---
title: "”You should probably read this”: Hedge Detection in Text (2024)"
description: "The paper addresses sentence-level hedge detection. 69.74 F1. 22 references. Among them: 0."
authors: ["Denys Katerenchuk", "Rivka Levitan"]
year: 2024
venue: "arXiv:2405.13319"
source: "https://arxiv.org/abs/2405.13319"
doi: "10.48550/arxiv.2405.13319"
arxiv: "2405.13319"
licence: "arXiv non-exclusive distribution licence (read only, no redistribution)"
licence_url: "http://arxiv.org/licenses/nonexclusive-distrib/1.0/"
summary_licence: "Elamur Cosmic License"
canonical: "https://research.elamur.ai/free/en/papers/katerenchuk-2024-should-probably-read-this-hedge/"
---
[Elamur Research](https://md.research.elamur.ai/free/en/) · [this page in HTML](https://research.elamur.ai/free/en/papers/katerenchuk-2024-should-probably-read-this-hedge/)

[home](https://md.research.elamur.ai/free/en/) / [papers](https://md.research.elamur.ai/free/en/papers/) / [2024](https://md.research.elamur.ai/free/en/papers/2024/page/15/) / ”You should probably read this”: Hedge Detection in Text

# ”You should probably read this”: Hedge Detection in Text

**Authors:** Denys Katerenchuk and [Rivka Levitan](https://md.research.elamur.ai/free/en/authors/rivka-levitan/) · **Year:** 2024 · **Venue:** arXiv:2405.13319 · **Licence:** [arXiv non-exclusive distribution licence (read only, no redistribution)](http://arxiv.org/licenses/nonexclusive-distrib/1.0/)

**Links:** [Publisher's page](https://arxiv.org/abs/2405.13319) · [PDF](https://arxiv.org/pdf/2405.13319) · [DOI 10.48550/arxiv.2405.13319](https://doi.org/10.48550/arxiv.2405.13319) · [arXiv 2405.13319](https://arxiv.org/abs/2405.13319) · [OpenAlex](https://openalex.org/W4398795203)

**Citations:** 0 citations (OpenAlex, 2026-10-14)

## Summary
The paper addresses sentence-level hedge detection. The task is to decide whether a sentence signals that the writer is unsure of a claim, which matters in high-stakes fields such as medicine or finance. The authors use the Wikipedia portion of the CoNLL-2010 shared task, which is harder than the BioScope portion because it includes weasel words. They compare several pre-trained and custom word embeddings and test CNN, GRU, LSTM and transformer classifiers, with and without attention. They then add part-of-speech information from spaCy, either by appending tag vectors to word vectors or by running separate word and POS networks that merge only at the last hidden layer. Tags alone turn out to be predictive. The joint word+POS models reach 69.74 F1, and a plain GRU with small in-domain FastText embeddings reaches 70.24 F1. Both beat the earlier attention-based best of 67.52. The authors argue that on a dataset this small, simpler recurrent models and cheap syntactic signals can outperform heavier architectures such as RoBERTa.

## Key points
- Data: the CoNLL-2010 Wikipedia corpus has 11,110 training sentences (10% held out for development) and 9,634 evaluation sentences. It is imbalanced, with 77.26% of sentences certain, so results are reported as F1. Sentences are capped at 64 tokens; the mean length is 22.5.
- Embedding comparison on dev (single-layer BiLSTM):
  - FastText 2M scored 63.57 F1.
  - GloVe 300d 6B scored 63.12 F1.
  - A custom 256-dimensional FastText model trained on 1 GB of Wikipedia (Wiki 1G) scored 61.99 F1, despite its small size.
- Architecture comparison:
  - GRU-based models did best on average. The authors attribute this to overfitting by more complex models on roughly 10k sentences.
  - Fine-tuned RoBERTa reached only 60.26 F1 before it overfit.
- POS-only models, using learned 8-dimensional tag embeddings, scored 47.64–48.9 F1. This shows the tags carry a real signal.
- Final evaluation results:
  - The joint word+POS formulation reached 69.74 F1.
  - A GRU with Wiki 1G embeddings reached 70.24 F1.
  - The previous baseline (Adel & Schütze, attention) was 67.52.

## Interesting ideas
- Train a classifier on POS tags alone, as a probe for whether structure by itself carries the target signal, before investing in fusion.
- Keep word evidence and grammatical evidence in two independent streams that meet only at the final layer, instead of concatenating them per token.
- Hedges come in kinds (modal verbs, peacock expressions, weasel words), and a cue word does not guarantee uncertainty in context.
- Report the mean development score over several iterations sampled after convergence, rather than the single best checkpoint, to avoid a lucky configuration.
- A small embedding model trained on in-domain text can rival much larger general ones on a small task.

## For Mova

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

- **I1** (learn): Run tag-only or tree-only versions of a pragmatic classifier as a control, to show how much of a result comes from structure rather than from lexical items.
- **I2** (frames): Epistemic marking in prag could keep a lexical-cue score and a structural score apart, so the explanation says which evidence flagged a hedged assertion.
- **I3** (world): Claims entered into the world of the text could carry a certainty status and cue category, so the readers treat "some believe X" differently from a stated fact.
- **I4** (learn): Apply the same averaged-window reporting when comparing parser or silver-data variants, so small differences are not mistaken for gains.
- **I5** (tokens): Cheap domain-specific resources trained on fables or school texts may serve the tagger and lemma disambiguation as well as large general ones, and are easier to inspect.

*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

- [The Graduate Center, CUNY](https://md.research.elamur.ai/free/en/universities/the-graduate-center-cuny/) — University, New York, United States · 13 works · 207 citations
- [Brooklyn College](https://md.research.elamur.ai/free/en/universities/brooklyn-college/) — University, Brooklyn, United States · 2 works · 4 citations

📚 Shelves (4): [🖥️ Computer Science](https://md.research.elamur.ai/free/en/shelves/computer-science/) · 16,667 works · [🤖 Artificial Intelligence](https://md.research.elamur.ai/free/en/shelves/computer-science/artificial-intelligence/) · 16,194 works · [🎭 Arts and Humanities](https://md.research.elamur.ai/free/en/shelves/arts-and-humanities/) · 16,039 works · [🗣️ Language and Linguistics](https://md.research.elamur.ai/free/en/shelves/arts-and-humanities/language-and-linguistics/) · 15,891 works

## Cite

Cite as: Denys Katerenchuk and Rivka Levitan. 2024. ”You should probably read this”: Hedge Detection in Text. arXiv:2405.13319.

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

```bibtex
@misc{katerenchuk2024you,
  title = {{”You should probably read this”: Hedge Detection in Text}},
  author = {Denys Katerenchuk and Rivka Levitan},
  year = {2024},
  doi = {10.48550/arxiv.2405.13319},
  eprint = {2405.13319},
  archivePrefix = {arXiv},
  url = {https://doi.org/10.48550/arxiv.2405.13319}
}
```

- [← previous łlinstruct: An Instruction-tuned model for English Language Proficiency Assessments](https://md.research.elamur.ai/free/en/papers/ghosh-2024-llinstruct-instruction-tuned-model-english/)
- [next → 英语科技论文摘要语步结构语料库构建研究(Research on Construction of Corpus for Move Structures in Abstracts of English Scientific Research Articles)](https://md.research.elamur.ai/free/en/papers/li-2024-research-construction-corpus-move-structures/)
- [↑ back to papers of 2024](https://md.research.elamur.ai/free/en/papers/2024/page/15/)

## References

22 references, 13 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, “[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/),” Association for Computational Linguistics, 2010.
2. George Lakoff, “Lexicography and generative grammar i: Hedges and meaning criteria,” Annals of the New York Academy of Sciences, vol. 211, no. 1, pp. 144–153, 1973. [↗](https://doi.org/10.1111/j.1749-6632.1973.tb49487.x)
3. Viola Ganter and Michael Strube, “[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, 2009, pp. 173–176.
4. Maria Georgescul, “[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/),” in Proceedings of the 14th International Conference on Computational Natural Language Learning: Shared Task, 2010, pp. 26–31.
5. Vladimir Vapnik, “The support vector method of function estimation,” in Nonlinear Modeling, pp. 55–85. Springer, 1998. [↗](https://doi.org/10.1007/978-1-4615-5703-6_3)
6. Eunsol Choi, Chenhao Tan, Lillian Lee, Cristian Danescu-Niculescu-Mizil, and Jennifer Spindel, “[Hedge detection as a lens on framing in the gmo debates: a position paper](https://md.research.elamur.ai/free/en/papers/choi-2012-hedge-detection-as-lens-framing/),” pp. 70–79, 2012. [↗](https://doi.org/10.48550/arxiv.1206.1066)
7. Roma Patel and A. Nenkova, “Modeling ambiguity in text: A corpus of legal literature,” 2019. [find](https://scholar.google.com/scholar?q=Roma+Patel+and+A%2E+Nenkova%2C+%E2%80%9CModeling+ambiguity+in+text%3A+A+corpus+of+legal+literature%2C%E2%80%9D+2019%2E)
8. Yoon Kim, “[Convolutional neural networks for sentence classification](https://md.research.elamur.ai/free/en/papers/kim-2014-convolutional-neural-networks-sentence-classification/),” pp. 1746–1751, Oct. 2014.
9. Jan K Chorowski, Dzmitry Bahdanau, Dmitriy Serdyuk, Kyunghyun Cho, and Yoshua Bengio, “[Attention-based models for speech recognition](https://md.research.elamur.ai/free/en/papers/chorowski-2015-attention-based-models-speech-recognition/),” in Advances in neural information processing systems, 2015, pp. 577–585. [↗](https://doi.org/10.48550/arxiv.1506.07503)
10. Heike Adel and Hinrich Schütze, “[Exploring different dimensions of attention for uncertainty detection](https://md.research.elamur.ai/free/en/papers/adel-2017-exploring-different-dimensions-attention-uncertainty/),” in Proceedings of the 15th Conference of the European Chapter of the Association for Computational Linguistics: Volume 1, Long Papers, 2017, pp. 22–34. [↗](https://doi.org/10.48550/arxiv.1612.06549)
11. Xu Han, Binyang Li, and Zhuoran Wang, “An attention-based neural framework for uncertainty identification on social media texts,” Tsinghua Science and Technology, vol. 1, 2020. [↗](https://doi.org/10.26599/tst.2019.9010022)
12. Ashish Vaswani, Noam Shazeer, Niki Parmar, Jakob Uszkoreit, Llion Jones, Aidan N Gomez, Łukasz Kaiser, and Illia Polosukhin, “[Attention is all you need](https://md.research.elamur.ai/free/en/papers/vaswani-2017-attention-all-need/),” in Advances in neural information processing systems, 2017, pp. 5998–6008.
13. Jacob Devlin, Ming-Wei Chang, Kenton Lee, and Kristina Toutanova, “[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/),” 2019.
14. Alec Radford, Jeffrey Wu, Rewon Child, David Luan, Dario Amodei, and Ilya Sutskever, “Language models are unsupervised multitask learners,” OpenAI Blog, vol. 1, no. 8, pp. 9, 2019. [find](https://scholar.google.com/scholar?q=Alec+Radford%2C+Jeffrey+Wu%2C+Rewon+Child%2C+David+Luan%2C+Dario+Amodei%2C+and+Ilya+Sutskever%2C+%E2%80%9CLanguage+models+are+unsupervised+multitask+learners%2C%E2%80%9D+OpenAI+Blog%2C+vol%2E+1%2C+no%2E+8%2C+pp%2E+9%2C+2019%2E)
15. Manjira Sinha, Nilesh Agarwal, and Tirthankar Dasgupta, “Relation aware attention model for uncertainty detection in text,” in Proceedings of the ACM/IEEE Joint Conference on Digital Libraries in 2020, 2020, pp. 437–440. [find](https://scholar.google.com/scholar?q=Manjira+Sinha%2C+Nilesh+Agarwal%2C+and+Tirthankar+Dasgupta%2C+%E2%80%9CRelation+aware+attention+model+for+uncertainty+detection+in+text%2C%E2%80%9D+in+Proceedings+of+the+ACM%2FIEEE+Joint+Conference+on+Digital+Libraries+in+2020)
16. Matthew Honnibal, Ines Montani, Sofie Van Lan-deghem, and Adriane Boyd, “spaCy: Industrial-strength Natural Language Processing in Python,” 2020. [find](https://scholar.google.com/scholar?q=Matthew+Honnibal%2C+Ines+Montani%2C+Sofie+Van+Lan%2Ddeghem%2C+and+Adriane+Boyd%2C+%E2%80%9CspaCy%3A+Industrial%2Dstrength+Natural+Language+Processing+in+Python%2C%E2%80%9D+2020%2E)
17. Tomas Mikolov, Ilya Sutskever, Kai Chen, Greg S Corrado, and Jeff Dean, “[Distributed representations of words and phrases and their compositionality](https://md.research.elamur.ai/free/en/papers/mikolov-2013-distributed-representations-words-phrases-their/),” Advances in neural information processing systems, vol. 26, 2013. [↗](https://doi.org/10.48550/arxiv.1310.4546)
18. Yu Gu, Robert Tinn, Hao Cheng, Michael Lucas, Naoto Usuyama, Xiaodong Liu, Tristan Naumann, Jianfeng Gao, and Hoifung Poon, “[Domain-specific language model pretraining for biomedical natural language processing](https://md.research.elamur.ai/free/en/papers/gu-2020-domain-specific-language-model-pretraining/),” arXiv preprint arXiv:2007.15779, 2020. [↗](https://arxiv.org/abs/2007.15779)
19. Piotr Bojanowski, Edouard Grave, Armand Joulin, and Tomas Mikolov, “[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, vol. 5, pp. 135–146, 2017. [↗](https://doi.org/10.1162/tacl_a_00051)
20. Sepp Hochreiter and Jürgen Schmidhuber, “Long short-term memory,” Neural computation, vol. 9, no. 8, pp. 1735–1780, 1997. [↗](https://doi.org/10.1162/neco.1997.9.8.1735)
21. Junyoung Chung, Caglar Gulcehre, Kyunghyun Cho, and Yoshua Bengio, “Empirical evaluation of gated recurrent neural networks on sequence modeling,” in NIPS 2014 Workshop on Deep Learning, December 2014, 2014. [↗](https://doi.org/10.48550/arxiv.1412.3555)
22. Yinhan Liu, Myle Ott, Naman Goyal, Jingfei Du, Mandar Joshi, Danqi Chen, Omer Levy, Mike Lewis, Luke Zettlemoyer, and Veselin Stoyanov, “[Roberta: A robustly optimized bert pretraining approach](https://md.research.elamur.ai/free/en/papers/liu-2019-roberta-robustly-optimized-bert-pretraining/),” 2019, cite arxiv:1907.11692. [↗](https://arxiv.org/abs/1907.11692)

*Updated 1 November 2026*