Semi-supervised New Event Type Induction and Event Detection

Huang, Lifu; Ji, Heng

doi:10.18653/v1/2020.emnlp-main.53

Cited by 33 publications

(47 citation statements)

References 22 publications

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“…Semi-supervised learning (SSL) has attracted considerable attention to help achieve strong generalization by making use of both unlabeled data and labeled data [13,82,83,84,85,86,87,88,89]. Much research has used various SSL methods to help generate data or augment data for event extractions: role-identifying nouns [81], linear discrimination analysis [86], Vector Quantized Variational Autoencoder [85], multi-modal Generative Adversarial Network [89], etc. Distant Supervision Methods.…”

Section: Semi-supervised and Distant Supervision Methodsmentioning

confidence: 99%

An overview of event extraction and its applications

Liu¹,

Min²,

Huang³

2021

Preprint

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With the rapid development of information technology, online platforms have produced enormous text resources. As a particular form of Information Extraction (IE), Event Extraction (EE) has gained increasing popularity due to its ability to automatically extract events from human language. However, there are limited literature surveys on event extraction. Existing review works either spend much effort describing the details of various approaches or focus on a particular field. This study provides a comprehensive overview of the state-of-the-art event extraction methods and their applications from text, including closed-domain and open-domain event extraction. A trait of this survey is that it provides an overview in moderate complexity, avoiding involving too many details of particular approaches. This study focuses on discussing the common characters, application fields, advantages, and disadvantages of representative works, ignoring the specificities of individual approaches. Finally, we summarize the common issues, current solutions, and future research directions. We hope this work could help researchers and practitioners obtain a quick overview of recent event extraction.

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Section: Semi-supervised and Distant Supervision Methodsmentioning

confidence: 99%

An overview of event extraction and its applications

Liu¹,

Min²,

Huang³

2021

Preprint

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“…Previous work has shown that event-related structures are helpful in extracting new events (Lai et al, 2020) as well as discovering and generalizing to new event schemata (Huang et al, 2016(Huang et al, , 2018Huang and Ji, 2020). Hence we conduct event structure pre-training on a GNN as graph encoder to learn transferable event-related structure representations with recent advances in graph contrastive pre-training (Qiu et al, 2020;You et al, 2020;.…”

Section: Event Structure Pre-trainingmentioning

confidence: 99%

CLEVE: Contrastive Pre-training for Event Extraction

Wang¹,

Wang²,

Han³

et al. 2021

Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Confer

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Event extraction (EE) has considerably benefited from pre-trained language models (PLMs) by fine-tuning. However, existing pre-training methods have not involved modeling event characteristics, resulting in the developed EE models cannot take full advantage of large-scale unsupervised data. To this end, we propose CLEVE, a contrastive pre-training framework for EE to better learn event knowledge from large unsupervised data and their semantic structures (e.g. AMR) obtained with automatic parsers. CLEVE contains a text encoder to learn event semantics and a graph encoder to learn event structures respectively. Specifically, the text encoder learns event semantic representations by self-supervised contrastive learning to represent the words of the same events closer than those unrelated words; the graph encoder learns event structure representations by graph contrastive pre-training on parsed eventrelated semantic structures. The two complementary representations then work together to improve both the conventional supervised EE and the unsupervised "liberal" EE, which requires jointly extracting events and discovering event schemata without any annotated data. Experiments on ACE 2005 and MAVEN datasets show that CLEVE achieves significant improvements, especially in the challenging unsupervised setting. The source code and pre-trained checkpoints can be obtained from https://github.com/THU-KEG/CLEVE.

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“…Nguyen and , Ding et al (2019), andYan et al (2019) introduced external knowledge to as-sist neural networks to better understand each given text. Recently, many studies applied powerful pretrained language models to better comprehend contexts (Du and Cardie, 2020;Liu et al, 2020a,b;Huang and Ji, 2020).…”

Section: Related Workmentioning

confidence: 99%

Treasures Outside Contexts: Improving Event Detection via Global Statistics

Li¹,

Zhao²,

Yang³

et al. 2021

Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing

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Event detection (ED) aims at identifying event instances of specified types in given texts, which has been formalized as a sequence labeling task. As far as we know, existing neural-based ED models make decisions relying on the contextual semantic features of each word in the input text, which we find is easy to get confused by varied contexts in the test stage. To this end, we come up with the idea of introducing a set of statistical features from word-event co-occurrence frequencies in the entire training set to cooperate with the contextual features. Specifically, we propose a Semantic and Statistic-Joint Discriminative Network (S 2 -JDN) consisting of a semantic feature extractor, a statistical feature extractor, and a joint event discriminator. In experiments, S 2 -JDN effectively exceeds ten recent state-ofthe-art (SOTA) baseline methods on ACE2005 and KBP2015 benchmark datasets. Further, we perform extensive experiments to investigate the effectiveness of S 2 -JDN. * Corresponding author. 1 The event trigger is usually a single verb or nominalization, and some papers also refer to the multi-word trigger as event nugget. In this paper, we uniformly use "trigger".

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Semi-supervised New Event Type Induction and Event Detection

Cited by 33 publications

References 22 publications

An overview of event extraction and its applications

An overview of event extraction and its applications

CLEVE: Contrastive Pre-training for Event Extraction

Treasures Outside Contexts: Improving Event Detection via Global Statistics

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