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A Review of dataset and labeling methods for causality extraction 28

A Review of dataset and labeling methods for causality extra...

引用

28th International Conference on computational Linguistics, COLING 2020

作者： Xu, Jinghang Zuo, Wanli Liang, Shining Zuo, Xianglin College of Computer Science and Technology Jilin University China Key Laboratory of Symbol Computation and Knowledge Engineering of Ministry of Education China

ISBN: (纸本)9781952148279

Causality represents the most important kind of correlation between events. Extracting causality from text has become a promising hot topic in NLP. However, there is no mature research systems, evaluation rules and datasets for public evaluation. Moreover, there is a lack of unified causal sequence labeling methods, which constitute the key factors that hinder the progress of causality extraction research. We survey the limitations and shortcomings of existing causality research field comprehensively from the aspects of basic concepts, extraction methods, experimental data, and labeling methods, so as to provide reference for future research on causality extraction. We summarize the existing causality datasets, explore their practicability and extensibility from multiple perspectives. Aiming at the problem of causal sequence labeling, we analyze the existing methods of causal sequence labeling, with a summarizations of its regulation. Multiple candidate causal labeling sequences are put forward according to labeling controversy to explore the optimal labeling method through experiments, and suggestions are provided for selecting labeling method. © 2020 COLING 2020 - 28th International Conference on computational Linguistics, Proceedings of the Conference. All rights reserved.

关键词： Extraction

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Modeling and refining directional relations based on fuzzy mathematical morphology

Modeling and refining directional relations based on fuzzy m...

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High-Power Diode Laser Technology and Applications IV

作者： Sun, Haibin Li, Wenhui Key Laboratory of Symbol Computation Knowledge Engineering of the Ministry of Education College of Computer Science and Technology Jilin University Changchun 130012 China

ISBN: (纸本)0819461466

In this paper, we investigate the deficiency of Goyal and Egenhofer's method for modeling cardinal directional relations between simple regions and provide the computational model based on the concept of mathematical morphology, which can be a complement and refinement of Goyal and Egenhofer's model for crisp regions. Based on fuzzy set theory, we extend Goyal and Egenhofer's model to handle fuzziness and provide a computational model based on alpha-morphology, which combines fuzzy set theory and mathematical morphology, to refine the fuzzy cardinal directional relations. Then the computational problems are investigated. We also give an example of spatial configuration in 2-dimensional discrete space. The experiment results confirm the cognitive plausibility of our computational models. © Springer-Verlag Berlin Heidelberg 2005.

关键词： Fuzzy sets

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Qualitative spatial relationships cleaning for spatial data mining

Qualitative spatial relationships cleaning for spatial data ...

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International Conference on Machine Learning and Cybernetics, ICMLC 2005

作者： Sun, Hai-Bin Li, Wen-Hui Key Laboratory of Symbol Computation and Knowledge Engineering of the Ministry of Education College of Computer Science and Technology Jilin University Changchun 130012 China

ISBN: (纸本)078039092X

In this article, we investigate the problem of preparing qualitative spatial relations before implementing spatial data mining by checking consistency in a constraint network, which includes topological and cardinal directional relations between pairs of spatial objects. We aim to explore potential spatial relations and possible inconsistency among the data of relationships for enforcing the correctness of spatial data mining. This task is carried out through qualitative spatial reasoning method, specifically consistency checking. We try to lay the theoretical foundation for this kind of problem. Instead of using conventional composition tables, we investigate the interactions between topological and cardinal directional relations with the aid of rules. These rules are shown to be sound, i.e. the deductions are logically correct. Based on these rules, an improved constraint propagation algorithm is introduced to enforce the path consistency. An example is presented to show the utility of these rules. © 2005 IEEE.

关键词： Data mining

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Communities detection with applications to real-world networks analysis

Communities detection with applications to real-world networ...

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2011 7th International Conference on computational Intelligence and Security, CIS 2011

作者： Yang, Bo School of Computer Science and Technology Key Laboratory of Symbol Computation and Knowledge Engineering of the Ministry of Education Jilin University Changchun 130012 China

ISBN: (纸本)9780769545844

Community structure is one of non-trivial topological properties ubiquitously demonstrated in real-world complex networks. Related theories and approaches are of fundamental importance for understanding the functions of networks. Previously, we have proposed a probabilistic algorithm called the NCMA to efficiently as well as effectively mine communities from real-world networks. Here, we show that the NCMA can be readily extended and applied to address a wide range of network oriented applications beyond community detection including ranking, characterizing and searching real-world networks. © 2011 IEEE.

关键词： Complex networks

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Super-resolution reconstruction from fluorescein angiogram sequences

Super-resolution reconstruction from fluorescein angiogram s...

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International Conference on Intelligent Computing, ICIC 2005

作者： Guo, Xiaoxin Xu, Zhiwen Lu, Yinan Liu, Zhanhui Pang, Yunjie Key Laboratory of Symbol Computation and Knowledge Engineering Jilin University Ministry of Education Qianjin Road 10# Changchun 130012 China

ISBN: (纸本)9783540282266

Intensity degradations are a familiar problem for fluorescein angiogram sequences. In this paper, we attempt to super-resolve a fluorescein angiogram, and to keep the high intensity pixels from degrading. To this end, we incorporate a new constraint, called intensity constraint, to Miller's regularization formulation with a smoothness constraint. Considering the specified requirement for fluorescein angiograms, we also modify the Q-th order converging algorithm for implementation purpose. In our scheme, including its formulation and implementation, super-resolution reconstruction can not only handle the traditional problems, such as blur, decimation, and noise, but also achieve an important feature, intensity preservation. The experiments show that our approach has satisfactory results in the two aspects. © Springer-Verlag Berlin Heidelberg 2005.

关键词： Image reconstruction

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Combining GCN and Bi-LSTM for Protein Secondary Structure Prediction

Combining GCN and Bi-LSTM for Protein Secondary Structure Pr...

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2021 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2021

作者： Jin, Hailong Du, Wei Gu, Jiawei Zhang, Tianhao Shi, Xiaohu College of Computer Science and Technology of Jilin University Changchun China Laboratory of Symbol Computation and Knowledge Engineering of the Ministry of Education Changchun China

ISBN: (纸本)9781665401265

Protein secondary structure prediction is still a challenging task in bioinformatics, especially for 8-state (Q8) classification. To address this problem, we have proposed a deep learning based model by integrating graph convolutional network(GCN) and bidirectional long short-term memory (Bi-LSTM) network in this paper. In the model, GCN is utilized to synthesize the information of amino acids and their interactions, while Bi-LSTM has strong ability to capture the long-range dependencies of amino acids. For sequence representation, a new protein embedding derived by ProtTrans is used instead of the traditional amino acid one-hot encoding, together with evolutionary features of PSSM and HHM profiles. Amino acid contact potential derived from SPOTContact-Helical is used to construct amino acid graph. To verify the effectiveness of our proposed model, it is applied to several benchmark datasets, and obtained 78.05%, 76.81% 72.84%, 74.46% and 76.04% Q8 accuracy on CASP10, CASP11, CASP12, CB513 and TS115 datasets, respectively. Compared with 8 state-of-the-art competitions, our model obtained the best performance in most of datasets. © 2021 IEEE.

关键词： Amino acids

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Nutrition feature basis of food classification in traditional chinese medicine 7

Nutrition feature basis of food classification in traditiona...

引用

7th International Conference on Big Data Computing and Communications, BigCom 2021

作者： Zhao, Fucheng Han, Xiaosong Zhang, Yikun Niu, Peiyu Cheng, Jun Yi, Wan Jilin University Key Laboratory for Symbol Computation and Knowledge Engineering of National Education Ministry College of Computer Science and Technology Changchun130012 China

ISBN: (纸本)9781665442527

In TCM (Traditional Chinese Medicine) theory, the cold-and-hot property of food is considered as an important information to guide people's daily diet and keep them healthy. But the classification of this property mostly relies on experience from medical books, which lacks corresponding molecular chemistry basis and makes differentiation very difficult for ordinary people. To solve above question, in this paper, we collected 500 kinds of food and their nutrients, used ANOVA algorithm for feature selection to find out the nutrient feature that have a greater correlation with the cold and hot properties of food, and then used LightGBM algorithm to classify the processed data set. Finally, we constructed a reliable and accurate classification model and found the related nutritional elements are Tyrosine, Arginine, Thiamine, Vitamin B6 and Selenium. © 2021 IEEE.

关键词： Machine learning

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Making Simple Tabular Reduction works on negative table constraints

Making Simple Tabular Reduction works on negative table cons...

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27th AAAI Conference on Artificial Intelligence, AAAI 2013

作者： Li, Hongbo Liang, Yanchun Guo, Jinsong Li, Zhanshan Key Laboratory for Symbol Computation and Knowledge Engineering of National Education Ministry College of Computer Science and Technology Jilin University Changchun 130012 China

ISBN: (纸本)9781577356158

Simple Tabular Reduction algorithms (STR) work well to establish Generalized Arc Consistency (GAC) on positive table constraints. However, the existing STR algorithms are useless for negative table constraints. In this work, we propose a novel STR algorithm and its improvement, which work on negative table constraints. Our preliminary experiments are performed on some random instances and a certain benchmark instances. The results show that the new algorithms outperform GAC-valid and the MDD-based GAC algorithm. Copyright © 2013, Association for the Advancement of Artificial Intelligence (***). All rights reserved.

关键词： Benchmarking

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PNESR-DDI: An Effective Drug-Drug Interaction Prediction Model Based on Pretraining Method and Enhanced Subgraph Reconstruction

PNESR-DDI: An Effective Drug-Drug Interaction Prediction Mod...

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2024 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2024

作者： Chen, Ke Han, Xiaosong Li, Xiaoran Liang, Yanchun Xu, Dong Guan, Renchu Jilin University Key Laboratory for Symbol Computation and Knowledge Engineering of National Education Ministry College of Software Changchun China Jilin University Key Laboratory for Symbol Computation and Knowledge Engineering of National Education Ministry College of Computer Science and Technology Changchun China Zhuhai College of Science and Technology Zhuhai Laboratory of Key Laboratory of Symbol Computation and Knowledge Engineering of Ministry of Education School of Computer Science Zhuhai China University of Missouri Christopher S. Bond Life Sciences Center Department of Electrical Engineering and Computer Science Columbia United States

ISBN: (纸本)9798350386226

Drug-Drug Interaction (DDI) task plays a crucial role in clinical treatment and drug development. Recently, deep learning methods have been successfully applied for DDI prediction. However, training deep learning models always need large amount of data, while known DDIs are scarce. To address this challenge, a graph neural network-based DDI prediction model named PNESR-DDI is proposed, which compensates for the lack of DDIs by enriching drug representations. First, to obtain initial node representations that incorporate rich semantic information from the biomedical knowledge graph (KG), a link prediction pre-training method on external KG is proposed in the node embedding pre-training module. Then, considering the large scale of the KG, subgraph extraction for the target drug pairs is introduced to reduce noise and decrease computational complexity in the subgraph anchoring module. After that, the subgraph is updated, and node similarities are propagated in the subgraph reconstruction module. Based on the node similarity scores, the subgraph is pruned and reconstructed, which adjusts node representations to be more conducive to DDI prediction. Finally, the drug embeddings, subgraph representations, and drug fingerprint features are concatenated to predict DDIs. PNESRDDI is evaluated on two benchmark DDI datasets: DrugBank and TWOSIDES. Experiment results show that PNESR-DDI achieves better performance than baselines. Ablation results validate the effectiveness of the pre-training method and the adaptive subgraph reconstruction strategy. © 2024 IEEE.

关键词： knowledge graph

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Fast kernel for calculating structural information similarities

Fast kernel for calculating structural information similarit...

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3rd IEEE International Conference on Intelligent Systems

作者： Wei, Jin-Mao Wang, Shu-Qin Wang, Jing You, Jun-Ping Institute of Computational Intelligence Northeast Normal University Open Symbol Computation and Knowledge Engineering Laboratory of State Education Jilin University Changchun Jilin 130024 China School of Mathematics and Statistics Northeast Normal University Changchun Jilin 130024 China

ISBN: (纸本)9781424401956

Structural similarity computation plays a crucial role in many applications such as in searching similar documents, in comparing chemical compounds, in finding genetic similarities, etc. We propose in this paper to use structural information content (SIC) for measuring structural information, considering both the nodes and edges of trees. We utilize a binary encoding approach for assigning the weights of different layer nodes and determining if some tree is a subtree of another tree. By defining a fast kernel and recursively computing SICs, we evaluate the structural information similarities of data trees to pattern trees. In the paper, we present the algorithm for calculating SICs with computation complexity of O(n), and use simple examples to instantiate the performance of the proposed method..

关键词： structural information content structural similarity structural pattern analysis web mining

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