Exploiting a More Global Context for Event Detection Through Bootstrapping

Kodelja, Dorian; Besançon, Romaric; Ferret, Olivier

doi:10.1007/978-3-030-15712-8_51

Cited by 9 publications

(7 citation statements)

References 9 publications

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“…Fei et al (2020) integrated RNN and CRFs as a unified framework, namely RecurCRFs, for biomedical event trigger detection. Kodelja et al (2019) integrated the global representation of contexts beyond the sentence level into the CNN model. Since the CNN model can only model short‐range dependencies within its kernel window, pooling operations (such as max pooling) cause a lot of information loss.…”

Section: Preliminarymentioning

confidence: 99%

Judicial intelligent assistant system: Extracting events from Chinese divorce cases to detect disputes for the judge

Zhang,

Li,

Sheng

et al. 2024

Expert Systems

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In the formal procedure of Chinese civil cases, the textual materials provided by different parties describe the development process of the cases. It is a difficult but necessary task to extract the key information for the cases from these textual materials and to clarify the dispute focus of related parties. Currently, officers read the materials manually and use methods, such as keyword searching and regular matching, to get the target information. These approaches are time‐consuming and heavily depend on prior knowledge and the carefulness of the officers. To assist the officers in enhancing working efficiency and accuracy, we conduct a case study of detecting disputes from Chinese divorce cases based on proposing a Two‐Round‐Labeling (TRL) event extracting technique in this article. We implement the Judicial Intelligent Assistant (JIA) system according to the proposed approach to (1) automatically extract focus events from divorce case materials, (2) align events by identifying co‐reference among them, and (3) detect conflicts among events brought by the plaintiff and the defendant. With the JIA system, it is convenient for judges to determine the disputed issues in Chinese divorce cases. Experimental results demonstrate that the proposed approach and system can obtain the focus of Chinese divorce cases and detect conflicts more effectively and efficiently compared with the existing method.

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Section: Preliminarymentioning

confidence: 99%

Judicial intelligent assistant system: Extracting events from Chinese divorce cases to detect disputes for the judge

Zhang,

Li,

Sheng

et al. 2024

Expert Systems

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“…Deep learning technology has been deeply researched by using neural network to solve classification tasks in recent years. Kodelja et al [9] use Convolutional Neural Network (CNN) to help capture representation of contexts beyond the sentence level, and use these to reduce inter-sentence ambiguities. Sha et al [10] propose a dependency bridge Recurrent Neural Network (dbRNN) for event extraction, which enables each word to carry syntactically related information, and propose that much better performance is brought applying sequence structure and tree structure in RNN.…”

Section: Machine Learning Event Extractionmentioning

confidence: 99%

Research on domain event extraction methods based on pre-trained model

Meng

Zhang²,

Liu

et al. 2023

International Conference on Signal Processing, Computer Networks, and Communications (SPCNC 2022)

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One of the important research directions in information extraction is event extraction (EE). It aims at recognizing event types and event arguments from natural language texts, which is an important technical basis for artificial intelligence application that serves for the information work in business, science and technology, military and other fields. Currently, data annotation samples of the relevant field based on encyclopedia and news data are relatively rare and lack relevant datasets. Therefore, there are only a few public research on the event extraction in the relevant intelligence field. By integrating universal information extraction, the event extraction method which use the pre-trained model for the relevant intelligence field can handle the problem of rare data annotation samples for the event extraction in the relevant field. By expanding training samples automatically, the context information of encyclopedia and news data is learnt effectively to extract relevant events from encyclopedia and news data. Compared with other event extraction methods, using precision, recall and F1 value as assessment indicators, in event recognition tasks, the value of F1 enhances 1.26%, and in argument recognition tasks, the value of F1 enhances 1.58%. The method can significantly boost the extraction performance in small samples shown in the experimental results.

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“…Some other improvements of the typical CNN model have also been proposed [151]- [154]. For example, Burel et al [152] designed a semantically enhanced deep-learning model, called Dual-CNN, which adds a semantic layer in a typical CNN to capture contextual information.…”

Section: A Convolutional Neural Networkmentioning

confidence: 99%

“…The PMCNN also utilizes dependency-based embedding for word semantic and syntactic representations and employs a rectified linear unit as a nonlinear function. Kodelja et al [154] built the representation of global contexts following a bootstrapping approach, and integrated the representation into a CNN model for event extraction.…”

Section: A Convolutional Neural Networkmentioning

confidence: 99%

A Survey of Event Extraction From Text

2019

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Numerous important events happen everyday and everywhere but are reported in different media sources with different narrative styles. How to detect whether real-world events have been reported in articles and posts is one of the main tasks of event extraction. Other tasks include extracting event arguments and identifying their roles, as well as clustering and tracking similar events from different texts. As one of the most important research themes in natural language processing and understanding, event extraction has a wide range of applications in diverse domains and has been intensively researched for decades. This article provides a comprehensive yet up-to-date survey for event extraction from text. We not only summarize the task definitions, data sources and performance evaluations for event extraction, but also provide a taxonomy for its solution approaches. In each solution group, we provide detailed analysis for the most representative methods, especially their origins, basics, strengths and weaknesses. Last, we also present our envisions about future research directions. INDEX TERMS Event extraction, event extraction tasks, event corpus, natural language processing.

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Exploiting a More Global Context for Event Detection Through Bootstrapping

Cited by 9 publications

References 9 publications

Judicial intelligent assistant system: Extracting events from Chinese divorce cases to detect disputes for the judge

Judicial intelligent assistant system: Extracting events from Chinese divorce cases to detect disputes for the judge

Research on domain event extraction methods based on pre-trained model

A Survey of Event Extraction From Text

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