Event Detection and Domain Adaptation with Convolutional Neural Networks

By Thien Huu Nguyen, Ralph Grishman. 2015

427 citations recorded by the source corpus.

We study the event detection problem using convolutional neural networks (CNNs) that overcome the two fundamental limitations of the traditional feature-based approaches to this task: complicated feature engineering for rich feature sets and error propagation from the preceding stages which generate these features. The experimental results show that the CNNs outperform the best reported feature-based systems in the general setting as well as the domain adaptation setting without resorting to extensive external resources.

Subfields: Artificial Intelligence

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In Artificial Intelligence

1. Zhiyuan Liu
2. Mohit Bansal
3. Maosong Sun
4. Yoav Goldberg
5. William Yang Wang
6. Kang Liu
7. Xu Han
8. Jun Zhao

Citations in the corpus

• Natural Language Processing for EHR-Based Computational Phenotyping (2018, background): “tion [146], semantic analysis [147], [148], information retrieval [149], [150], entity recognition [151], [152], relation extraction [153], [154], [155], [156], and event detection [157], [158].”
• A Primer on Neural Network Models for Natural Language Processing (2015, methodology): “…between entities (Zeng, Liu, Lai, Zhou, & Zhao, 2014; dos Santos, Xiang, & Zhou, 2015), event detection (Chen, Xu, Liu, Zeng, & Zhao, 2015; Nguyen & Grishman, 2015), paraphrase identification (Yin & Schütze, 2015) semantic role labeling (Collobert, Weston, Bottou, Karlen, Kavukcuoglu, &…”
• Event Extraction by Answering (Almost) Natural Questions (2020, background): “In recent years, continuous representations from convolutional neural network (Nguyen and Grishman, 2015; Chen et al., 2015) and recurrent neural network (Nguyen et al.”
• 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).”
• Exploiting Argument Information to Improve Event Detection via Supervised Attention Mechanisms (2017, methodology): “Following existing work (Li et al., 2013; Chen et al., 2015; Nguyen and Grishman, 2015), we exploit the annotated entity information in our ED system.”
• MAVEN: A Massive General Domain Event Detection Dataset (2020, background): “, 2013; Araki and Mitamura, 2015) and advanced neural models (Chen et al., 2015; Nguyen and Grishman, 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).”
• Toward Mention Detection Robustness with Recurrent Neural Networks (2016, background): “…that captures the general syntactic and semantic properties of words (Collobert and Weston, 2008; Mnih and Hinton, 2008; Turian et al., 2010), they can alleviate the lexical sparsity, induce more general feature representation, thus generalizing well across domains (Nguyen and Grishman, 2015b).”
• Combining Neural Networks and Log-linear Models to Improve Relation Extraction (2015, methodology): “Following the common practice of domain adaptation research on this dataset (Plank and Moschitti, 2013; Nguyen and Grishman, 2014; Nguyen et al., 2015c; Gormley et al., 2015), we usenews (the union of bn andnw) as the training data, a half ofbc as the development set and the remainder (cts, wl and…”
• Exploiting the Ground-Truth: An Adversarial Imitation Based Knowledge Distillation Approach for Event Detection (2019, methodology): “Representation-based approaches include (Chen et al. 2015; Nguyen and Grishman 2015) 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…”
• DSGAN: Generative Adversarial Training for Distant Supervision Relation Extraction (2018, methodology): “For relation extraction, the input information consists of the sentences and entity pairs; thus, as the common setting (Zeng et al., 2014; Nguyen and Grishman, 2015), we use both word embedding and position embedding to convert input instances into continuous real-valued vectors.”
• 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).”
• 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.”
• Incremental Event Detection via Knowledge Consolidation Networks (2020, methodology): “The proposed models can be divided into feature-based methods (Ahn, 2006; Ji and Grishman, 2008; Liao and Grishman, 2010; Li et al., 2013; Li and Ji, 2014) and neural network-based methods (Chen et al., 2015; Nguyen and Grishman, 2015; Liu et al., 2017, 2018a,b; Lu et al., 2019; Ding et al., 2019).”
• Joint Event Extraction via Recurrent Neural Networks (2016, methodology): “Note that we do not use the relative position features, unlike the prior work on neural networks for EE (Nguyen and Grishman, 2015b; Chen et al., 2015).”