---
title: "Event Detection and Domain Adaptation with — Nguyen 2015"
description: "The paper addresses event detection (ED): finding the words that trigger events in text and assigning each one an event type."
authors: ["Thien Huu Nguyen", "Ralph Grishman"]
year: 2015
venue: "Proceedings of the 53rd Annual Meeting of the Association for Computational Linguistics and the 7th International Joint Conference on Natural Language Processing (Volume 2: Short Papers)"
source: "https://aclanthology.org/P15-2060/"
doi: "10.3115/v1/p15-2060"
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/nguyen-2015-event-detection-domain-adaptation-convolutional/"
---
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# Event Detection and Domain Adaptation with Convolutional Neural Networks

**Authors:** [Thien Huu Nguyen](https://md.research.elamur.ai/free/en/authors/thien-huu-nguyen/), [Ralph Grishman](https://md.research.elamur.ai/free/en/authors/ralph-grishman/) · **Year:** 2015 · **Venue:** Proceedings of the 53rd Annual Meeting of the Association for Computational Linguistics and the 7th International Joint Conference on Natural Language Processing (Volume 2: Short Papers) · **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/P15-2060/) · [PDF](https://aclanthology.org/P15-2060.pdf) · [DOI 10.3115/v1/p15-2060](https://doi.org/10.3115/v1/p15-2060) · [OpenAlex](https://openalex.org/W2250575108)

**Citations:** 408 citations (openalex-snapshot · 2026-10-14) · [146](#cited-by) in our library, of the 140884 papers whose bibliographies we read

## Summary
The paper addresses event detection (ED): finding the words that trigger events in text and assigning each one an event type. Strong earlier systems used many hand-built features produced by parsers, name taggers and gazetteers. That approach takes expert effort for every new domain, and errors from those tools pass into the final detector. Nguyen and Grishman instead treat ED as per-token multi-class classification. Each candidate token is seen through a fixed window of surrounding context and fed to a convolutional neural network. Each word is represented by joining a pre-trained word embedding, an embedding of its relative distance to the candidate, and an optional embedding of its entity type. On ACE 2005, the plain CNN matches or beats feature-based systems that rely on external tools. Adding entity types gives state-of-the-art results among systems that use only sentence-level information. In unsupervised domain adaptation, with news as the training domain, the CNN generalizes clearly better than feature-based baselines. This supports learned representations as a way to build extractors that hold up when the text domain changes.

## Key points
- **Task and data:** ED is framed as a 34-way classification (33 ACE 2005 event subtypes plus "None").
  - The standard split is used: 529 training documents (14,849 sentences), 30 development documents and 40 newswire test documents.
- **Model and settings:**
  - Filters of widths {2, 3, 4, 5}, with 150 feature maps per width.
  - A 31-token context window.
  - 50-dimensional position and entity-type embeddings.
  - 300-dimensional word2vec initialization.
  - Dropout of 0.5 and AdaDelta updates; the embedding tables are fine-tuned during training.
- **Ablation on the development set:** position embeddings matter most. Without them, F1 drops to roughly 14–16; with them it reaches 68.5, and 70.7 when entity types are added.
- **Two variants:** CNN1 uses no external features, while CNN2 adds entity types. CNN2 beats the best sentence-level system of Li et al. (2013b) by about 1.5 F1.
- **Domain adaptation:**
  - Setup: train on news (bn+nw), test on bc, cts and wl.
  - Result: CNN2 is best on all three target domains, with significant margins of roughly 5 F1 on bc and cts.

## Interesting ideas
- **Encode relative position for token classification.** Telling the network how far each context word is from the token being classified is cheap, and here it was the difference between a useless model and a strong one. The trick should transfer to any task that classifies one token in context, such as relations, slot filling or argument roles.
- **Use only what can be learned from raw text.** A model without parser or tagger features suffers less when moved to new genres, because it does not inherit the errors those tools make on unfamiliar input. This argues for minimal-preprocessing designs in low-resource or noisy-text settings.
- **Add symbolic information as embeddings.** Entity types were fed in as learned vectors using BIO labels on entity heads, rather than as handcrafted features. This keeps the architecture uniform while still benefiting from extra annotation when it exists.
- **Report a realistic evaluation setting.** The authors noted that baselines using gold entity annotations are optimistic, and also compared against systems using automatically predicted entities. Matching the evaluation to the information actually available at deployment gives fairer comparisons.
- **Use genre splits as a domain-shift test.** Training on one set of ACE genres and testing on held-out genres, with significance testing, is a simple and reproducible robustness check. The same design could be applied to any multi-genre corpus.

*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

146 papers of our library cite it (of the 140884 whose bibliographies we read); 12 of them have a page here, 134 not yet.

- [A Language-Independent Neural Network for Event Detection](https://md.research.elamur.ai/free/en/papers/feng-2016-language-independent-neural-network-event/)
- [DEGREE: A Data-Efficient Generation-Based Event Extraction Model](https://md.research.elamur.ai/free/en/papers/hsu-2022-degree-data-efficient-generation-based/)
- [Zero-Shot Transfer Learning for Event Extraction](https://md.research.elamur.ai/free/en/papers/huang-2018-zero-shot-transfer-learning-event/)
- [Leveraging FrameNet to Improve Automatic Event Detection](https://md.research.elamur.ai/free/en/papers/liu-2016-leveraging-framenet-improve-automatic-event/)
- [Exploiting Argument Information to Improve Event Detection via Supervised Attention Mechanisms](https://md.research.elamur.ai/free/en/papers/liu-2017-exploiting-argument-information-improve-event/)
- [Joint Event Extraction via Recurrent Neural Networks](https://md.research.elamur.ai/free/en/papers/nguyen-2016-joint-event-extraction-recurrent-neural/)
- [Employing the Correspondence of Relations and Connectives to Identify Implicit Discourse Relations via Label Embeddings](https://md.research.elamur.ai/free/en/papers/nguyen-2019-employing-correspondence-relations-connectives-identify/)
- [Event Detection Using Frame-Semantic Parser](https://md.research.elamur.ai/free/en/papers/spiliopoulou-2017-event-detection-frame-semantic-parser/)
- [Graph based Neural Networks for Event Factuality Prediction using Syntactic and Semantic Structures](https://md.research.elamur.ai/free/en/papers/veyseh-2019-graph-based-neural-networks-event/)
- [MAVEN: A Massive General Domain Event Detection Dataset](https://md.research.elamur.ai/free/en/papers/wang-2020-maven-massive-general-domain-event/)
- [Joint Extraction of Events and Entities within a Document Context](https://md.research.elamur.ai/free/en/papers/yang-2016-joint-extraction-events-entities-within/)
- [Exploring Pre-trained Language Models for Event Extraction and Generation](https://md.research.elamur.ai/free/en/papers/yang-2019-exploring-pre-trained-language-models/)

## Cite

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

```bibtex
@inproceedings{nguyen2015event,
  title = {{Event Detection and Domain Adaptation with Convolutional Neural Networks}},
  author = {Nguyen, Thien Huu and Grishman, Ralph},
  booktitle = {Proceedings of the 53rd Annual Meeting of the Association for Computational Linguistics and the 7th International Joint Conference on Natural Language Processing (Volume 2: Short Papers)},
  year = {2015},
  doi = {10.3115/v1/p15-2060},
  url = {https://doi.org/10.3115/v1/p15-2060}
}
```

- [← previous Evaluation methods for unsupervised word embeddings](https://md.research.elamur.ai/free/en/papers/schnabel-2015-evaluation-methods-unsupervised-word-embeddings/)
- [next → Event Extraction via Dynamic Multi-Pooling Convolutional Neural Networks](https://md.research.elamur.ai/free/en/papers/chen-2015-event-extraction-dynamic-multi-pooling/)
- [↑ back to papers of 2015](https://md.research.elamur.ai/free/en/papers/2015/)

## References

32 references, 13 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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