检索结果-南通市图书馆

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 text.Extracting 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 data.Although 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 scenarios.To solve the above issues,this paper proposes a multi-modal event extraction method based on knowledge fusion.Specifically,for event-type recognition,we use a meticulous pipeline approach that integrates multiple pre-trained models.This 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 events.For event element extraction,we propose a method for constructing a priori templates that combine event types with corresponding trigger words.This 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 depth.Furthermore,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 fusion.The 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 model.These 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

来源：

维普期刊数据库评论

在线全文

维普期刊数据库

学校读者我要写书评

暂无评论

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

引用

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 records.Two annotated data sets and some other additional resources for these two subtasks were provided for participators.This evaluation competition attracted 354 teams and 46 of them successfully submitted the valid results.The pre-trained language models are widely applied in this evaluation task.Data argumentation and external resources are also helpful.

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

来源：

维普期刊数据库

同方期刊数据库评论

在线全文

学校读者我要写书评

暂无评论

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

引用

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

来源：

维普期刊数据库

同方期刊数据库评论

在线全文

学校读者我要写书评

暂无评论

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

引用

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

来源：

维普期刊数据库

同方期刊数据库评论

在线全文

学校读者我要写书评

暂无评论

Joint Entity and event extraction with Generative Adversarial Imitation Learning

引用

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 diverse.We 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

来源：

维普期刊数据库

同方期刊数据库评论

在线全文

学校读者我要写书评

暂无评论

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

引用

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 extraction.It is a common phenomenon where multiple events exist in the same sentence.However,extracting multiple events is more difficult than extracting a single event.Existing event extraction methods based on sequence models ignore the interrelated information between events because the sequence is too long.In addition,the current argument extraction relies on the results of syntactic dependency analysis,which is complicated and prone to error trans-mission.In order to solve the above problems,a joint event extraction method based on global event-type guidance and attention enhancement was proposed in this work.Specifically,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 events.For 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 arguments.The 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

来源：

维普期刊数据库评论

在线全文

维普期刊数据库

学校读者我要写书评

暂无评论

A Multi-event extraction Model for Nursing Records

引用

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 effects.However,some of the written records of intensive care unit(ICU)nurses are incomplete according to our observations.This paper proposes an approach extracting structured nursing events from unstructured nursing records for detecting the missing items automatically.According 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 records.We find that several events may occur in a nursing record.Therefore,we present a multi-event extraction model for the nursing records.The experimental results demonstrate that our model achieves good results on CNEED and outperforms competitive methods on the multi-event argument attribution problem.By observing the results of automatic event extraction by our model,we detect missing items in the existing nursing records.This 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

来源：

维普期刊数据库评论

在线全文

维普期刊数据库

学校读者我要写书评

暂无评论

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

引用

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

来源：

同方期刊数据库评论

在线全文

同方期刊数据库

学校读者我要写书评

暂无评论

Identifying associations between epidemiological entities in news data for animal disease surveillance

引用

Artificial Intelligence in Agriculture 2021年第1期5卷 163-174页

作者： Sarah Valentin Renaud Lancelot Mathieu Roche CIRAD UMR ASTREUMR TETISF-34398 MontpellierFrance ASTRE Univ MontpellierCIRADINRAEMontpellierFrance TETIS Univ MontpellierAgroParisTechCIRADCNRSINRAEMontpellierFrance

event-based surveillance systems are at the crossroads of human and animal(and plant and ecosystem)health,epidemiology,statistics,and informatics.Thus,their deployment faces many challenges specific to each domain and their intersections,such as relations among automation,artificial intelligence,and expertise.In this context,ourwork pertins to the extraction of epidemiological events in textual data(i.***.news)by unsupervised methods.We define the event extraction task as detecting pairs of epidemiological entities(e.g.a disease name and location).The quality of the ranked lists of pairs was evaluated using specific ranking evaluation metrics.We used a publicly available annotated corpus of 438 documents(i.***.news articles)related to animal disease events.The statistical approach was able to detect event-related pairs of epidemiological features with a good trade-off between precision and recall.Our results showed that using a window of words outperformed document-based and sentence-based approaches,while reducing the probability of detecting false pairs.Our results indicated that Mutual Information was less adapted than the Dice coefficient for ranking pairs of features in the event extraction framework.We believe that Mutual Information would be more relevant for rare pair detection(i.***.weak signals),but requires higher manual curation to avoid false positive extraction pairs.Moreover,generalising the country-level spatial features enabled better discrimination(i.***.ranking)of relevant disease-location pairs for event extraction.

关键词： Animal disease surveillance Text mining event extraction

来源：

维普期刊数据库评论

在线全文

维普期刊数据库

学校读者我要写书评

暂无评论

Cross-Context News Corpus for Protest event-Related Knowledge Base Construction

引用

Data Intelligence 2021年第2期3卷 308-335页

作者： Ali Hürriyetoglu Erdem Yörük Osman Mutlu Fırat Durusan ÇagrıYoltar Deniz Yüret Burak Gürel KoçUniversity Rumelifeneri yoluSariyerIstanbul 34450Turkey

We describe a gold standard corpus of protest events that comprise various local and international English language sources from various countries.The corpus contains document-,sentence-,and token-level annotations.This corpus facilitates creating machine learning models that automatically classify news articles and extract protest event-related information,constructing knowledge bases that enable comparative social and political science studies.For each news source,the annotation starts with random samples of news articles and continues with samples drawn using active learning.Each batch of samples is annotated by two social and political scientists,adjudicated by an annotation supervisor,and improved by identifying annotation errors semi-automatically.We found that the corpus possesses the variety and quality that are necessary to develop and benchmark text classification and event extraction systems in a cross-context setting,contributing to the generalizability and robustness of automated text processing systems.This corpus and the reported results will establish a common foundation in automated protest event collection studies,which is currently lacking in the literature.

关键词： event extraction Text classification Political science Social science News Contentious politics Protests event coreference resolution

来源：

维普期刊数据库评论

在线全文

维普期刊数据库

学校读者我要写书评

暂无评论

欢迎您,

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

在线全文

在线全文

在线全文

在线全文

在线全文

在线全文

在线全文

在线全文

在线全文

在线全文

请选择保存的检索档案：

请选择收藏分类：

通借通还

欢迎您,

建议与咨询 留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

在线全文

在线全文

在线全文

在线全文

在线全文

在线全文

在线全文

在线全文

在线全文

在线全文

请选择保存的检索档案： 新增检索档案 确定 取消

请选择收藏分类： 新增自定义分类 确定 取消

通借通还

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

请选择保存的检索档案：

请选择收藏分类：