---
title: "Entity, Relation, and Event Extraction with — Wadden 2019"
description: "Extracting entities, relations and events often requires information beyond a single sentence."
authors: ["David Wadden", "Ulme Wennberg", "Yi Luan", "Hannaneh Hajishirzi"]
year: 2019
venue: "Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP)"
source: "https://aclanthology.org/D19-1585/"
doi: "10.18653/v1/d19-1585"
arxiv: "1909.03546"
licence: "CC BY 4.0 (ACL Anthology)"
licence_url: "https://creativecommons.org/licenses/by/4.0/"
summary_licence: "Elamur Cosmic License"
canonical: "https://research.elamur.ai/free/en/papers/wadden-2019-entity-relation-event-extraction-contextualized/"
---
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# Entity, Relation, and Event Extraction with Contextualized Span Representations

**Authors:** [David Wadden](https://md.research.elamur.ai/free/en/authors/david-wadden/), Ulme Wennberg, [Yi Luan](https://md.research.elamur.ai/free/en/authors/yi-luan/), [Hannaneh Hajishirzi](https://md.research.elamur.ai/free/en/authors/hannaneh-hajishirzi/) · **Year:** 2019 · **Venue:** Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP) · **Licence:** [CC BY 4.0 (ACL Anthology)](https://creativecommons.org/licenses/by/4.0/)

**Links:** [Publisher's page](https://aclanthology.org/D19-1585/) · [PDF](https://aclanthology.org/D19-1585.pdf) · [DOI 10.18653/v1/d19-1585](https://doi.org/10.18653/v1/d19-1585) · [arXiv 1909.03546](https://arxiv.org/abs/1909.03546) · [OpenAlex](https://openalex.org/W2972140869)

**Citations:** 564 citations (openalex-snapshot · 2026-10-14) · [254](#cited-by) in our library, of the 140673 papers whose bibliographies we read

## Summary
Extracting entities, relations and events often requires information beyond a single sentence. Coreference links, for example, can help decide what type a hard-to-label mention should get. This paper introduces DyGIE++, a multi-task information extraction system that extends the span-based DyGIE model. DyGIE++ enumerates candidate spans and encodes them with BERT, feeding each sentence together with neighbouring sentences. It then refines the span vectors through dynamically built graphs (coreference, relation and event propagation) before task-specific classifiers score them. Coreference serves only as an auxiliary task. The authors evaluate on four corpora from different domains: ACE05, SciERC, GENIA and WLPC. They compare a fine-tuned BERT setup with a cheaper variant that trains an LSTM over frozen BERT embeddings.

DyGIE++ sets new best results on nearly every task and subtask. The analysis shows a division of labour: BERT handles context within a sentence and in nearby sentences, while graph propagation captures longer-range links. The graph updates help most in specialized scientific domains. The code is openly released and is designed to be adapted to new tasks and datasets.

## Key points
- DyGIE++ beats prior state of the art on all subtasks except one (ACE05 event argument identification: 55.4 vs 57.2 F1). Relative error reductions range from 0.2% to 27.9%. The largest single gain is SciERC relations, which rise from 41.6 to 48.4 F1.
- A BERT input window of 3 sentences gave the best scores on all relation and event extraction tasks.
- Fine-tuning BERT gives modest gains for NER (+0.32 F1 on average) and relations (+1.0 F1) but lowers event extraction scores by 1.6 F1. The BERT+LSTM variant keeps gradients for about 15M parameters, compared with 100M in BERT BASE, so it is lighter and easier to train.
- Replacing BERT with SciBERT improves the scientific datasets: SciERC entities go from 69.8 to 72.0 F1, SciERC relations from 41.9 to 45.3, and GENIA from 78.4 to 79.5.
- On the SciERC dev set, coreference propagation fixed 44 entity errors and introduced 21. Most fixes removed overly specific labels, such as replacing Method with Other Scientific Term. Code is available at github.com/dwadden/dygiepp.

*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: [Natural language processing](https://md.research.elamur.ai/free/en/shelves/nlp/)

Topics: [Coreference resolution](https://md.research.elamur.ai/free/en/topics/coreference-resolution/)

## Cited by

Cited by

254 papers of our library cite it (of the 140673 whose bibliographies we read); 10 of them have a page here, 244 not yet.

- [Incorporating Syntax and Semantics in Coreference Resolution with Heterogeneous Graph Attention Network](https://md.research.elamur.ai/free/en/papers/jiang-2021-incorporating-syntax-semantics-coreference-resolution/)
- [Document-Level Event Argument Extraction by Conditional Generation](https://md.research.elamur.ai/free/en/papers/li-2021-document-level-event-argument-extraction/)
- [A Joint Neural Model for Information Extraction with Global Features](https://md.research.elamur.ai/free/en/papers/lin-2020-joint-neural-model-information-extraction/)
- [Conundrums in Event Coreference Resolution: Making Sense of the State of the Art](https://md.research.elamur.ai/free/en/papers/lu-2021-conundrums-event-coreference-resolution-making/)
- [Text2Event: Controllable Sequence-to-Structure Generation for End-to-end Event Extraction](https://md.research.elamur.ai/free/en/papers/lu-2021-text2event-controllable-sequence-structure-generation/)
- [Contrastively Pre-trained Event Embeddings with Schema-free LLM Annotations](https://md.research.elamur.ai/free/en/papers/mtumbuka-2026-contrastively-pre-trained-event-embeddings/)
- [Investigating Pretrained Language Models for Graph-to-Text Generation](https://md.research.elamur.ai/free/en/papers/ribeiro-2021-investigating-pretrained-language-models-graph/)
- [GLM-130B: An Open Bilingual Pre-trained Model](https://md.research.elamur.ai/free/en/papers/zeng-2022-glm-130b-open-bilingual-pre/)
- [Revisiting Few-sample BERT Fine-tuning](https://md.research.elamur.ai/free/en/papers/zhang-2020-revisiting-few-sample-bert-fine/)
- [A Frustratingly Easy Approach for Entity and Relation Extraction](https://md.research.elamur.ai/free/en/papers/zhong-2020-frustratingly-easy-approach-entity-relation/)

## Cite

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

```bibtex
@inproceedings{wadden2019entity,
  title = {{Entity, Relation, and Event Extraction with Contextualized Span Representations}},
  author = {Wadden, David and Wennberg, Ulme and Luan, Yi and Hajishirzi, Hannaneh},
  booktitle = {Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP)},
  year = {2019},
  doi = {10.18653/v1/d19-1585},
  eprint = {1909.03546},
  archivePrefix = {arXiv},
  url = {https://doi.org/10.18653/v1/d19-1585}
}
```

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## References

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