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Joint 30th International Conference on Computational Linguistics and 14th International Conference on Language Resources and Evaluation, LREC-COLING 2024

作者： Ren, Weicheng Li, Zixuan Jin, Xiaolong Bai, Long Su, Miao Liu, Yantao Guan, Saiping Guo, Jiafeng Cheng, Xueqi School of Computer Science and Technology University of Chinese Academy of Sciences China Key Lab of Network Data Science and Technology Institute of Computing Technology Chinese Academy of Sciences China

ISBN: (纸本)9782493814104

Nested Event Extraction (NEE) aims to extract complex event structures where an event contains other events as its arguments recursively. Nested events involve a kind of Pivot Elements (PEs) that simultaneously act as arguments of outer-nest events and as triggers of inner-nest events, and thus connect them into nested structures. This special characteristic of PEs brings challenges to existing NEE methods, as they cannot well cope with the dual identities of PEs. Therefore, this paper proposes a new model, called PerNee, which extracts nested events mainly based on recognizing PEs. Specifically, PerNee first recognizes the triggers of both inner-nest and outer-nest events and further recognizes the PEs via classifying the relation type between trigger pairs. The model uses prompt learning to incorporate information from both event types and argument roles for better trigger and argument representations to improve NEE performance. Since existing NEE datasets (e.g., Genia11) are limited to specific domains and contain a narrow range of event types with nested structures, we systematically categorize nested events in the generic domain and construct a new NEE dataset, called ACE2005-Nest. Experimental results demonstrate that PerNee consistently achieves state-of-the-art performance on ACE2005-Nest, Genia11, and Genia13. The ACE2005-Nest dataset and the code of the PerNee model are available at https://***/waysonren/PerNee. © 2024 ELRA Language Resource Association: CC BY-NC 4.0.

关键词： Character recognition

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Learning knowledge representation across knowledge graphs 31

Learning knowledge representation across knowledge graphs

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31st AAAI Conference on Artificial Intelligence, AAAI 2017

作者： Cai, Pengshan Li, Wei Feng, Yansong Wang, Yuanzhuo Jia, Yantao CAS Key Laboratory of Network Data Science and Technology Institute of Computing Technology Chinese Academy of Science Beijing China Key Laboratory of Intelligent Information Processing Institute of Computing Technology Chinese Academy of Science Beijing China Institute of Computer Science and Technology Peking University Beijing China

ISBN: (纸本)9781577357865

Distributed knowledge representation learning (KRL) methods encode both entities and relations in knowledge graphs (KG) in a lower-dimensional semantic space, which model relatively dense knowledge graphs well and greatly improve the performance of knowledge graph completion and knowledge reasoning. However, existing KRL methods including Trans(E, H, R, D and Sparse) hardly obtain comparative performances on sparse KGs where most of entities and relations have very low frequencies. Furthermore, all existing methods target at KRL on one knowledge graph independently. The embeddings of different KGs are independent with each other. In this paper, we propose a novel cross-knowledge- graph (cross-KG) KRL method which learns embeddings for two different KGs simultaneously. Through projecting semantic related entities and relations in two KGs to a uniform semantic space, our method could learn better embeddings for sparse KGs by incorporating information from another relatively larger and denser KG. The learned embeddings are also helpful for downstream cross-KGs or cross-linguals tasks like ontology alignment. The experiment results show that our method could significandy outperform corresponding baseline methods on knowledge graph completion on single KG and cross-KG entity prediction and mapping tasks. © 2017, Association for the Advancement of Artificial Intelligence (***). All rights reserved.

关键词： Knowledge graph

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Community discovery method based on complex network of data fusion based on the super network perspective

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International Journal of Computers and Applications 2021年第4期43卷 383-390页

作者： Pei, Li School of Computer Science & Technology Xi‘an University of Posts and Telecommunications Xi‘an China Shaanxi Key Laboratory of Network Data Intelligent Processing Xi‘an University of Posts and Telecommunications Xi‘an China Ministry of Education Key Lab For Intelligent Networks and Network Security Xi‘an Jiaotong University Xi‘an China

To enhance the computational efficiency and precision of community discovery, a community discovery algorithm with the mixed label based on the minimum description length (MDI) of information compression is proposed in this paper. Firstly, the community detection is converted to the information compression problem of seeking for an effective network structure and the quality evaluation function is constructed based on the MDL criterion. Secondly, the community discovery algorithm with heuristic mixed label movement is constructed based on the label node movement algorithm and Louvain community addition algorithm so as to reduce the quality evaluation function. Finally, the simulation experiment in the standard test set and API capture Sina microblog dataset shows that the proposed algorithm is superior to the selected comparison algorithm in computational efficiency and precision. © 2018 Informa UK Limited, trading as Taylor & Francis Group.

关键词： Computational efficiency

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Thematic Event Extraction Based on Event-Related Sentence Detection 1

Thematic Event Extraction Based on Event-Related Sentence De...

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1st IEEE International Conference on Medical Artificial Intelligence, MedAI 2023

作者： Zhang, Xing Guo, Yucan Jin, Xiaolong Liang, Haozhe Guan, Saiping School of Computer Science and Technology University of Chinese Academy of Sciences China Institute of Computing Technology Chinese Academy of Sciences Cas Key Laboratory of Network Data Science and Technology China Institute of Systems Engineering Academy of Military Science Australia

ISBN: (纸本)9798350358780

Existing works in event extraction typically extract event arguments within the sentence scope. However, besides the sentence level, events may also be naturally presented at the document level. A document-level event usually reflects, to some extent, the theme (i.e., the main content) of the document (e.g., electronic medical records and news articles), which is thus referred to as the thematic event. Thematic Event Extraction (TEE) aims to extract the arguments of thematic events. TEE faces a major challenge, i.e., the sparsity and dispersion of arguments, which means that the arguments of a thematic event are dispersed in different sentences of the document. To overcome this challenge, we propose an Event-related Sentence Detection based TEE model, called ESDTEE, which first detects the sentences related to the thematic event and then extracts the arguments only within these detected sentences using existing models. Extensive experiments with comprehensive analyses demonstrate the effectiveness of ESDTEE. © 2023 IEEE.

关键词： Medical computing

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New results on quantum boomerang attacks

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Quantum Information Processing 2023年第4期22卷 1-27页

作者： Zou, Hongkai Zou, Jian Luo, Yiyuan College of Computer and Data Science Fuzhou University Fuzhou China Key Lab of Information Security of Network Systems Fuzhou University Fuzhou China School of Information Sciences and Technology Huizhou University Huizhou China

At SAC 2021, Frixons et al. proposed quantum boomerang attacks that can effectively recover the keys of block ciphers in the quantum setting. Based on their work, we further consider how to quantize the generic boomerang attacks proposed by Biham et al. at FSE 2002, so as to obtain more generic quantum boomerang attacks. Similar to Frixons et al.’s work, we only consider quantum key recovery attacks in the single-key setting. With the help of some famous quantum algorithms, this paper presents two methods to convert the attacks of Biham et al. into some new quantum key recovery attacks. In order to proof our methods, we apply our new ideas to attack Serpent-256 and ARIA-196. To sum up, for Serpent-256, we give valid 9-round and 10-round quantum key recovery attacks respectively. The quantum time complexity of 9-round and 10-round of Serpent-256 is 2 115.43 and 2 126.6 respectively. Furthermore, we show a valid quantum key attack on 6-round ARIA-196 which has a time complexity of 2 89.8 with negligible memory. The time complexity of the above quantum attacks are better than the corresponding classical attacks and quantum generic key recovery attack via Grover’s algorithm. © 2023, The Author(s), under exclusive licence to Springer science+Business Media, LLC, part of Springer Nature.

关键词： Quantum theory

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Graph wavelet neural network 7

Graph wavelet neural network

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7th International Conference on Learning Representations, ICLR 2019

作者： Xu, Bingbing Shen, Huawei Cao, Qi Qiu, Yunqi Cheng, Xueqi CAS Key Laboratory of Network Data Science and Technology Institute of Computing Technology Chinese Academy of Sciences China School of Computer and Control Engineering University of Chinese Academy of Sciences Beijing China

We present graph wavelet neural network (GWNN), a novel graph convolutional neural network (CNN), leveraging graph wavelet transform to address the shortcomings of previous spectral graph CNN methods that depend on graph Fourier transform. Different from graph Fourier transform, graph wavelet transform can be obtained via a fast algorithm without requiring matrix eigendecomposition with high computational cost. Moreover, graph wavelets are sparse and localized in vertex domain, offering high efficiency and good interpretability for graph convolution. The proposed GWNN significantly outperforms previous spectral graph CNNs in the task of graph-based semi-supervised classification on three benchmark datasets: Cora, Citeseer and Pubmed. © 7th International Conference on Learning Representations, ICLR 2019. All Rights Reserved.

关键词： Wavelet transforms

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A Discriminative Semantic Ranker for Question Retrieval 21

A Discriminative Semantic Ranker for Question Retrieval

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11th ACM SIGIR International Conference on Theory of Information Retrieval, ICTIR 2021

作者： Cai, Yinqiong Fan, Yixing Guo, Jiafeng Zhang, Ruqing Lan, Yanyan Cheng, Xueqi University of Chinese Academy of Sciences Beijing China Cas Key Lab of Network Data Science and Technology Institute of Computing Technology Chinese Academy of Sciences Beijing China Institute for Ai Industry Research Tsinghua University Beijing China

ISBN: (纸本)9781450386111

Similar question retrieval is a core task in community-based question answering (CQA) services. To balance the effectiveness and efficiency, the question retrieval system is typically implemented as multi-stage rankers: The first-stage ranker aims to recall potentially relevant questions from a large repository, and the latter stages attempt to re-rank the retrieved results. Most existing works on question retrieval mainly focused on the re-ranking stages, leaving the first-stage ranker to some traditional term-based methods. However, term-based methods often suffer from the vocabulary mismatch problem, especially on short texts, which may block the re-rankers from relevant questions at the very beginning. An alternative is to employ embedding-based methods for the first-stage ranker, which compress texts into dense vectors to enhance the semantic matching. However, these methods often lose the discriminative power as term-based methods, thus introduce noise during retrieval and hurt the recall performance. In this work, we aim to tackle the dilemma of the first-stage ranker, and propose a discriminative semantic ranker, namely DenseTrans, for high-recall retrieval. Specifically, DenseTrans is a densely connected Transformer, which learns semantic embeddings for texts based on Transformer layers. Meanwhile, DenseTrans promotes low-level features through dense connections to keep the discriminative power of the learned representations. DenseTrans is inspired by DenseNet in computer vision (CV), but poses a new way to use the dense connectivity which is totally different from its original design purpose. Experimental results over two question retrieval benchmark datasets show that our model can obtain significant gain on recall against strong term-based methods as well as state-of-the-art embedding-based methods. © 2021 ACM.

关键词： Semantics

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Focus on the Stability of Large Systems: Toward Automatic Prediction and Analysis of Vulnerability Threat Intelligence 6

Focus on the Stability of Large Systems: Toward Automatic Pr...

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6th IEEE International Conference on data science in Cyberspace, DSC 2021

作者： Song, Shiwen Wu, Qiong Zheng, Xin Wang, Peng Dou, Yuchen Li, Zhongwen Zhai, Lidong Institute Of Information Engineering Chinese Academy Of Sciences BeiJing China Bag Data Academy Zhongke Beijing China Institute Of Computing Technology Chinese Academy Of Sciences Cas Key Laboratory Of Network Data Science And Technology Beijing China

ISBN: (纸本)9781665418157

With the increase in the number of users and business volume, the business systems of Internet companies are becoming more and more complex, resulting in a surge in the number of alarms. A large number of dirty alarms add a huge workload to security operations, which indirectly pose a large number of threats to business systems. At present, most systems use the method of accessing third-party Threat Intelligence to assist operators to realize automatic handling of alarms. However, this method has lagging and accuracy problems, making this work always difficult to meet the requirements of fast and accurate. This article proposes a new method for gathering vulnerability Threat Intelligence, which can obtain vulnerability information in advance of security announcements issued by security vendors. By analyzing the vulnerability disclosure process, this method obtains vulnerability information from the original source submitted by open source mail group, of developers. We used NLP technology and XGBoost model to automatically analyze the vulnerability information, and finally generate FINTEL. The experimental result shows that this method has an accuracy of 93%, and can obtain vulnerability information 10h to 7 days before security vendors release. The scope of application covers all open source code repositories and some closed source repositories. © 2021 IEEE.

关键词： Open source software

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Optimal algorithm for profiling dynamic arrays with finite values 22

Optimal algorithm for profiling dynamic arrays with finite v...

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22nd International Conference on Extending database technology, EDBT 2019

作者： Yang, Dingcheng Yu, Wenjian Deng, Junhui Liu, Shenghua BNRist Dept. Computer Science and Tech. Tsinghua Univ. Beijing China CAS Key Lab. Network Data Science and Tech. Inst. Computing Technology Chinese Academy of Sciences Beijing China

ISBN: (纸本)9783893180813

How can one quickly answer the most and top popular objects at any time, given a large log stream in a system of billions of users? It is equivalent to find the mode and top-frequent elements in a dynamic array corresponding to the log stream. However, most existing work either restrain the dynamic array within a sliding window, or do not take advantages of only one element can be added or removed in a log stream. Therefore, we propose a profiling algorithm, named S-Profile, which is of O(1) time complexity for every updating of the dynamic array, and optimal in terms of computational complexity. With the profiling results, answering the queries on the statistics of dynamic array becomes trivial and fast. With the experiments of various settings of dynamic arrays, our accurate S-Profile algorithm outperforms the existing methods, showing at least 2X speedup to the heap based approach and 13X speedup to the balanced tree based approach. © 2019 Copyright held by the owner/author(s).

关键词： Trees (mathematics)

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One Comment from One Perspective: An Effective Strategy for Enhancing Automatic Music Comment 28

One Comment from One Perspective: An Effective Strategy for ...

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28th International Conference on Computational Linguistics, COLING 2020

作者： Huo, Tengfei Liu, Zhiqiang Zhang, Jinchao Zhou, Jie CAS Key Lab of Network Data Science and Technology China Institute of Computing Technology Chinese Academy of Sciences China University of Chinese Academy of Sciences Beijing China Pattern Recognition Center WeChat AI Tencent Inc. China

ISBN: (纸本)9781952148279

The automatic generation of music comments is of great significance for increasing the popularity of music and the music platform’s activity. In human music comments, there exists high distinction and diverse perspectives for the same song. In other words, for a song, different comments stem from different musical perspectives. However, to date, this characteristic has not been considered well in research on automatic comment generation. The existing methods tend to generate common and meaningless comments. In this paper, we propose an effective multi-perspective strategy to enhance the diversity of the generated comments. The experiment results on two music comment datasets show that our proposed model can effectively generate a series of diverse music comments based on different perspectives, which outperforms state-of-the-art baselines by a substantial margin. © 2020 COLING 2020 - 28th International Conference on Computational Linguistics, Proceedings of the Conference. All rights reserved.

关键词： Music

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