---
title: "Joint Extraction of Events and Entities within a — Yang 2016"
description: "Yang and Mitchell address two weaknesses of typical event extraction systems."
authors: ["Bishan Yang", "Tom M. Mitchell"]
year: 2016
venue: "Proceedings of the 2016 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies"
source: "https://aclanthology.org/N16-1033/"
doi: "10.18653/v1/n16-1033"
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/yang-2016-joint-extraction-events-entities-within/"
---
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# Joint Extraction of Events and Entities within a Document Context

**Authors:** [Bishan Yang](https://md.research.elamur.ai/free/en/authors/bishan-yang/), [Tom M. Mitchell](https://md.research.elamur.ai/free/en/authors/tom-m-mitchell/) · **Year:** 2016 · **Venue:** Proceedings of the 2016 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies · **Licence:** [CC BY 4.0 (ACL Anthology)](https://creativecommons.org/licenses/by/4.0/)

**Links:** [Publisher's page](https://aclanthology.org/N16-1033/) · [PDF](https://aclanthology.org/N16-1033.pdf) · [DOI 10.18653/v1/n16-1033](https://doi.org/10.18653/v1/n16-1033) · [OpenAlex](https://openalex.org/W2964206023)

**Citations:** 220 citations (openalex-snapshot · 2026-10-14) · [97](#cited-by) in our library, of the 140884 papers whose bibliographies we read

## Summary
Yang and Mitchell address two weaknesses of typical event extraction systems. First, entities are usually tagged in a separate earlier step, so tagging mistakes carry over into event argument extraction. Second, events are usually predicted one sentence at a time, without using the rest of the document.

Their approach splits the task into three learnable parts:
- a structured model of a single event, linking the trigger's type, each candidate argument's role and that argument's entity type;
- a model of which event types tend to co-occur across a document;
- an entity extraction model.

These components are then combined in one joint optimization that infers triggers, semantic roles and entities for the whole document at once.

Experiments on the ACE corpus show clear gains over state-of-the-art event extractors and over a strong entity extraction baseline. The work shows that modelling entities together with events, and using document-level context, makes predictions more context-aware than sentence-level pipelines. The code is publicly released.

## Key points
- The setup follows the ACE definitions:
  - 33 event subtypes;
  - 35 argument roles reduced to 28, by merging 8 sparse time-related roles into one;
  - 9 categories of entities: VALUE and TIME, together with seven others (WEA, VEH, FAC, LOC, GPE, ORG and PER).
- The within-event model is a factor graph over trigger type, argument roles and entity types:
  - it is trained by L2-regularized maximum likelihood with L-BFGS;
  - exact marginals are computed with sum-product.
- Invalid label combinations get zero probability. Examples are a role that the event type does not allow, or an entity type that does not fit the role. This brings the complexity down to O(M×(k1T+k2N)), with k1=6 and k2=3 in ACE.
- In the error analysis, most argument errors are missed arguments. Only 4.1% are wrong role labels. Remaining failures come from lexical sparsity, long-distance dependencies, and figurative language.
- Code: github.com/bishanyang/EventEntityExtractor.

## Interesting ideas
- **Break a hard joint problem into separately trained parts, then combine them at inference.** This keeps training tractable while still letting the decisions inform each other. The pattern could transfer to other multi-layer annotation tasks, such as NER combined with relation extraction.
- **Use schema constraints as hard zeros in the model.** Ruling out incompatible type and role combinations both adds linguistic knowledge and cuts computational cost. It is a cheap trick wherever an annotation scheme defines which label pairs are allowed.
- **Predict entity types inside the event model instead of taking them from an upstream tagger.** This avoids error propagation from the pipeline. Event evidence can also correct entity labels; for example, a place role suggests a location rather than a person.
- **Model document-level co-occurrence of event types.** For instance, attacks tend to appear alongside injuries and deaths. Such patterns help interpret sentences that say little about the event on their own. A similar signal could support extraction from news-style texts in other languages.
- **Split errors into missing, spurious and misclassified.** This breakdown shows that recall, not label confusion, is the main bottleneck. That kind of diagnosis helps decide where to put further effort.

*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

97 papers of our library cite it (of the 140884 whose bibliographies we read); 7 of them have a page here, 90 not yet.

- [DEGREE: A Data-Efficient Generation-Based Event Extraction Model](https://md.research.elamur.ai/free/en/papers/hsu-2022-degree-data-efficient-generation-based/)
- [Event Extraction as Multi-turn Question Answering](https://md.research.elamur.ai/free/en/papers/li-2020-event-extraction-as-multi-turn/)
- [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/)
- [Jointly Multiple Events Extraction via Attention-based Graph Information Aggregation](https://md.research.elamur.ai/free/en/papers/liu-2018-jointly-multiple-events-extraction-attention/)
- [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/)
- [Entity, Relation, and Event Extraction with Contextualized Span Representations](https://md.research.elamur.ai/free/en/papers/wadden-2019-entity-relation-event-extraction-contextualized/)

## Cite

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

```bibtex
@inproceedings{yang2016joint,
  title = {{Joint Extraction of Events and Entities within a Document Context}},
  author = {Yang, Bishan and Mitchell, Tom M.},
  booktitle = {Proceedings of the 2016 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies},
  year = {2016},
  doi = {10.18653/v1/n16-1033},
  url = {https://doi.org/10.18653/v1/n16-1033}
}
```

- [← previous Joint Event Extraction via Recurrent Neural Networks](https://md.research.elamur.ai/free/en/papers/nguyen-2016-joint-event-extraction-recurrent-neural/)
- [next → Joint Inference for Event Coreference Resolution](https://md.research.elamur.ai/free/en/papers/lu-2016-joint-inference-event-coreference-resolution/)
- [↑ back to papers of 2016](https://md.research.elamur.ai/free/en/papers/2016/page/2/)

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