Joint Event Extraction via Recurrent Neural Networks

By Thien Huu Nguyen, Kyunghyun Cho, Ralph Grishman. 2016

649 citations recorded by the source corpus.

Event extraction is a particularly challenging problem in information extraction. The state-of-the-art models for this problem have either applied convolutional neural networks in a pipelined framework (Chen et al., 2015) or followed the joint architecture via structured prediction with rich local and global features (Li et al., 2013). The former is able to learn hidden feature representations automatically from data based on the continuous and generalized representations of words. The latter, on the other hand, is capable of mitigating the error propagation problem of the pipelined approach and exploiting the inter-dependencies between event triggers and argument roles via discrete structures. In this work, we propose to do event extraction in a joint framework with bidirectional recurrent neural networks, thereby benefiting from the advantages of the two models as well as addressing issues inherent in the existing approaches. We systematically investigate different memory features for the joint model and demonstrate that the proposed model achieves the state-of-the-art performance on the ACE 2005 dataset.

Subfields: Artificial Intelligence, Information Systems and Management, Management Science and Operations Research

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Artificial Intelligence

1. Zhiyuan Liu
2. Maosong Sun
3. Heng Ji
4. Mo Yu
5. Xu Han
6. Kang Liu
7. Jun Zhao
8. Yankai Lin

In Information Systems and Management

1. Yubo Chen
2. Kang Liu
3. Thien Huu Nguyen
4. Ralph Grishman
5. Lifu Huang
6. Jian Liu
7. Sijia Wang
8. Mo Yu

In Management Science and Operations Research

1. Kang Liu
2. Yubo Chen
3. Maosong Sun
4. Zhiyuan Liu
5. Jun Zhao
6. Thien Huu Nguyen
7. Ralph Grishman
8. Lifu Huang

Citations in the corpus

• Incremental Prompting: Episodic Memory Prompt for Lifelong Event Detection (2022, background): “Lifelong Event Detection Deep neural networks have shown state-of-the-art performance on supervised event detection (Nguyen et al., 2016; Feng et al., 2016; Zhang et al., 2017; Huang and Ji, 2020; Wang et al., 2021b).”
• A Survey of the Usages of Deep Learning for Natural Language Processing (2020, result): “[185] used an RNN-based encoder–decoder pair to identify event triggers and roles, exceeding earlier results.”
• Event Extraction as Machine Reading Comprehension (2020, methodology): “, 2015), Recurrent Neural Networks (Nguyen et al., 2016; Sha et al., 2018),”
• Leveraging Knowledge Bases in LSTMs for Improving Machine Reading (2017, neutral): “…and global features (Li et al., 2013); J OINT E NTI - TY E VENT refers to the graphical model for joint entity and event extraction (Yang and Mitchell, 2016); DMCNN is the dynamic multi-pooling CNNs in Chen et al. (2015); and JRNN is an RNN model with memory introduced by Nguyen et al. (2016).”
• Graph Convolutional Networks With Argument-Aware Pooling for Event Detection (2018, methodology): “Specifically, we repeat the model selection procedure in Table 1 of the previous section to select the best network architecture for each pooling method of comparison in {ANCHOR, OVERALL, DYNAMIC} (using the ACE 2005 development dataset).”
• Semi-supervised New Event Type Induction and Event Detection (2020, neutral): “We follow the same data split with prior work (Li et al., 2013; Nguyen et al., 2016) in which 529/30/40 newswire documents are used for training/dev/test set.”
• Exploiting the Ground-Truth: An Adversarial Imitation Based Knowledge Distillation Approach for Event Detection (2019, methodology): “…that used Convo-lution Neural Networks to do automatic feature engineering; (Feng et al. 2016) that leveraged hybrid networks for feature learning; (Nguyen, Cho, and Grishman 2016) that modeled non-continue skip-grams; (Duan, He, and Zhao 2017) that investigated document embedding for ED; (Liu et…”
• Event Extraction by Answering (Almost) Natural Questions (2020, background): “, 2015) and recurrent neural network (Nguyen et al., 2016) have been proved to help substantially for the pipeline classifiers.”
• Improving Event Extraction via Multimodal Integration (2017, methodology): “Most methods [24, 29] predict the labels based on the probabilistic distribution of confront being an Attack trigger in the training set and external gazetteers or dictionaries.”
• Text2Event: Controllable Sequence-to-Structure Generation for End-to-end Event Extraction (2021, neutral): “…al., 2019; Ma et al., 2020; Zhang et al., 2020c), 2) multi-task joint models (McClosky et al., 2011; Li et al., 2013, 2014; Yang and Mitchell, 2016; Nguyen et al., 2016; Liu et al., 2018; Zhang et al., 2019a; Zheng et al., 2019), 3) semantic structure grounding (Huang et al., 2016, 2018; Zhang et…”
• Exploiting Argument Information to Improve Event Detection via Supervised Attention Mechanisms (2017, methodology): “…learning based methods have been proposed, which tackled event detection and argu-ment extraction simultaneously (Riedel et al., 2009; Li et al., 2013; Venugopal et al., 2014; Nguyen et al., 2016), these approaches usually only make remarkable improvements to AE, but insigni(cid:12)cant to ED.”
• Exploring Pre-trained Language Models for Event Extraction and Generation (2019, methodology): “JRNN (Nguyen et al., 2016) proposes a joint framework based on bidirectional RNN for event extraction.”
• The Art of Prompting: Event Detection based on Type Specific Prompts (2022, methodology): “…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., 2017; Lin et al., 2020; Wang et al., 2021b), however, they cannot be directly applied to detect new types of…”
• Query and Extract: Refining Event Extraction as Type-oriented Binary Decoding (2021, background): “…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 triggers and arguments with multi-class…”
• 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.”
• Jointly Multiple Events Extraction via Attention-based Graph Information Aggregation (2018, methodology): “• The positional embedding vector of w i : If w c is the current word, we encode the relative distance i − c from w i to w c as a real-valued vector by looking up the randomly initialized position embedding table (Nguyen et al., 2016; Liu et al., 2017; Nguyen and Gr-ishman, 2018).”
• How Does NLP Benefit Legal System: A Summary of Legal Artificial Intelligence (2020, background): “, 2018), and event extraction (Chen et al., 2015; Nguyen et al., 2016; Nguyen and Grishman, 2018).”