A language-independent neural network for event detection

By Xiaocheng Feng, Lifu Huang, Duyu Tang, Heng Ji, Bing Qin, Ting-Wei Liu. 2016

283 citations recorded by the source corpus.

Event detection remains a challenge because of the difficulty of encoding the word semantics in various contexts. Previous approaches have heavily depended on language-specific knowledge and preexisting natural language processing tools. However, not all languages have such resources and tools available compared with English language. A more promising approach is to automatically learn effective features from data, without relying on language-specific resources. In this study, we develop a language-independent neural network to capture both sequence and chunk information from specific contexts and use them to train an event detector for multiple languages without any manually encoded features. Experiments show that our approach can achieve robust, efficient and accurate results for various languages. In the ACE 2005 English event detection task, our approach achieved a 73.4% F-score with an average of 3.0% absolute improvement compared with state-of-the-art. Additionally, our experimental results are competitive for Chinese and Spanish.

Subfields: Artificial Intelligence

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In Artificial Intelligence

1. Zhiyuan Liu
2. Maosong Sun
3. Mo Yu
4. Xu Han
5. Heng Ji
6. Kang Liu
7. Shiyu Chang
8. Lifu Huang

Citations in the corpus

• Incremental Prompting: Episodic Memory Prompt for Lifelong Event Detection (2022, background): “on conventional supervised event detection (Chen et al., 2015; Nguyen et al., 2016; Feng et al., 2016; Huang and Ji, 2020; Wang et al., 2021b).”
• Query and Extract: Refining Event Extraction as Type-oriented Binary Decoding (2021, background): “4 Related Work 558 Traditional event extraction studies (McClosky 559 et al., 2011; Li et al., 2013; Chen et al., 2015; Cao 560 et al., 2015; Feng et al., 2016; Yang and Mitchell, 561 2016; Nguyen et al., 2016; Wadden et al., 2019; Lin 562 et al., 2020; Wang et al., 2021) usually detect event 563…”
• Improving Event Extraction via Multimodal Integration (2017, background): “A number of EE models have brought forth encouraging results by retrieving additional related text documents [19, 22, 37], extracting salient textual features [15, 24, 26], and adopting more advanced learning frameworks [5, 7, 16, 29].”
• Joint Event and Temporal Relation Extraction with Shared Representations and Structured Prediction (2019, background): “While other domains have shown progress on event extraction using neural methods (Nguyen and Grishman, 2015; Nguyen et al., 2016; Feng et al., 2016), recent progress in the temporal relation domain is focused more on the setting where gold events are provided.”
• Lifelong Event Detection with Knowledge Transfer (2021, methodology): “…the model on the current dataset: Although some of the methods designed explicitly for event detection (Ji and Grishman, 2008; Chen et al., 2015; Feng et al., 2016; Liu et al., 2017; Yan et al., 2019; Tong et al., 2020) may have better detection performance, this model is more flexible in that…”
• MAVEN: A Massive General Domain Event Detection Dataset (2020, background): “…2008; Gupta and Ji, 2009; Li et al., 2013; Araki and Mitamura, 2015) and advanced neural models (Chen et al., 2015; Nguyen and Gr-ishman, 2015; Nguyen et al., 2016; Feng et al., 2016; Ghaeini et al., 2016; Liu et al., 2017; Zhao et al., 2018; Chen et al., 2018; Ding et al., 2019; Yan et al., 2019).”
• The Art of Prompting: Event Detection based on Type Specific Prompts (2022, methodology): “Supervised ED: Most of the existing Event Detection studies follow a supervised learning paradigm (Ji and Grishman, 2008; Liao and Grishman, 2010; McClosky et al., 2011; Li et al., 2013; Chen et al., 2015; Cao et al., 2015; Feng et al., 2016; Yang and Mitchell, 2016; Nguyen et al., 2016; Zhang et al”
• Jointly Multiple Events Extraction via Attention-based Graph Information Aggregation (2018, methodology): “There are also a group of deep learning methods using RNNs (Nguyen et al., 2016; Sha et al., 2018; Liu et al., 2018) and CNNs (Chen et al., 2015; Feng et al., 2016; Nguyen and Grishman, 2016) capturing the associations between events.”
• Exploiting the Ground-Truth: An Adversarial Imitation Based Knowledge Distillation Approach for Event Detection (2019, methodology): “Previous studies have shown that the chunk knowledge corresponding to the sentences can provide evidence for event type disambiguation (Hong et al. 2011; Li, Ji, and Huang 2013; Feng et al. 2016; Liu et al. 2017).”
• Graph Convolutional Networks With Argument-Aware Pooling for Event Detection (2018, methodology): “2017) 12) CNN-RNN: the ensemble model of CNN and LSTM in (Feng et al. 2016)”
• Exploring Pre-trained Language Models for Event Extraction and Generation (2019, background): “…et al., 2010; Miwa et al., 2009; Liu et al., 2016; Hong et al., 2011; Li et al., 2013b) which track designed features for extraction, and the neural based ones that take advantage of neural networks to learn features automatically (Chen et al., 2015; Nguyen and Grishman, 2015; Feng et al., 2016).”