Negative=0). The system's average distance is 0.12, compared with 0.21 for the majority baseline.

## Interesting ideas
- Factuality is computed by multiplying the scalar values of all embedding operators that scope over an event, starting from 1 for a plain declarative written by the author; this makes each step traceable and stacks naturally for nested hedges.
- Embedding predicates are sorted into a small taxonomy of classes, each tied to its own scale (epistemic, deontic, success, and so on), with secondary epistemic readings recorded; this lets one dictionary cover modals, negators, and verbs like "fail" or "manage".
- Ordinal labels are scored by average numeric distance rather than plain accuracy, so a near miss (somewhat vs. highly speculative) costs less than a gross error.
- The error analysis shows that parser mistakes, especially PP-attachment errors, quietly break semantic dependencies, and that patch rules on dependencies catch only some of these cases.
- The weak negation result suggests that negative polarity should come from explicit scope under a NEGATOR predicate, not from a low value on a modal scale.

## For Mova

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

- **I1** (frames): Each frame could carry a source and an epistemic value in [0,1], updated by every modal or valence-shifting predicate that dominates it in the UD tree, so the confidence of every claim can be explained.
- **I2** (world): A compiled lexicon of such operator classes with prior scalar values could sit next to the VerbNet classes, letting the world model tell whether a state actually holds, might hold, or was attempted but failed.
- **I3** (learn): Graded factuality and certainty outputs could be scored by distance to gold or silver values, with a majority-class baseline used as a negative control.
- **I4** (tree): Collecting attachments that break operator scope (for example "ability … to X" attached to the main verb) would give a focused set of repair rules and test cases for the parser.
- **I5** (frames): Polarity could be stored as its own flag driven by negator scope, kept separate from the epistemic value, so that "may not" and "did not" stay distinct in the frame.

*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

- [National Institutes of Health](https://md.research.elamur.ai/free/en/government/national-institutes-of-health/) — Government body, Bethesda, United States · 9 works · 3,833 citations
- [United States National Library of Medicine](https://md.research.elamur.ai/free/en/labs/united-states-national-library-of-medicine/) — Research institute, Bethesda, United States · 5 works · 5,321 citations

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## Cite

Cite as: Halil Kilicoglu, Graciela Rosemblat, Michael Cairelli and Thomas Rindflesch. 2015. A Compositional Interpretation of Biomedical Event Factuality. Proceedings of the Second Workshop on Extra-Propositional Aspects of Meaning in Computational Semantics (ExProM 2015).

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

```bibtex
@inproceedings{kilicoglu2015compositional,
  title = {{A Compositional Interpretation of Biomedical Event Factuality}},
  author = {Halil Kilicoglu, Graciela Rosemblat, Michael Cairelli and Thomas Rindflesch},
  booktitle = {Proceedings of the Second Workshop on Extra-Propositional Aspects of Meaning in Computational Semantics (ExProM 2015)},
  year = {2015},
  doi = {10.3115/v1/w15-1303},
  url = {https://doi.org/10.3115/v1/w15-1303}
}
```

- [← previous Shallow Discourse Parsing Using Constituent Parsing Tree](https://md.research.elamur.ai/free/en/papers/chen-2015-shallow-discourse-parsing-constituent-parsing/)
- [next → A Study of the Effect of Resolving Negation and Sentiment Analysis in Recognizing Text Entailment for Arabic](https://md.research.elamur.ai/free/en/papers/al-khawaldeh-2019-study-effect-resolving-negation-sentiment/)
- [↑ back to papers of 2015](https://md.research.elamur.ai/free/en/papers/2015/page/3/)

## References

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

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2. Marie-Catherine de Marneffe, Bill MacCartney, and Christopher D. Manning. 2006. Generating typed dependency parses from phrase structure parses. In Proceedings of the 5th International Conference on Language Resources and Evaluation, pages 449–454. [our card](https://md.research.elamur.ai/free/en/papers/marneffe-2006-generating-typed-dependency-parses-phrase/)
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*Updated 24 October 2026*