检索结果-南通市图书馆

A joint learning Framework for the CCKS-2020 Financial Event Extraction Task

在线全文

学校读者我要写书评

暂无评论

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

Knowledge-enriched joint-learning model for implicit emotion cause extraction

在线全文

学校读者我要写书评

暂无评论

CAAI Transactions on Intelligence Technology 2023年第1期8卷 118-128页

作者： Chenghao Wu Shumin Shi Jiaxing Hu Heyan Huang School of Computer Science and Technology Beijing Institute of TechnologyBeijingChina Beijing Engineering Research Center of High Volume Language Information Processing and Cloud Computing Applications BeijingChina

Emotion cause extraction(ECE)task that aims at extracting potential trigger events of certain emotions has attracted extensive attention ***,current work neglects the implicit emotion expressed without any explicit emotional keywords,which appears more frequently in application *** lack of explicit emotion information makes it extremely hard to extract emotion causes only with the local ***,an entire event is usually across multiple clauses,while existing work merely extracts cause events at clause level and cannot effectively capture complete cause event *** address these issues,the events are first redefined at the tuple level and a span-based tuple-level algorithm is proposed to extract events from different *** on it,a corpus for implicit emotion cause extraction that tries to extract causes of implicit emotions is *** authors propose a knowledge-enriched jointlearning model of implicit emotion recognition and implicit emotion cause extraction tasks(KJ-IECE),which leverages commonsense knowledge from ConceptNet and NRC_VAD to better capture connections between emotion and corresponding cause *** on both implicit and explicit emotion cause extraction datasets demonstrate the effectiveness of the proposed model.

关键词： emotion cause extraction external knowledge fusion implicit emotion recognition joint learning

Pedestrian Re-identification Model based on joint learning Method

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

Pedestrian Re-identification Model based on Joint learning M...

The Impact of Semi-Supervised learning on the Performance of Intelligent Chatbot System

第32届中国控制与决策会议

作者： Shuangsi Xue Hui Cao Tao Wang Xingyu Yan Zhuzhu Wang School of Electrical Engineering Xi’an Jiaotong University

A pedestrian re-identification（Re-ID） model based on joint learning method is *** paper proposed a two-branch model including global feature branch and global feature *** global feature branch uses the spatial pyramid pooling model to average the local feature informations of the image and increase the global information of different receptive *** local feature branch uses the dynamical distance optimization algorithm without additional *** results on public datasets show that the proposed model can effectively learn global and local features for Re-ID and outperforms existing basic person re-identification methods.

关键词： Pedestrian re-identification joint learning Spatial pyramid pooling Dynamical distance optimization

来源： cnki会议评论

在线全文

cnki会议

学校读者我要写书评

暂无评论

Computers, Materials & Continua 2022年第5期71卷 3937-3952页

作者： Sudan Prasad Uprety Seung Ryul Jeong The Graduate School of Business Information Technology Kookmin UniversitySeoul02707Korea

Artificial intelligent based dialog systems are getting attention from both business and academic *** key parts for such intelligent chatbot systems are domain classification,intent detection,and named entity *** supervised,unsupervised,and hybrid approaches are used to detect each *** intelligent systems,also called natural language understanding systems analyze user requests in sequential order:domain classification,intent,and entity recognition based on the semantic rules of the classified *** sequential approach propagates the downstream error;i.e.,if the domain classification model fails to classify the domain,intent and entity recognition ***,training such intelligent system necessitates a large number of user-annotated datasets for each *** study proposes a single joint predictive deep neural network framework based on long short-term memory using only a small user-annotated dataset to address these *** investigates value added by incorporating unlabeled data from user chatting logs into multi-domain spoken language understanding *** experimental analysis of the proposed joint frameworks,along with the semi-supervised multi-domain model,using open-source annotated and unannotated utterances shows robust improvement in the predictive performance of the proposed multi-domain intelligent chatbot over a base joint model and joint model based on adversarial learning.

关键词： Chatbot dialog system joint learning LSTM natural language understanding semi-supervised learning

Enhanced Deep Autoencoder Based Feature Representation learning for Intelligent Intrusion Detection System

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

Computers, Materials & Continua 2021年第9期68卷 3271-3288页

作者： Thavavel Vaiyapuri Adel Binbusayyis College of Computer Engineering and Sciences Prince Sattam Bin Abdulaziz UniversityAlKharjSaudi Arabia

In the era of Big data,learning discriminant feature representation from network traffic is identified has as an invariably essential task for improving the detection ability of an intrusion detection system(IDS).Owing to the lack of accurately labeled network traffic data,many unsupervised feature representation learning models have been proposed with state-of-theart ***,these models fail to consider the classification error while learning the feature ***,the learnt feature representation may degrade the performance of the classification *** the first time in the field of intrusion detection,this paper proposes an unsupervised IDS model leveraging the benefits of deep autoencoder(DAE)for learning the robust feature representation and one-class support vector machine(OCSVM)for finding the more compact decision hyperplane for intrusion ***,the proposed model defines a new unified objective function to minimize the reconstruction and classification error *** unique contribution not only enables the model to support joint learning for feature representation and classifier training but also guides to learn the robust feature representation which can improve the discrimination ability of the classifier for intrusion *** set of evaluation experiments are conducted to demonstrate the potential of the proposed ***,the ablation evaluation on benchmark dataset,NSL-KDD validates the design decision of the proposed ***,the performance evaluation on recent intrusion dataset,UNSW-NB15 signifies the stable performance of the proposed ***,the comparative evaluation verifies the efficacy of the proposed model against recently published state-of-the-art methods.

关键词： Cybersecurity network intrusion detection deep learning autoencoder stacked autoencoder feature representational learning joint learning one-class classifier OCSVM

joint user profiling with hierarchical attention networks

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

Frontiers of Computer Science 2023年第3期17卷 133-143页

作者： Xiaojian LIU Yi ZHU Xindong WU Key Laboratory of Knowledge Engineering with Big Data(Hefei University of Technology) Ministry of EducationHefei 230009China School of Computer Science and Information Engineering Hefei University of TechnologyHefei 230009China School of Information Engineering Yangzhou UniversityYangzhou 225009China Mininglamp Academy of Sciences MininglampBeijing 100193China

User profiling by inferring user personality traits,such as age and gender,plays an increasingly important role in many real-world *** existing methods for user profiling either use only one type of data or ignore handling the noisy information of ***,they usually consider this problem from only one *** this paper,we propose a joint user profiling model with hierarchical attention networks(JUHA)to learn informative user representations for user *** JUHA method does user profiling based on both inner-user and inter-user *** explore inner-user features from user behaviors(e.g.,purchased items and posted blogs),and inter-user features from a user-user graph(where similar users could be connected to each other).JUHA learns basic sentence and bag representations from multiple separate sources of data(user behaviors)as the first round of data *** this module,convolutional neural networks(CNNs)are introduced to capture word and sentence features of age and gender while the self-attention mechanism is exploited to weaken the noisy *** this,we build another bag which contains a user-user ***-user features are learned from this bag using propagation information between linked users in the *** acquire more robust data,inter-user features and other inner-user bag representations are joined into each sentence in the current bag to learn the final bag ***,all of the bag representations are integrated to lean comprehensive user representation by the self-attention *** experimental results demonstrate that our approach outperforms several state-of-the-art methods and improves prediction performance.

关键词： user profiling hierarchical attention joint learning inner-user feature inter-user feature

同方期刊数据库

Robust Integrated Models for Chinese Predicate-Argument Structure Analysis

在线全文

学校读者我要写书评

暂无评论

China Communications 2012年第3期9卷 10-18页

作者：罗彦彦浅原正幸松本裕治 Natural Language Processing Laboratory Graduate School of Information ScienceNara Institute of Science and Technology

Predicate-Argument （PA） structure anal- ysis is often divided into three subtasks： predicate sense disambiguation, argument identification and argument classification mostly been modeled in To date, they have isolation. However, this approach neglects logical constraints between them. We therefore exploite integrating predicate sense disambiguation with the latter two subtasks respectively, which verifies that the automatic predicate sense disambiguation could help the se- mantic role labeling task. In addition, a dual de- composition algorithm is used to alleviate the er- ror propagation between argument identification subtask and argument classification subtask by benefitting the argument identification subtask greatly. Experiment results show that our ap- proach leads to a better performance with PA a- nalysis than other pipeline approaches.

关键词： semantic role labeling PA structureanalysis dual decomposition joint learning