Modeling Document-Level Context for Event Detection via Important Context Selection

Abstract Document-level financial event extraction (DFEE) is the task of detecting event and extracting the corresponding event arguments in financial documents, which plays an important role in information extraction in the financial domain. This task is challenging as the financial documents are generally long text and event arguments of one event may be scattered in different sentences. To address this issue, we propose a novel Prior Information Enhanced Extraction framework (PIEE) for DFEE, leveraging prior information from both event types and pre-trained language models. Specifically, PIEE consists of three components: event detection, event argument extraction, and event table filling. In event detection, we identify the event type. Then, the event type is explicitly used for event argument extraction. Meanwhile, the implicit information within language models also provides considerable cues for event arguments localization. Finally, all the event arguments are filled in an event table by a set of predefined heuristic rules. To demonstrate the effectiveness of our proposed framework, we participate the share task of CCKS2020 Task5-2: Document-level Event Arguments Extraction. On both Leaderboard A and Leaderboard B, PIEE takes the first place and significantly outperforms the other systems.

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Using an event-detection measure to assess drivers' attention and situation awareness

PsycEXTRA Dataset ◽

10.1037/e577512012-025 ◽

2005 ◽

Author(s):

Leo Gugerty ◽

Melissa Falzetta

Keyword(s):

Situation Awareness ◽

Event Detection

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Military situation awareness: Facilitating critical event detection in chat

PsycEXTRA Dataset ◽

10.1037/e577582012-003 ◽

2006 ◽

Author(s):

Jean M. Catanzaro ◽

Matthew R. Risser ◽

John W. Gwynne ◽

Daniel I. Manes

Keyword(s):

Situation Awareness ◽

Event Detection ◽

Critical Event

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Framework for rare event detection using Artificial Neural Network based context free grammar

Journal of Intelligent & Fuzzy Systems ◽

10.3233/jifs-189164 ◽

2020 ◽

Vol 39 (6) ◽

pp. 8463-8475

Author(s):

Palanivel Srinivasan ◽

Manivannan Doraipandian

Keyword(s):

Neural Network ◽

Artificial Neural Network ◽

Event Detection ◽

Performance Metrics ◽

Rare Events ◽

Rare Event ◽

Video Stream ◽

Context Free Grammar ◽

Artificial Neural ◽

Context Free

Rare event detections are performed using spatial domain and frequency domain-based procedures. Omnipresent surveillance camera footages are increasing exponentially due course the time. Monitoring all the events manually is an insignificant and more time-consuming process. Therefore, an automated rare event detection contrivance is required to make this process manageable. In this work, a Context-Free Grammar (CFG) is developed for detecting rare events from a video stream and Artificial Neural Network (ANN) is used to train CFG. A set of dedicated algorithms are used to perform frame split process, edge detection, background subtraction and convert the processed data into CFG. The developed CFG is converted into nodes and edges to form a graph. The graph is given to the input layer of an ANN to classify normal and rare event classes. Graph derived from CFG using input video stream is used to train ANN Further the performance of developed Artificial Neural Network Based Context-Free Grammar – Rare Event Detection (ACFG-RED) is compared with other existing techniques and performance metrics such as accuracy, precision, sensitivity, recall, average processing time and average processing power are used for performance estimation and analyzed. Better performance metrics values have been observed for the ANN-CFG model compared with other techniques. The developed model will provide a better solution in detecting rare events using video streams.

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A fast event detection algorithm for residential loads within normal and disturbed operating conditions

European Journal of Electrical Engineering ◽

10.3166/ejee.18.95-116 ◽

2016 ◽

Vol 18 (1-2) ◽

pp. 95-116 ◽

Cited By ~ 1

Author(s):

Faten MOUELHI

Keyword(s):

Event Detection ◽

Detection Algorithm ◽

Operating Conditions

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Tweets analysis for event detection

Ingénierie des systèmes d information ◽

10.3166/isi.21.1.61-80 ◽

2016 ◽

Vol 21 (1) ◽

pp. 61-80

Author(s):

Soumaya Cherichi ◽

Rim Faiz

Keyword(s):

Event Detection

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Saliency detection in deep learning era: trends of development

Information and Control Systems ◽

10.31799/1684-8853-2019-3-10-36 ◽

2019 ◽

pp. 10-36 ◽

Cited By ~ 2

Author(s):

M. N. Favorskaya ◽

L. C. Jain

Keyword(s):

Deep Learning ◽

Object Detection ◽

Event Detection ◽

Visual Analysis ◽

Saliency Detection ◽

Salient Object Detection ◽

Public Image ◽

Detection Methods ◽

Salient Object ◽

Salient Event

Introduction:Saliency detection is a fundamental task of computer vision. Its ultimate aim is to localize the objects of interest that grab human visual attention with respect to the rest of the image. A great variety of saliency models based on different approaches was developed since 1990s. In recent years, the saliency detection has become one of actively studied topic in the theory of Convolutional Neural Network (CNN). Many original decisions using CNNs were proposed for salient object detection and, even, event detection.Purpose:A detailed survey of saliency detection methods in deep learning era allows to understand the current possibilities of CNN approach for visual analysis conducted by the human eyes’ tracking and digital image processing.Results:A survey reflects the recent advances in saliency detection using CNNs. Different models available in literature, such as static and dynamic 2D CNNs for salient object detection and 3D CNNs for salient event detection are discussed in the chronological order. It is worth noting that automatic salient event detection in durable videos became possible using the recently appeared 3D CNN combining with 2D CNN for salient audio detection. Also in this article, we have presented a short description of public image and video datasets with annotated salient objects or events, as well as the often used metrics for the results’ evaluation.Practical relevance:This survey is considered as a contribution in the study of rapidly developed deep learning methods with respect to the saliency detection in the images and videos.

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