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4th International workshop on Multimodal Brain Image Analysis (MBIA) / 7th International workshop on Mathematical Foundations of Computational Anatomy (MFCA)

作者： Tang, Haoteng Guo, Lei Dennis, Emily Thompson, Paul M. Huang, Heng Ajilore, Olusola Leow, Alex D. Zhan, Liang Univ Pittsburgh Dept Elect & Comp Engn Pittsburgh PA 15213 USA Harvard Univ Harvard Med Sch Boston MA 02115 USA Univ Southern Calif Inst Neuroimaging & Informat Los Angeles CA 90007 USA Univ Illinois Dept Psychiat Chicago IL 60612 USA

ISBN: (纸本)9783030332266;9783030332259

Mild Cognitive Impairment (MCI) is a clinically intermediate stage in the course of Alzheimer's disease (AD). MCI does not always lead to dementia. Some MCI patients may stay in the MCI status for the rest of their life, while others will develop AD eventually. Therefore, classification methods that help to distinguish MCI from earlier or later stages of the disease are important to understand the progression of AD. In this paper, we propose a novel computational framework - named Augmented graph Embedding, or AGE - to tackle this challenge. In this new AGE framework, the random walk approach is first applied to brain structural networks derived from diffusion-weighted MRI to extract nodal feature vectors. A technique adapted from natural language processing is used to analyze these nodal feature vectors, and a multimodal augmentation procedure is adopted to improve classification accuracy. We validated this new AGE framework on data from the Alzheimer's Disease Neuroimaging Initiative (ADNI). Results show advantages of the proposed framework, compared to a range of existing methods.

关键词： Mild Cognitive Impairment Brain structural network graph embedding Random walk natural language processing Data augmentation

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IEEE 35th International Conference on Data Engineering (ICDE)

作者： Yang, Chaoqun Zhu, Yuanyuan Zhong, Ming Li, Rongrong Wuhan Univ Sch Comp Sci Wuhan Peoples R China

ISBN: (纸本)9781728108902

Computing semantic similarity between concepts is a fundamental task in natural language processing and has a large variety of applications. In this paper, first of all, we will review and analyze existing semantic similarity computation methods in knowledge graphs. Through the analysis of these methods, we find that existing works mainly focus on the context features of concepts which indicate the position or the frequency of the concepts in the knowledge graphs, such as the depth of terms, information content of the terms, or the distance between terms, while a fundamental part to describe the meaning of the concept, the synsets of concepts, are neglected for a long term. Thus, in this paper, we propose a new method to compute the similarity of concepts based on their extended synsets. Moreover, we propose a general hybrid framework, which can combine our new similarity measure based on extended synsets with any of existing context feature based semantic similarities to evaluate the concepts more accurately. We conducted experiments on five well-known datasets for semantic similarity evaluation, and the experimental results show that our general framework can improve most of existing methods significantly.

关键词： semantic similarity knowledge graph synonym

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Unsupervised neural networks for automatic Arabic text summarization using document clustering and topic modeling

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EXPERT SYSTEMS WITH APPLICATIONS 2021年 172卷 114652-114652页

作者： Alami, Nabil Meknassi, Mohammed En-nahnahi, Noureddine El Adlouni, Yassine Ammor, Ouafae Sidi Mohamed Ben Abdellah Univ Fac Sci Dhar El Mahraz LISAC Lab POB 1796 Fes 30003 Morocco Sidi Mohamed Ben Abdellah Univ Fac Sci & Technol Lab Modeling & Math Struct LMSM BP 2202 Fes 30000 Morocco

Humans must easily handle the vast amounts of data being generated by the revolution of information technology. Thus, Automatic Text summarization has been applied to various domains in order to find the most relevant information and make critical decisions quickly. In the context of Arabic, text summarization techniques suffer from several problems. first, most existing methods do not consider the context or domain to which the document belongs. Second, the majority of the existing approaches are based on the traditional bag-of-words representation, which involves high dimensional and sparse data, and makes it difficult to capture relevant information. Third, research in Arabic Text summarization is fairly small and only recently compared to that on Anglo-Saxon and other languages due to the shortage of Arabic corpora, resources, and automatic processing tools. In this paper, we try to overcome these limitations by proposing a new approach using documents clustering, topic modeling, and unsupervised neural networks in order to build an efficient document representation model. first, a new document clustering technique using Extreme learning machine is performed on large text collection. Second, topic modeling is applied to documents collection in order to identify topics present in each cluster. Third, each document is represented in a topic space by a matrix where rows represent the document sentences and columns represent the cluster topics. The generated matrix is then trained using several unsupervised neural networks and ensemble learning algorithms in order to build an abstract representation of the document in the concept space. Important sentences are ranked and extracted according to a graph model with a redundancy elimination component. The proposed approach is evaluated on Essex Arabic Summaries Corpus and compared against other Arabic text summarization approaches using ROUGE measure. Experimental results showed that the models trained on topic represen

关键词： Arabic text summarization natural language processing Deep learning Neural networks Clustering Topic modeling

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Pattern-based acquisition of scientific entities from scholarly article titles

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arXiv 2021年

作者： D’Souza, Jennifer Auer, Sören TIB Leibniz Information Centre for Science and Technology L3S Research Center Leibniz University of Hannover Hannover Germany

We describe a rule-based approach for the automatic acquisition of scientific entities from scholarly article titles. Two observations motivated the approach: (i) noting the concentration of an article’s contribution information in its title;and (ii) capturing information pattern regularities via a system of rules that alleviate the human annotation task in creating gold standards that annotate single instances at a time. We identify a set of lexico-syntactic patterns that are easily recognizable, that occur frequently, and that generally indicates the scientific entity type of interest about the scholarly contribution. A subset of the acquisition algorithm is implemented for article titles in the Computational Linguistics (CL) scholarly domain. The tool called ORKG-Title-Parser, in its first release, identifies the following six concept types of scientific terminology from the CL paper titles, viz. research problem, solution, resource, language, tool, and method. It has been empirically evaluated on a collection of 50,237 titles that cover nearly all articles in the ACL Anthology. It has extracted 19,799 research problems;18,111 solutions;20,033 resources;1,059 languages;6,878 tools;and 21,687 methods at an average extraction precision of 75%. The code and related data resources are publicly available at https://gitlab. com/TIBHannover/orkg/orkg-title-parser. Finally, in the article, we discuss extensions and applications to areas such as scholarly knowledge graph (SKG) creation. © 2021, CC BY.

关键词： natural language processing systems

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graph classification algorithm based on graph structure embedding

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EXPERT SYSTEMS WITH APPLICATIONS 2020年 161卷 113715-113715页

作者： Ma, Tinghuai Pan, Qian Wang, Hongmei Shao, Wenye Tian, Yuan Al-Nabhan, Najla Nanjing Univ Informat Sci & Technol Sch Comp & Software Nanjing 210044 Jiangsu Peoples R China Nanjing Inst Technol Nanjing 211167 Jiangsu Peoples R China King Saud Univ Coll Comp & Informat Sci Riyadh 11362 Saudi Arabia

With the application of data mining in many fields such as information science, bioinformatics, and network intrusion detection, more and more data are showing new features such as strong structuration and complex relationships between data. As a complex data structure, a graph can be used to describe the relationship between things. Traditional graph classification methods based on graph feature vector construction need to select a feature vector construction criterion in advance, such as graph-based theoretical indicators or graph-based topology occurrences, and then extract features from each graph in the graph set according to the designated criterion. However, the construction method of the graph feature vector is easy to lose the graph structural information and requires strong professional knowledge. Inspired by the Word2Vec and Doc2Vec models in the natural language processing (NLP), this paper first constructs a "word list" of graph data consisting of subgraphs. Then a neural network for training graph embedding is designed with the graph itself as its input, and the "word" in the graph and the attribute features of the graph are used as its output, so that the neural network automatically learns the graph embedding corresponding to each graph. The graph embedding not only reflects the features of the graph itself but also includes the relative relationship among graphs. Finally, on the basis of the well-trained graph embedding, the common classifier can be used to classify graphs. based on real-world bioinformatics and social data sets, the experiments demonstrate that the proposed graph classification algorithm has advantages over the existing graph classification algorithms based on feature vector construction. (c) 2020 Elsevier Ltd. All rights reserved.

关键词： graph classification graph embedding Neural network

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Parallel Data-Local Training for Optimizing Word2Vec Embeddings for Word and graph Embeddings 5

Parallel Data-Local Training for Optimizing Word2Vec Embeddi...

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5th IEEE/ACM workshop on Machine Learning in High Performance Computing Environments (MLHPC)

作者： Moon, Gordon E. Newman-Griffis, Denis Kim, Jinsung Sukumaran-Rajam, Aravind Fosler-Lussier, Eric Sadayappan, P. Ohio State Univ Comp Sci & Engn Columbus OH 43210 USA Univ Utah Sch Comp Salt Lake City UT USA

ISBN: (纸本)9781728159850

The Word2Vec model is a neural network-based unsupervised word embedding technique widely used in applications such as natural language processing, bioinformatics and graph mining. As Word2Vec repeatedly performs Stochastic Gradient Descent (SGD) to minimize the objective function, it is very compute-intensive. However, existing methods for parallelizing Word2Vec are not optimized enough for data locality to achieve high performance. In this paper, we develop a parallel data-locality-enhanced Word2Vec algorithm based on Skip-gram with a novel negative sampling method that decouples loss calculation with positive and negative samples;this allows us to efficiently reformulate matrix-matrix operations for the negative samples over the sentence. Experimental results demonstrate our parallel implementations on multi-core CPUs and GPUs achieve significant performance improvement over the existing state-of-the-art parallel Word2Vec implementations while maintaining evaluation quality. We also show the utility of ourWord2Vec implementation within the Node2Vec algorithm which accelerates embedding learning for large graphs.

关键词： Parallel Machine Learning Unsupervised Learning Learning Latent Representations Parallel Word2Vec Node2Vec Word Embedding graph Embedding

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盾构施工知识自动抽取与建模

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土木工程与管理学报 2023年第2期40卷 130-137页

作者：刘江波徐宏王浩茜徐晟长安大学经济与管理学院陕西西安710064 长安大学陕西省“一带一路”重大交通基础设施数字化建设与管理学科创新引智基地陕西西安710064 中铁一局集团有限公司陕西西安710054

建立领域知识图谱有利于解决盾构施工知识分散,检索效率低下的问题,但现有知识抽取方法较为复杂,且较多地依赖于人工参与,因此,提出一种以规范文本为对象的知识自动抽取方法。该方法在条文预处理和条文分类的前提下,以现有自然语言处理... 详细信息

建立领域知识图谱有利于解决盾构施工知识分散,检索效率低下的问题,但现有知识抽取方法较为复杂,且较多地依赖于人工参与,因此,提出一种以规范文本为对象的知识自动抽取方法。该方法在条文预处理和条文分类的前提下,以现有自然语言处理平台预训练模型提供的语义角色标注(SRL)结果为基础,通过后优化处理和模式匹配实现知识抽取,形成不同类型条文关键知识要素的结构化整理。最后,以GB 50446—2017《盾构法隧道施工及验收规范》为例,进行知识抽取并建立了盾构施工知识图谱。结果表明,该方法能在一定程度上降低中文规范文本的抽取难度,为领域知识图谱的建立提供有效的数据支撑,从而促进盾构施工知识的高效积累、共享和重用。

关键词：知识抽取语义角色标注本体知识图谱

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基于知识图谱与依存句法分析的井控领域文本相似度计算

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计算机与数字工程 2023年第8期51卷 1732-1737页

作者：尚福华张洪铭解红涛东北石油大学计算机与信息技术学院大庆163318

针对油田井控领域文本相似度计算时,由于语义信息不足导致相似度计算结果准确度不高的问题。论文提出一种基于知识图谱和依存句法分析的文本相似度计算方法。在该方法中,首先利用依存句法技术获取文本核心关系词对,然后基于井控知识图... 详细信息

针对油田井控领域文本相似度计算时,由于语义信息不足导致相似度计算结果准确度不高的问题。论文提出一种基于知识图谱和依存句法分析的文本相似度计算方法。在该方法中,首先利用依存句法技术获取文本核心关系词对,然后基于井控知识图谱和同义词词林对计算不同的关系词对间的相似度,再结合基于词向量的句子相似度计算,分配权重,最后获取井控文本的综合相似度。实验结果表明:与现有方法相比,该方法融合了文本在词和句子级别上的信息,更好地对文本语义进行识别,较大地提高了油田井控领域文本之间相似度计算结果准确率。

关键词：文本相似度自然语言处理知识图谱依存句法分析

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Analyses of characters in dramatic works by using document embeddings

Analyses of characters in dramatic works by using document e...

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2020 workshop on Digital Humanities and natural language processing, DH and NLP 2020

作者： Can Yavuz, Mehmet Faculty of Engineering and Natural Science Sabancı University Tuzla Physics Department Boğaziçi University Bebek İstanbul Turkey

Shakespearean tragedies show clear antagonisms and the resolutions are rational which means they obey the Aristotelian unity-of-action principle. Any tragic play must rely upon its own movement. Therefore, it is complete and there should not be extra characters written only for the sake of resolution, so called"Deus-ex-Machina". In this work, Deus-ex-Machina characters are automatically detected using machine learning methods. We first train unsupervised Doc2Vec network by using all plays of Shakespeare. Then, we collected all the lines uttered by each character in a separate document and extracted the document vectors. Thus, each character is represented with a vector in the semantic space of Shakespeare. We measure the semantic similarity between characters using the cosine difference, the angle between normalized vectors of each character document and we observe characters form a cluster. According to this work, it is possible to detect Deus-ex-Machina characters. Examples of strong unity-of-action principle plays could be demonstrated as well as distinct characters. Dis/similar characters between the plays could also be shown. Copyright © 2020 for this paper by its authors. Use permitted under Creative Commons License Attribution 4.0 International (CC BY 4.0). DHandNLP, 2 March 2020, Evora, Portugal.

关键词： Embeddings

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An Causal XAI Diagnostic Model for Breast Cancer based on Mammography Reports

An Causal XAI Diagnostic Model for Breast Cancer Based on Ma...

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IEEE International Conference on Bioinformatics and Biomedicine (BIBM)

作者： Dehua Chen Hongjin Zhao Jianrong He Qiao Pan Weiliang Zhao School Computer Science and Technology Donghua University Shanghai China Comprehensive Breast Health Center Ruijin Hospital Shanghai China School Computer Science and Technology Macquarie University Sydney Australia

ISBN: (纸本)9781665429825

Breast cancer has become one of the most common malignant tumors in women worldwide, and it seriously threatens women’s physical and mental health. In recent years, with the development of Artificial Intelligence(AI) and the accumulation of medical data, AI has begun to be deeply integrated with mammography, MRI, ultrasound, etc. to assist physicians in disease diagnosis. However, the existing breast cancer diagnosis model based on Computer Vision(CV) is greatly affected by the image quality; on the other hand, the breast cancer diagnosis model based on natural language processing(NLP) cannot effectively extract the semantic information of the mammography report. The lack of model interpretability also makes the existing diagnostic models have low confidence. In this paper, we proposed Breast Cancer Causal XAI Diagnostic Model(BCCXDM). Specifically, we first structured the mammography report. Then find the causal graph based on the structured table. We combine the existing tabular learning method TabNet with causal graphs(Causal-TabNet) to enable reasoning in the graphs to preserve the correlation between features. More importantly, we use GNN and node transition probability to aggregate node information. We evaluate our model on the real-world mammography report, and compare it with other popular interpretable methods. The experimental results show that our interpretable results are closer to the diagnostic criteria of clinicians.

关键词： Ultrasonic imaging Computational modeling Aggregates Semantics natural languages Transforms Predictive models

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