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Multi-Modal Military event extraction Based on Knowledge Fusion

Computers, Materials & Continua 2023年第10期77卷 97-114页

作者： Yuyuan Xiang Yangli Jia Xiangliang Zhang Zhenling Zhang School of Computer Science Liaocheng UniversityLiaocheng252059China

event extraction stands as a significant endeavor within the realm of information extraction,aspiring to automatically extract structured event information from vast volumes of unstructured *** event elements from multi-modal data remains a challenging task due to the presence of a large number of images and overlapping event elements in the *** researchers have proposed various methods to accomplish this task,most existing event extraction models cannot address these challenges because they are only applicable to text *** solve the above issues,this paper proposes a multi-modal event extraction method based on knowledge ***,for event-type recognition,we use a meticulous pipeline approach that integrates multiple pre-trained *** approach enables a more comprehensive capture of the multidimensional event semantic features present in military texts,thereby enhancing the interconnectedness of information between trigger words and *** event element extraction,we propose a method for constructing a priori templates that combine event types with corresponding trigger *** approach facilitates the acquisition of fine-grained input samples containing event trigger words,thus enabling the model to understand the semantic relationships between elements in greater ***,a fusion method for spatial mapping of textual event elements and image elements is proposed to reduce the category number overload and effectively achieve multi-modal knowledge *** experimental results based on the CCKS 2022 dataset show that our method has achieved competitive results,with a comprehensive evaluation value F1-score of 53.4%for the *** results validate the effectiveness of our method in extracting event elements from multi-modal data.

关键词： event extraction multi-modal knowledge fusion pre-trained models

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Overview of CCKS 2020 Task 3: Named Entity Recognition and event extraction in Chinese Electronic Medical Records

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Data Intelligence 2021年第3期3卷 376-388页

作者： Xia Li Qinghua Wen Hu Lin Zengtao Jiao Jiangtao Zhang The 305th Hospital of the Chinese People’s Liberation Army Wenjin StreetXicheng DistrictBeijing 100017China Department of Computer Science and Technology Tsinghua UniversityBeijing 100084China Yiducloud Beijing Technology Co. Ltd.Huayuan North RoadHaidian DistrictBeijing 100089China

The China Conference on Knowledge Graph and Semantic Computing(CCKS)2020 Evaluation Task 3 presented clinical named entity recognition and event extraction for the Chinese electronic medical *** annotated data sets and some other additional resources for these two subtasks were provided for *** evaluation competition attracted 354 teams and 46 of them successfully submitted the valid *** pre-trained language models are widely applied in this evaluation *** argumentation and external resources are also helpful.

关键词： Chinese electronic medical records event extraction Named entity recognition Clinical text CCKS

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A Joint Learning Framework for the CCKS-2020 Financial event extraction Task

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Data Intelligence 2021年第3期3卷 444-459页

作者： Jiawei Sheng Qian Li Yiming Hei Shu Guo Bowen Yu Lihong Wang Min He Tingwen Liu Hongbo Xu Institute of Information Engineering Chinese Academy of SciencesBeijing 100093China School of Cyber Security University of Chinese Academy of SciencesBeijing 100049China School of Computer Science Beihang UniversityBeijing 100191China School of Cyber Science and Technology Beihang UniversityBeijing 100191China National Computer Network Emergency Response Technical Team/Coordination Center of China Beijing 100029China

This paper presents a winning solution for the CCKS-2020 financial event extraction task, where the goal is to identify event types, triggers and arguments in sentences across multiple event types. In this task, we focus on resolving two challenging problems(i.e., low resources and element overlapping) by proposing a joint learning framework, named SaltyFishes. We first formulate the event extraction task as a joint probability model. By sharing parameters in the model across different types, we can learn to adapt to low-resource events based on high-resource events. We further address the element overlapping problems by a mechanism of Conditional Layer Normalization, achieving even better extraction accuracy. The overall approach achieves an F1-score of 87.8% which ranks the first place in the competition.

关键词： event detection event extraction Joint learning Financial event

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Joint event extraction Based on Global event-Type Guidance and Attention Enhancement

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Computers, Materials & Continua 2021年第9期68卷 4161-4173页

作者： Daojian Zeng Jian Tian Ruoyao Peng Jianhua Dai Hui Gao Peng Peng Hunan Normal University Changsha410081China Changsha University of Science&Technology Changsha410114China National University of Defense Technology Changsha410073China University of Waterloo WaterlooN2L3GICanada

event extraction is one of the most challenging tasks in information *** is a common phenomenon where multiple events exist in the same ***,extracting multiple events is more difficult than extracting a single *** event extraction methods based on sequence models ignore the interrelated information between events because the sequence is too *** addition,the current argument extraction relies on the results of syntactic dependency analysis,which is complicated and prone to error *** order to solve the above problems,a joint event extraction method based on global event-type guidance and attention enhancement was proposed in this ***,for multiple event detection,we propose a global-type guidance method that can detect event types in the candidate sequence in advance to enhance the correlation information between *** argument extraction,we converted it into a table-flling problem,and proposed a table-flling method of the attention mechanism,that is simple and can enhance the correlation between trigger words and *** experimental results based on the ACE 2005 dataset showed that the proposed method achieved 1.6%improvement in the task of event detection,and obtained state-of-the-art results in the argument extraction task,which proved the effectiveness of the method.

关键词： event extraction event-type guidance table flling attention mechanisms

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Joint Entity and event extraction with Generative Adversarial Imitation Learning

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Data Intelligence 2019年第2期1卷 99-120页

作者： Tongtao Zhang Heng Ji Avirup Sil Computer Science Department Rensselaer Polytechnic InstituteTroyNew York 12180-3590USA IBM Research AI ArmonkNew York 10504-1722USA

We propose a new framework for entity and event extraction based on generative adversarial imitation learning-an inverse reinforcement learning method using a generative adversarial network(GAN).We assume that instances and labels yield to various extents of difficulty and the gains and penalties(rewards)are expected to be *** utilize discriminators to estimate proper rewards according to the difference between the labels committed by the ground-truth(expert)and the extractor(agent).Our experiments demonstrate that the proposed framework outperforms state-of-the-art methods.

关键词： Information extraction event extraction Imitation learning Generative adversarial network

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A Prior Information Enhanced extraction Framework for Document-level Financial event extraction

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Data Intelligence 2021年第3期3卷 460-476页

作者： Haitao Wang Tong Zhu Mingtao Wang Guoliang Zhang Wenliang Chen School of Computer Science and Technology Soochow UniversitySuzhou 215006China

Document-level financial event extraction(DFEE) is the task of detecting events 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 proposed 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 participated in the share task of CCKS2020 Task 4-2: Documentlevel event Arguments extraction. On both Leaderboard A and Leaderboard B, PIEE took the first place and significantly outperformed the other systems.

关键词： event extraction Information extraction Financial event event detection event argument extraction

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A comprehensive review of existing corpora and methods for creating annotated corpora for event extraction tasks

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Journal of Data and Information Science 2024年第4期9卷 196-238页

作者： Mohd Hafizul Afifi Abdullah Norshakirah Aziz Said Jadid Abdulkadir Kashif Hussain Hitham Alhussian Noureen Talpur Centre for Cyber-Physical Systems(C2PS) Universiti Teknologi PETRONAS32610 Seri IskandarMalaysia Centre for Research in Data Science(CeRDaS) Universiti Teknologi PETRONAS32610 Seri IskandarMalaysia Department of Science and Engineering Solent UniversitySO140YNUK

Purpose:The purpose of this study is to serve as a comprehensive review of the existing annotated *** review study aims to provide information on the existing annotated corpora for event extraction,which are limited but essential for training and improving the existing event extraction *** addition to the primary goal of this study,it provides guidelines for preparing an annotated corpus and suggests suitable tools for the annotation ***/methodology/approach:This study employs an analytical approach to examine available corpus that is suitable for event extraction *** offers an in-depth analysis of existing event extraction corpora and provides systematic guidelines for researchers to develop accurate,high-quality *** ensures the reliability of the created corpus and its suitability for training machine learning ***:Our exploration reveals a scarcity of annotated corpora for event extraction *** particular,the English corpora are mainly focused on the biomedical and general *** the issue of annotated corpora scarcity,there are several high-quality corpora available and widely used as benchmark ***,access to some of these corpora might be limited owing to closed-access policies or discontinued maintenance after being initially released,rendering them inaccessible owing to broken ***,this study documents the available corpora for event extraction *** limitations:Our study focuses only on well-known corpora available in English and ***,this study places a strong emphasis on the English corpora due to its status as a global lingua franca,making it widely understood compared to other *** implications:We genuinely believe that this study provides valuable knowledge that can serve as a guiding framework for preparing and accurately annotating events from text *** provides comprehensive guidelines for researchers to improve th

关键词： Information extraction event extraction Text mining Large language model Natural language processing

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BUEES： a bottom-up event extraction system

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Frontiers of Information Technology & Electronic Engineering 2015年第7期16卷 541-552页

作者： Xiao DING Bing QIN Ting LIU Research Center for Social Computing and Information Retrieval Harbin Institute of Technology Harbin 150001 China

Traditional event extraction systems focus mainly on event type identification and event participant extraction based on pre-specified event type paradigms and manually annotated corpora. However, different domains have different event type paradigms. When transferring to a new domain, we have to build a new event type paradigm and annotate a new corpus from scratch. This kind of conventional event extraction system requires massive human effort, and hence prevents event extraction from being widely applicable. In this paper, we present BUEES, a bottom-up event extraction system, which extracts events from the web in a completely unsupervised way. The system automatically builds an event type paradigm in the input corpus, and then proceeds to extract a large number of instance patterns of these events. Subsequently, the system extracts event arguments according to these patterns. By conducting a series of experiments, we demonstrate the good performance of BUEES and compare it to a state-of-the-art Chinese event extraction system, i.e., a supervised event extraction system. Experimental results show that BUEES performs comparably （5% higher F-measure in event type identification and 3-0 higher F-measure in event argument extraction）, but without any human effort.

关键词： event extraction Unsupervised learning Bottom-up

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A Multi-event extraction Model for Nursing Records

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国际计算机前沿大会会议论文集 2022年第2期 146-158页

作者： Ruoyu Song Lan Wei Yuhang Guo School of Computer Science and Technology Beijing Institute of TechnologyBeijing 100081China Xuanwu Hospital Capital Medical University Beijing 100053China

Nursing records contain information on patients’treatment processes,which reflect the changes in patients’conditions and have legal ***,some of the written records of intensive care unit(ICU)nurses are incomplete according to our *** paper proposes an approach extracting structured nursing events from unstructured nursing records for detecting the missing items *** to the PIO(problem,intervention,outcome)principle in the field of medical care,we propose event schemas for nursing records and annotate a Chinese nursing event extraction dataset(CNEED)on ICU nursing *** find that several events may occur in a nursing ***,we present a multi-event extraction model for the nursing *** experimental results demonstrate that our model achieves good results on CNEED and outperforms competitive methods on the multi-event argument attribution *** observing the results of automatic event extraction by our model,we detect missing items in the existing nursing *** proves that our model can be used to help nurses check and improve the method of recording nursing processes.

关键词： event extraction Nursing records Multi-event

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event co-reference resolution via a multi-loss neural network without using argument information

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Science China(Information Sciences) 2019年第11期62卷 31-39页

作者： Xinyu ZUO Yubo CHEN Kang LIU Jun ZHAO Institute of Automation Chinese Academy of Sciences University of Chinese Academy of Sciences

event co-reference resolution is an important task in natural language processing, and nearly all the existing approaches for this task rely on event argument information. However, these methods tend to suffer from error propagation from event argument extraction. Additionally, not every event mention contains all arguments of an event, and the argument information may confuse the model where events contain arguments to detect an event co-reference in real text. Furthermore, the context information of an event is useful to infer the co-reference between events. Thus, to reduce the errors propagated from event argument extraction and use context information effectively, we propose a multi-loss neural network model that does not require any argument information relating to the within-document event co-reference resolution task; furthermore, it achieves a significantly better performance than the state-of-the-art methods.

关键词： event co-reference resolution neural network information extraction multi-loss function event extraction

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