---
title: "Jointly Multiple Events Extraction via — Liu 2018"
description: "Event extraction involves finding trigger words, assigning event types, and identifying arguments with their roles."
authors: ["Xiao Liu", "Zhunchen Luo", "Heyan Huang"]
year: 2018
venue: "Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing"
source: "https://aclanthology.org/D18-1156/"
doi: "10.18653/v1/d18-1156"
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/liu-2018-jointly-multiple-events-extraction-attention/"
---
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# Jointly Multiple Events Extraction via Attention-based Graph Information Aggregation

**Authors:** [Xiao Liu](https://md.research.elamur.ai/free/en/authors/xiao-liu/), Zhunchen Luo, [Heyan Huang](https://md.research.elamur.ai/free/en/authors/heyan-huang/) · **Year:** 2018 · **Venue:** Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing · **Licence:** [CC BY 4.0 (ACL Anthology)](https://creativecommons.org/licenses/by/4.0/)

**Links:** [Publisher's page](https://aclanthology.org/D18-1156/) · [PDF](https://aclanthology.org/D18-1156.pdf) · [DOI 10.18653/v1/d18-1156](https://doi.org/10.18653/v1/d18-1156) · [OpenAlex](https://openalex.org/W2891553865)

**Citations:** 397 citations (openalex-snapshot · 2026-10-14) · [112](#cited-by) in our library, of the 140884 papers whose bibliographies we read

## Summary
Event extraction involves finding trigger words, assigning event types, and identifying arguments with their roles. The task gets harder when one sentence contains several related events, because the type of one event often depends on the others. RNN- and CRF-style sequential models also struggle to connect triggers that are far apart. The authors propose JMEE, a joint framework that predicts triggers and arguments together. Tokens are represented with word, POS, positional and entity-type embeddings and then passed through a BiLSTM. A graph convolution network runs over dependency arcs from the Stanford Parser, which act as shortcuts that shorten paths between triggers. Edge types are reduced to three (forward, reverse, self-loop). Gates downweight uninformative or noisy edges, and highway units regulate propagation across layers. A self-attention step aggregates information across trigger candidates, and a biased loss compensates for label sparsity. On ACE 2005 with gold entities, JMEE outperforms prior systems by about two F1 points in trigger classification and argument role labeling. This shows that syntax-aware graph modeling helps with multi-event sentences.

## Key points
- About 26.2% of ACE 2005 sentences (1042 of 3978) contain multiple events, which motivates the work.
- The shortcut arcs reduce the distance between triggers. In the paper's example, two triggers are six steps apart in word order but only three along dependency arcs.
- The architecture stacks three GCN layers equipped with edge gating and highway units, along with a single-layer Bi-LSTM of 220 hidden units and a 300-unit self-attention module, and sentences are capped at 50 tokens.
- JMEE's F1 scores on ACE 2005:
  - Trigger identification: 75.9
  - Trigger classification: 73.7 (previous best, dbRNN: 71.9)
  - Argument identification: 68.4
  - Argument role labeling: 60.3 (dbRNN: 58.7)
- The baselines are Cross-Event, JointBeam, DMCNN, PSL, JRNN and dbRNN. A separate evaluation compares single-event (1/1) and multi-event (1/N) test sentences.

*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/)

## Cited by

Cited by

112 papers of our library cite it (of the 140884 whose bibliographies we read); 5 of them have a page here, 107 not yet.

- [StanceCrafters at StanceEval2024: Multi-task Stance Detection using BERT Ensemble with Attention Based Aggregation](https://md.research.elamur.ai/free/en/papers/hasanaath-2024-stancecrafters-stanceeval2024-multi-task-stance/)
- [Event Extraction as Multi-turn Question Answering](https://md.research.elamur.ai/free/en/papers/li-2020-event-extraction-as-multi-turn/)
- [Event Extraction as Machine Reading Comprehension](https://md.research.elamur.ai/free/en/papers/liu-2020-event-extraction-as-machine-reading/)
- [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/)
- [MAVEN: A Massive General Domain Event Detection Dataset](https://md.research.elamur.ai/free/en/papers/wang-2020-maven-massive-general-domain-event/)

## Cite

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

```bibtex
@inproceedings{liu2018jointly,
  title = {{Jointly Multiple Events Extraction via Attention-based Graph Information Aggregation}},
  author = {Liu, Xiao and Luo, Zhunchen and Huang, Heyan},
  booktitle = {Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing},
  year = {2018},
  doi = {10.18653/v1/d18-1156},
  url = {https://doi.org/10.18653/v1/d18-1156}
}
```

- [← previous Iterative Back-Translation for Neural Machine Translation](https://md.research.elamur.ai/free/en/papers/hoang-2018-iterative-back-translation-neural-machine/)
- [next → Jointly Predicting Predicates and Arguments in Neural Semantic Role Labeling](https://md.research.elamur.ai/free/en/papers/he-2018-jointly-predicting-predicates-arguments-neural/)
- [↑ back to papers of 2018](https://md.research.elamur.ai/free/en/papers/2018/page/2/)

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