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Construction of a Chinese Corpus for Multi-Type Economic Event Relation

ACM TRANSACTIONS ON ASIAN AND LOW-RESOURCE LANGUAGE INFORMATION PROCESSING 2022年第6期21卷 1–20页

作者： Wan, Qizhi Wan, Changxuan Xiao, Keli Liu, Dexi Liu, Qing Deng, Jiangling Luo, Wenkang Hu, Rong Jiangxi Univ Finance & Econ Jiangxi Key Lab Data & Knowledge Engn Sch Informat Management 665 Yuping West St Nanchang 330032 Jiangxi Peoples R China SUNY Stony Brook Coll Business 310 Adm Bldg Stony Brook NY 11780 USA Jiangxi Univ Finance & Econ Jiangxi Key Lab Data & Knowledge Engn Sch Software & Internet Things Engn 665 Yuping West St Nanchang 330032 Jiangxi Peoples R China

We construct a Chinese Economic Event Treebank (CEETB), focusing on revealing economic and finance events and their relations. Investigating economic event relations will benefit academic research and practice in not just economics but many other scientific areas. The characteristics of economic-related texts (e.g., abundant longer enterprises names and terms) and the Chinese language speciality (e.g., component ellipsis in long sentences) have resulted in challenges in the event relation extraction task. Existing Chinese corpora containing economic event relations mainly focused on finance areas (e.g., the equity market) and only covered a few event types. To support research that may involve economic text analysis in Chinese, our CEETB is constructed following a carefully designed process. First, based on practical and research requirements, we summarize nine different types of event relations and four types of component ellipses in economic texts. Then, an excellent annotation scheme is presented to hyalinize the model, strategy, and process in annotation, followed by statistical analysis and quality evaluation for the CEETB corpus. Finally, to demonstrate the strengths of the constructed corpus in practical applications, we conduct experiments on five SOTA models for event relation extraction.

关键词： Economic event relation event extraction element ellipsis information extraction natural language processing

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Dependency Structure -Enhanced Graph Attention Networks for Event Detection 38

Dependency Structure -Enhanced Graph Attention Networks for ...

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38th AAAI Conference on Artificial Intelligence (AAAI) / 36th Conference on Innovative Applications of Artificial Intelligence / 14th Symposium on Educational Advances in Artificial Intelligence

作者： Wan, Qizhi Wan, Changxuan Xiao, Keli Lu, Kun Li, Chenliang Liu, Xiping Liu, Dexi Jiangxi Univ Finance & Econ Sch Informat Management Nanchang Jiangxi Peoples R China Jiangxi Key Lab Data & Knowledge Engn Nanchang Jiangxi Peoples R China SUNY Stony Brook Coll Business Stony Brook NY USA Univ Oklahoma Sch Lib & Informat Studies Norman OK 73019 USA Wuhan Univ Sch Cyber Sci & Engn Wuhan Peoples R China

ISBN: (纸本)1577358872

Existing models on event detection share three -fold limitations, including (1) insufficient consideration of the structures between dependency relations, (2) limited exploration of the directed -edge semantics, and (3) issues in strengthening the event core arguments. To tackle these problems, we propose a dependency structure-enhanced event detection framework. In addition to the traditional token dependency parsing tree, denoted as TDG, our model considers the dependency edges in it as new nodes and constructs a dependency relation graph (DRG). DRG allows the embedding representations of dependency relations to be updated as nodes rather than edges in a graph neural network. Moreover, the levels of core argument nodes in the two graphs are adjusted by dependency relation types in TDG to enhance their status. Subsequently, the two graphs are further encoded and jointly trained in graph attention networks (GAT). Importantly, we design an interaction strategy of node embedding for the two graphs and refine the attention coefficient computational method to encode the semantic meaning of directed edges. Extensive experiments are conducted to validate the effectiveness of our method, and the results confirm its superiority over the state-of-the-art baselines. Our model outperforms the best benchmark with the Fl score increased by 3.5 and 3.4 percentage points on ACE2005 English and Chinese corpus.

关键词： Semantics

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Sequential Manipulation Against Rank Aggregation: Theory and Algorithm

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IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE 2024年第12期46卷 9353-9370页

作者： Ma, Ke Xu, Qianqian Zeng, Jinshan Liu, Wei Cao, Xiaochun Sun, Yingfei Huang, Qingming Univ Chinese Acad Sci Sch Elect Elect & Commun Engn Beijing 100049 Peoples R China Chinese Acad Sci Inst Comp Technol Key Lab Intelligent Informat Proc Beijing 100190 Peoples R China Jiangxi Normal Univ Sch Comp & Informat Engn Nanchang 330022 Jiangxi Peoples R China Tencent Data Platform Shenzhen 518054 Peoples R China Sun Yat Sen Univ Sch Cyber Sci & Technol Shenzhen Campus Shenzhen 518107 Peoples R China Univ Chinese Acad Sci Key Lab Big Data Min & Knowledge Management BDKM Sch Comp Sci & Technol Beijing 100049 Peoples R China

Rank aggregation with pairwise comparisons is widely encountered in sociology, politics, economics, psychology, sports, etc. Given the enormous social impact and the consequent incentives, the potential adversary has a strong motivation to manipulate the ranking list. However, the ideal attack opportunity and the excessive adversarial capability cause the existing methods to be impractical. To fully explore the potential risks, we leverage an online attack on the vulnerable data collection process. Since it is independent of rank aggregation and lacks effective protection mechanisms, we disrupt the data collection process by fabricating pairwise comparisons without knowledge of the future data or the true distribution. From the game-theoretic perspective, the confrontation scenario between the online manipulator and the ranker who takes control of the original data source is formulated as a distributionally robust game that deals with the uncertainty of knowledge. Then we demonstrate that the equilibrium in the above game is potentially favorable to the adversary by analyzing the vulnerability of the sampling algorithms such as Bernoulli and reservoir methods. According to the above theoretical analysis, different sequential manipulation policies are proposed under a Bayesian decision framework and a large class of parametric pairwise comparison models. For attackers with complete knowledge, we establish the asymptotic optimality of the proposed policies. To increase the success rate of the sequential manipulation with incomplete knowledge, a distributionally robust estimator, which replaces the maximum likelihood estimation in a saddle point problem, provides a conservative data generation solution. Finally, the corroborating empirical evidence shows that the proposed method manipulates the results of rank aggregation methods in a sequential manner.

关键词： Online manipulation adversarial learning pairwise comparison ranking aggregation

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SLMP: A Scientific Literature Management Platform Based on Large Language Models 15

SLMP: A Scientific Literature Management Platform Based on L...

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15th IEEE International Conference on knowledge Graph, ICKG 2024

作者： Guo, Menghao Jiang, Jinling Wu, Fan Sun, Shanxin Zhang, Chen Li, Wenhui Sun, Zeyi Chen, Guangyong Wu, Xindong Research Center for Life Sciences Computing Zhejiang Lab Hangzhou China Research Center for Data Hub and Security Zhejiang Lab Hangzhou China Research Center for High Efficiency Computing System Zhejiang Lab Hangzhou China Hefei University of Technology Key Laboratory of Knowledge Engineering With Big Data Hefei China

ISBN: (纸本)9798331508821

This paper presents a Scientific Literature Management Platform (SLMP, demo link1 ) based on large language models (LLMs). The platform consists of four modules: literature management, literature extraction, literature retrieval, and question answering. The core techniques used to support the four modules across the platform include a fine-tuned model PaperExtractGPT and a continual pre-training model ChatPaperGPT based on ChatGLM2 using the data from scientific research literature, responsible for information extraction and communication, respectively. Due to their powerful capabilities in natural language understanding and generation, LLMs can understand complex scientific concepts based on the provided contexts, and thus generate high-quality texts and conduct in-depth information retrieval and question answering. Our platform can help researchers manage and utilize literature more effectively and efficiently for finding relevant literature, obtaining required information, and generating new knowledge. © 2024 IEEE.

关键词： Question answering

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Joint Document-Level Event Extraction via Token-Token Bidirectional Event Completed Graph 61

Joint Document-Level Event Extraction via Token-Token Bidire...

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61st Annual Meeting of the the Association-for-Computational-Linguistics (ACL)

作者： Wan, Qizhi Wan, Changxuan Xiao, Keli Liu, Dexi Li, Chenliang Zheng, Bolong Liu, Xiping Hu, Rong Jiangxi Key Lab Data & Knowledge Engn Nanchang Jiangxi Peoples R China Jiangxi Univ Finance & Econ Sch Informat Management Nanchang Jiangxi Peoples R China SUNY Stony Brook Coll Business Stony Brook NY 11794 USA Wuhan Univ Sch Cyber Sci & Engn Wuhan Peoples R China Huazhong Univ Sci & Technol Sch Comp Scie Tech Wuhan Peoples R China

ISBN: (纸本)9781959429722

We solve the challenging document-level event extraction problem by proposing a joint exaction methodology that can avoid inefficiency and error propagation issues in classic pipeline methods. Essentially, we address the three crucial limitations in existing studies. First, the autoregressive strategy of path expansion heavily relies on the orders of argument roles. Second, the number of events in documents must be specified in advance. Last, unexpected errors usually exist when decoding events based on the entity-entity adjacency matrix. This paper designs a Token-Token Bidirectional Event Completed Graph (TT-BECG) in which the relation eType-Role1-Role2 serves as the edge type, precisely revealing which tokens play argument roles in an event of a specific event type. Exploiting the token-token adjacency matrix of the TT-BECG, we develop an edge-enhanced joint document-level event extraction model. Guided by the target token-token adjacency matrix, the predicted token-token adjacency matrix can be obtained during model training. Then, the event records in a document are decoded based on the predicted matrix, including the graph structure and edge-type decoding. Extensive experiments are conducted on two public datasets, and the results confirm the effectiveness of our method and its superiority over the state-of-the-art baselines.

关键词： Decoding

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Reference point reconstruction-based firefly algorithm for irregular multi-objective optimization

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APPLIED INTELLIGENCE 2023年第1期53卷 962-983页

作者： He, Yichen Peng, Hu Deng, Changshou Dong, Xiwei Wu, Zhijian Guo, Zhaolu Jiujiang Univ Sch Comp & Big Data Sci Jiujiang 332005 Peoples R China Wuhan Univ Sch Comp Sci Wuhan 430072 Peoples R China Jiangxi Univ Sci & Technol Sch Sci Ganzhou 341000 Peoples R China Jilin Univ Minist Educ Key Lab Symbol Computat & Knowledge Engn Changchun 130012 Peoples R China

Reference point-based environmental selection has achieved promising performance in multi-objective optimization problems. However, when solving the irregular multi-objective optimization problems, the performance of environmental selection is affected. This is because the irregular Pareto front is often degraded, disconnected, inverted, or with sharp tails, resulting in some reference points not located in appropriate region. This releases the selection pressure. Therefore, adjusting or generating some points is necessary to tackle this problem. However, how to identify the region of interest and how to generate new points in the appropriate region are the current problems to be solved. In this paper, a region-based reconstruction for reference points is proposed. For simplicity, the smallest region which consists of M reference points (M is the dimension of objective space) in the hyperplane of reference point is identified as the unit region. If the vertexes of the region all belong to active reference points, the region will be identified as region of interest and new reference points will be reconstructed in this region. In addition, the process is activated in the later stage of the algorithm operation, while the efficient of the search algorithm is weak. In order to find more valuable individuals in the neighborhood region of selected individuals, thereby, firefly algorithm is employed as search algorithm because of its search mechanism which has strong indicative features. Several experiments are designed to verify the performance of the proposed method. The experiment results show that the proposed method is effective.

关键词： Multi-objective optimization Region of interest Reference point reconstruction Firefly algorithm

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HUSS:A Heuristic Method for Understanding the Semantic Structure of Spreadsheets

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data Intelligence 2023年第3期5卷 537-559页

作者： Xindong Wu Hao Chen Chenyang Bu Shengwei Ji Zan Zhang Victor S.Sheng Key Laboratory of Knowledge Engineering with Big Data(the Ministry of Education of China) Hefei University of TechnologyChinaSchool of Computer Science and Information EngineeringHefei University of TechnologyHefeiChina Research Institute of Artificial Intelligence Zhejiang LabHangzhouChina Department of Computer Science Texas Tech UniversityLubbockTX 79409USA

Spreadsheets contain a lot of valuable data and have many practical *** key technology of these practical applications is how to make machines understand the semantic structure of spreadsheets,e.g.,identifying cell function types and discovering relationships between cell *** existing methods for understanding the semantic structure of spreadsheets do not make use of the semantic information of cells.A few studies do,but they ignore the layout structure information of spreadsheets,which affects the performance of cell function classification and the discovery of different relationship types of cell *** this paper,we propose a Heuristic algorithm for Understanding the Semantic Structure of spreadsheets(HUSS).Specifically,for improving the cell function classification,we propose an error correction mechanism(ECM)based on an existing cell function classification model[11]and the layout features of *** improving the table structure analysis,we propose five types of heuristic rules to extract four different types of cell pairs,based on the cell style and spatial location *** experimental results on five real-world datasets demonstrate that HUSS can effectively understand the semantic structure of spreadsheets and outperforms corresponding baselines.

关键词： Spreadsheet semantic structure Information extraction Heuristics Cell function analysis Table structure analysis

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A Tale of HodgeRank and Spectral Method: Target Attack Against Rank Aggregation is the Fixed Point of Adversarial Game

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IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE 2023年第4期45卷 4090-4108页

作者： Ma, Ke Xu, Qianqian Zeng, Jinshan Li, Guorong Cao, Xiaochun Huang, Qingming Univ Chinese Acad Sci Sch Comp Sci & Technol Beijing 100049 Peoples R China Chinese Acad Sci Inst Comp Technol Key Lab Intelligent Informat Proc Beijing 100190 Peoples R China Jiangxi Normal Univ Sch Comp & Informat Engn Nanchang 330022 Jiangxi Peoples R China Sun Yat sen Univ Sch Cyber Sci & Technol Shenzhen Campus Shenzhen 518107 Guangdong Peoples R China Univ Chinese Acad Sci Key Lab Big Data Min & Knowledge Management Beijing 100049 Peoples R China Chinese Acad Sci Inst Comp Technol Key Lab Intelligent Informat Proc Beijing 100190 Peoples R China Peng Cheng Lab Shenzhen 518055 Guangdong Peoples R China

Rank aggregation with pairwise comparisons has shown promising results in elections, sports competitions, recommendations, and information retrieval. However, little attention has been paid to the security issue of such algorithms, in contrast to numerous research work on the computational and statistical characteristics. Driven by huge profit, the potential adversary has strong motivation and incentives to manipulate the ranking list. Meanwhile, the intrinsic vulnerability of the rank aggregation methods is not well studied in the literature. To fully understand the possible risks, we focus on the purposeful adversary who desires to designate the aggregated results by modifying the pairwise data in this paper. From the perspective of the dynamical system, the attack behavior with a target ranking list is a fixed point belonging to the composition of the adversary and the victim. To perform the targeted attack, we formulate the interaction between the adversary and the victim as a game-theoretic framework consisting of two continuous operators while Nash equilibrium is established. Then two procedures against HodgeRank and RankCentrality are constructed to produce the modification of the original data. Furthermore, we prove that the victims will produce the target ranking list once the adversary masters the complete information. It is noteworthy that the proposed methods allow the adversary only to hold incomplete information or imperfect feedback and perform the purposeful attack. The effectiveness of the suggested target attack strategies is demonstrated by a series of toy simulations and several real-world data experiments. These experimental results show that the proposed methods could achieve the attacker's goal in the sense that the leading candidate of the perturbed ranking list is the designated one by the adversary.

关键词： Voting Sports Electric potential Training Stochastic processes Security Optimization Adversarial learning pairwise comparison ranking aggregation

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Towards annotation-free evaluation of cross-lingual image captioning 2

Towards annotation-free evaluation of cross-lingual image ca...

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2nd ACM International Conference on Multimedia in Asia, MMAsia 2020

作者： Chen, Aozhu Huang, Xinyi Lin, Hailan Li, Xirong Moe Key Lab of Data Engineering and Knowledge Engineering Renmin University of China Beijing China

ISBN: (纸本)9781450383080

Cross-lingual image captioning, with its ability to caption an unlabeled image in a target language other than English, is an emerging topic in the multimedia field. In order to save the precious human resource from re-writing reference sentences per target language, in this paper we make a brave attempt towards annotation-free evaluation of cross-lingual image captioning. Depending on whether we assume the availability of English references, two scenarios are investigated. For the first scenario with the references available, we propose two metrics, i.e., WMDRel and CLinRel. WMDRel measures the semantic relevance between a model-generated caption and machine translation of an English reference using their Word Mover's Distance. By projecting both captions into a deep visual feature space, CLinRel is a visual-oriented cross-lingual relevance measure. As for the second scenario, which has zero reference and is thus more challenging, we propose CMedRel to compute a cross-media relevance between the generated caption and the image content, in the same visual feature space as used by CLinRel. We have conducted a number of experiments to evaluate the effectiveness of the three proposed metrics. The combination of WMDRel, CLinRel and CMedRel has a Spearman's rank correlation of 0.952 with the sum of BLEU-4, METEOR, ROUGE-L and CIDEr, four standard metrics computed using references in the target language. CMedRel alone has a Spearman's rank correlation of 0.786 with the standard metrics. The promising results show high potential of the new metrics for evaluation with no need of references in the target language. © 2021 ACM.

关键词： Semantics

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knowledge Graph based Mutual Attention for Machine Reading Comprehension over Anti-Terrorism Corpus

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data Intelligence 2023年第3期5卷 685-706页

作者： Feng Gao Jin Hou Jinguang Gu Lihua Zhang School of Computer Science and Technology Wuhan University of Science and TechnologyWuhan 430065Hubei The Key Laboratory of Rich-Media Knowledge Organization and Service of Digital Publishing Content Insitute of Scientic and Technical Information of ChinaBeijing 100038China Wuhan University of Science and Technology Big Data Science and Engineering Research Institute Wuhan 430065Hubei Eastchina Jiaotong University Nanchang 330013Jiangxi

Machine reading comprehension has been a research focus in natural language processing and intelligence ***,there is a lack of models and datasets for the MRC tasks in the anti-terrorism ***,current research lacks the ability to embed accurate background knowledge and provide precise *** address these two problems,this paper first builds a text corpus and testbed that focuses on the anti-terrorism domain in a semi-automatic ***,it proposes a knowledge-based machine reading comprehension model that fuses domain-related triples from a large-scale encyclopedic knowledge base to enhance the semantics of the *** eliminate knowledge noise that could lead to semantic deviation,this paper uses a mixed mutual ttention mechanism among questions,passages,and knowledge triples to select the most relevant triples before embedding their semantics into the *** results indicate that the proposed approach can achieve a 70.70%EM value and an 87.91%F1 score,with a 4.23%and 3.35%improvement over existing methods,respectively.

关键词： Machine reading comprehension Anti-terrorism domain knowledge embedding knowledge attention Mutual attention

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