# Nugget Proposal Networks for Chinese Event Detection

arXiv: https://arxiv.org/abs/1805.00249 · HTML: https://mlsanity.com/paper/1805.00249/

## Key facts

- Cited by 11 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 1 May 2018.

## Built on by (most cited first, 11 of 11)

- [Unified Structure Generation for Universal Information Extraction](https://mlsanity.com/paper/2203.12277/) (2022)
- [Text2Event: Controllable Sequence-to-Structure Generation for End-to-end Event Extraction](https://mlsanity.com/paper/2106.09232/) (2021)
- [Lattice-BERT: Leveraging Multi-Granularity Representations in Chinese Pre-trained Language Models](https://mlsanity.com/paper/2104.07204/) (2021)
- [Title2Event: Benchmarking Open Event Extraction with a Large-scale Chinese Title Dataset](https://mlsanity.com/paper/2211.00869/) (2022)
- [A Survey on Deep Learning Event Extraction: Approaches and Applications](https://mlsanity.com/paper/2107.02126/) (2021)
- [Honey or Poison? Solving the Trigger Curse in Few-shot Event Detection via Causal Intervention](https://mlsanity.com/paper/2109.05747/) (2021)
- [Behind the Scenes: An Exploration of Trigger Biases Problem in Few-Shot Event Classification](https://mlsanity.com/paper/2108.12844/) (2021)
- [Universal Information Extraction with Meta-Pretrained Self-Retrieval](https://mlsanity.com/paper/2306.10444/) (2023)
- [Type Information Utilized Event Detection via Multi-Channel GNNs in Electrical Power Systems](https://mlsanity.com/paper/2211.08168/) (2022)
- [Event Argument Extraction using Causal Knowledge Structures](https://mlsanity.com/paper/2105.00477/) (2021)
- [MUSIED: A Benchmark for Event Detection from Multi-Source Heterogeneous Informal Texts](https://mlsanity.com/paper/2211.13896/) (2022)

## Abstract (arXiv)

Neural network based models commonly regard event detection as a word-wise classification task, which suffer from the mismatch problem between words and event triggers, especially in languages without natural word delimiters such as Chinese. In this paper, we propose Nugget Proposal Networks (NPNs), which can solve the word-trigger mismatch problem by directly proposing entire trigger nuggets centered at each character regardless of word boundaries. Specifically, NPNs perform event detection in a character-wise paradigm, where a hybrid representation for each character is first learned to capture both structural and semantic information from both characters and words. Then based on learned representations, trigger nuggets are proposed and categorized by exploiting character compositional structures of Chinese event triggers. Experiments on both ACE2005 and TAC KBP 2017 datasets show that NPNs significantly outperform the state-of-the-art methods.