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Short Text Topic Modeling Techniques, Applications, and Performance: A Survey

IEEE Transactions on knowledge and data engineering 2022年第3期34卷 1427-1445页

作者： Qiang, Jipeng Qian, Zhenyu Li, Yun Yuan, Yunhao Wu, Xindong Department of Computer Science Yangzhou University Jiangsu225009 China Key Laboratory of Knowledge Engineering with Big Data Ministry of Education Hefei University of Technology Hefei Anhui230026 China Mininglamp Academy of Sciences Minininglamp Beijing China

Analyzing short texts infers discriminative and coherent latent topics that is a critical and fundamental task since many real-world applications require semantic understanding of short texts. Traditional long text topic modeling algorithms (e.g., PLSA and LDA) based on word co-occurrences cannot solve this problem very well since only very limited word co-occurrence information is available in short texts. Therefore, short text topic modeling has already attracted much attention from the machine learning research community in recent years, which aims at overcoming the problem of sparseness in short texts. In this survey, we conduct a comprehensive review of various short text topic modeling techniques proposed in the literature. We present three categories of methods based on Dirichlet multinomial mixture, global word co-occurrences, and self-aggregation, with example of representative approaches in each category and analysis of their performance on various tasks. We develop the first comprehensive open-source library, called STTM, for use in Java that integrates all surveyed algorithms within a unified interface, benchmark datasets, to facilitate the expansion of new methods in this research field. Finally, we evaluate these state-of-the-art methods on many real-world datasets and compare their performance against one another and versus long text topic modeling algorithm. © 2020 IEEE.

关键词： Surveys

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Learning Inter-Entity Interaction for Few-Shot knowledge Graph Completion

Learning Inter-Entity Interaction for Few-Shot Knowledge Gra...

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2022 Conference on Empirical Methods in Natural Language Processing, EMNLP 2022

作者： Li, Yuling Yu, Kui Huang, Xiaoling Zhang, Yuhong Key Laboratory of Knowledge Engineering with Big Data Ministry of Education Hefei China School of Computer Science and Information Enginerring Hefei University of Technology China

Few-shot knowledge graph completion (FKGC) aims to infer unknown fact triples of a relation using its few-shot reference entity pairs. Recent FKGC studies focus on learning semantic representations of entity pairs by separately encoding the neighborhoods of head and tail entities. Such practice, however, ignores the inter-entity interaction, resulting in low-discrimination representations for entity pairs, especially when these entity pairs are associated with 1-to-N, N-to-1, and N-to-N relations. To address this issue, this paper proposes a novel FKGC model, named Cross-Interaction Attention Network (CIAN) to investigate the inter-entity interaction between head and tail entities. Specifically, we first explore the interactions within entities by computing the attention between the task relation and each entity neighbor, and then model the interactions between head and tail entities by letting an entity to attend to the neighborhood of its paired entity. In this way, CIAN can figure out the relevant semantics between head and tail entities, thereby generating more discriminative representations for entity pairs. Extensive experiments on two public datasets show that CIAN outperforms several state-of-the-art methods. The source code is available at https://***/cjlyl/FKGC-CIAN. © 2022 Association for Computational Linguistics.

关键词： knowledge graph

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H-tree: A hybrid structure for confidence computation in probabilistic databases

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14th Asia Pacific Web Technology Conference, APWeb 2012

作者： Zhang, Qian Qin, Biao Wang, Shan Key Laboratory of Data Engineering and Knowledge Engineering Renmin University of China Ministry of Education Beijing 100872 China School of Information Renmin University of China Beijing 100872 China

ISBN: (纸本)9783642292521

Probabilistic database has become a popular tool for uncertain data management. Most work in the area is focused on efficient query processing and has two main directions, accurate or approximate evaluation. In recent work for conjunctive query without self-joins on a tuple-independent probabilistic database, query evaluation is equivalent to computing marginal probabilities of boolean formulas associated with query results. If formulas can be factorized into a read-once form where every variable appears at most once, confidence computation is reduced to a tractable problem that can be evaluated in linear time. Otherwise, it is regarded as a NP-hard problem and need to be evaluated approximately. In this paper, we propose a framework that evaluates both tractable and NP-hard conjunctive queries efficiently. First, we develop a novel structure H-tree, where boolean formulas are decomposed to small partitions which are either read-once or NP-hard. Then we propose algorithms for building H-tree and parallelizing (approximate) confidence computation. We also propose fundamental theorems to ensure the correctness of our approaches. Performance experiments demonstrate the benefits of H-tree, especially for approximate confidence evaluation on NP-hard queries. © 2012 Springer-Verlag Berlin Heidelberg.

关键词： Query processing

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A neural network based forecasting method for the unemployment rate prediction using the search engine query data

A neural network based forecasting method for the unemployme...

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2011 8th IEEE International Conference on e-Business engineering, ICEBE 2011

作者： Xu, Wei Zheng, Tingting Li, Ziang Key Laboratory of Data Engineering and Knowledge Engineering Renmin University of China Ministry of Education Beijing 100872 China School of Information Renmin University of China Beijing 100872 China

ISBN: (纸本)9780769545189

Unemployment rate prediction has become critically important, because it can help government to make decision and design policies. In recent years, forecast of unemployment rate attracts much attention from governments, organizations, and research institutes, and researchers. Recently, a novel method using search engine query data to forecast unemployment was proposed by scholars. In this paper, a data mining based framework using web information is introduced for unemployment rate prediction. Under the framework, a neural network method, as one of the most effective data mining tools, is developed to forecast unemployment trend using search engine query data. In the proposed method, search engine query data related with employment activities is firstly found. Secondly, feature selection models including correlation coefficient method and genetic algorithm are constructed to reduce the dimension of the query data. Thirdly, various neural networks are employed to model the relationship between unemployment rate data and query data. Fourthly, an optimal neural network is selected as the selective predictor by using the cross-validation method. Finally, the selective neural network predictor with the best feature subset is used to forecast unemployment trend. The empirical results show that the proposed method clearly outperforms the classical forecasting approaches for the unemployment rate prediction. These findings imply that data mining method, such as neural networks, together with web information, can be used as an alternative tool to forecast social/economic hotspot. © 2011 IEEE.

关键词： Forecasting

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Recovering accurate labeling information from partially valid data for effective multi-label learning 29

Recovering accurate labeling information from partially vali...

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29th International Joint Conference on Artificial Intelligence, IJCAI 2020

作者： Li, Ximing Wang, Yang College of Computer Science and Technology Jilin University China Key Laboratory of Symbolic Computation Knowledge Engineering of Ministry of Education Jilin University China Key Laboratory of Knowledge Engineering With Big Data Ministry of Education Hefei University of Technology China School of Computer Sci and Information Engineering Hefei University of Technology China

ISBN: (纸本)9780999241165

Partial Multi-label Learning (PML) aims to induce the multi-label predictor from datasets with noisy supervision, where each training instance is associated with several candidate labels but only partially valid. To address the noisy issue, the existing PML methods basically recover the ground-truth labels by leveraging the ground-truth confidence of the candidate label, i.e., the likelihood of a candidate label being a ground-truth one. However, they neglect the information from non-candidate labels, which potentially contributes to the ground-truth label recovery. In this paper, we propose to recover the ground-truth labels, i.e., estimating the ground-truth confidences, from the label enrichment, composed of the relevance degrees of candidate labels and irrelevance degrees of non-candidate labels. Upon this observation, we further develop a novel two-stage PML method, namely Partial MultiLabel Learning with Label Enrichment-Recovery (PML3ER), where in the first stage, it estimates the label enrichment with unconstrained label propagation, then jointly learns the ground-truth confidence and multi-label predictor given the label enrichment. Experimental results validate that PML3ER outperforms the state-of-the-art PML methods. © 2020 Inst. Sci. inf., Univ. Defence in Belgrade. All rights reserved.

关键词： Learning systems

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Random rough subspace based neural network ensemble for insurance fraud detection

Random rough subspace based neural network ensemble for insu...

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International Workshop on Computational Transportation Science

作者： Xu, Wei Wang, Shengnan Zhang, Dailing Yang, Bo Key Laboratory of Data Engineering and Knowledge Engineering School of Information Renmin University of China Beijing 100872 China School of Information Renmin University of China Beijing 100872 China

ISBN: (纸本)9780769543352

In this paper, a random rough subspace based neural network ensemble method is proposed for insurance fraud detection. In this method, rough set reduction is firstly employed to generate a set of reductions which can keep the consistency of data information. Secondly, the reductions are randomly selected to construct a subset of reductions. Thirdly, each of the selected reductions is used to train a neural network classifier based on the insurance data. Finally, the trained neural network classifiers are combined using ensemble strategies. For validation, a real automobile insurance case is used to test the effectiveness and efficiency of our proposed method with two popular evaluation criteria including the percentage correctly classified (PCC) and the receive operating characteristic (ROC) curve. The experimental results show that our proposed model outperforms single classifier and other models used in comparison. The findings of this study reseal that the random rough subspace based neural network ensemble method can provide a faster and more accurate way to find suspicious insurance claims, and it is a promising tool for insurance fraud detection. © 2011 IEEE.

关键词： Insurance

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Optimizing queries with expensive video predicates based on estimation of attribute cardinality

Optimizing queries with expensive video predicates based on ...

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International Conference on Multimedia Information Networking and Security

作者： Yu, Lisheng Zhang, Jianmei Wang, Shan Key Laboratory of Ministry of Education for Data Engineering and Knowledge Engineering Renmin University of China Beijing 100872 China School of Information Renmin University of China Beijing 100872 China

ISBN: (纸本)9780769542584

With rapid advances in video processing technologies, video data increased rapidly and becomes popular in our daily life for both professional and consumer applications, e.g., surveillance, education, entertainment. Such needs require the data management system not only can store and access the video content, but also able to optimize the queries with expensive video predications in an effective and efficient way. In previous research literature, query optimizations in relational database systems (RDBMS) are often based on disk I/O cost of involved operations. However, for a query that contains expensive video predicates, traditional cost estimation model does not work well. Although researchers have proposed some approaches which can solve the problem in certain situations, there are still some unresolved issues, and it needs further optimization. In this paper, motivate from a real-world large supermarket's business data and video surveillance data management scenario, through considering the characteristics of video data and its expensive processing, we propose a novel query optimization approach that caches operators' results and reconstructs the join order based estimation for attribute cardinality. This approach reduces the invoking times of expensive video predicate in a greater degree and gives a better solution for mixed query optimization which contains traditional data types and large object operations. The experiment result is satisfactory while compare with existing expensive predicates query optimization methods. © 2010 IEEE.

关键词： Relational database systems

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Measuring semantic association in domain ontology

Measuring semantic association in domain ontology

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3rd International Conference on Semantics, knowledge, and Grid, SKG 2007

作者： Tian, Xuan Li, HaiHua Du, Xiaoyong School of Information Renmin University of China Beijing 100872 China School of Information Renmin University of China Key Laboratory of Data Engineering and Knowledge Engineering Beijing 100872 China

ISBN: (纸本)0769530079

In domain ontology, Semantic Association(SA) is used to depict the correlation between two concepts. In this paper, we define Semantic Association Degree(SAD) for measuring SA in the domain ontology. We first present a method to measure SAD of two direct related concepts by evaluating the semantic relationship between them, and then give another method to measure SAD of two indirect related concepts though SAD of two directed neighboring concepts. A set of comparison experiments show the benefit of our approaches. © 2007 IEEE.

关键词： Semantics

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Random analysis of statistical rough set

Random analysis of statistical rough set

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2016 International Conference on Machine Learning and Cybernetics, ICMLC 2016

作者： Tsang, Eric C.C. Zhao, Su-Yun Faculty of Information Technology Macau University of Science and Technology C-Macau China Key Laboratory of Data Engineering and Knowledge Engineering MOE Renmin University of China Beijing China

ISBN: (纸本)9781509003891

Attribute reduction is an inevitable problem in machine learning and statistical learning. To improve the traditional rough set reduction, statistical rough sets is then proposed by introducing random sampling into the rough approximation. Random sampling is the main contribution of statistical rough sets. As a result, it is necessary to analyze the randomness of statistical rough sets. In this paper, we analyze and demonstrate the influence of the randomness in the process of attribute reduction by a large number of experiments to test the effectiveness and stability of the random sampling. © 2016 IEEE.

关键词： Sampling

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Detecting spam comments with malicious users'behavioral characteristics

Detecting spam comments with malicious users'behavioral char...

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2010 IEEE International Conference on Information Theory and Information Security, ICITIS 2010

作者： Wang, Qianqian Liang, Bin Shi, Wenchang Liang, Zhaohui Sun, Wei Key Laboratory of Data Engineering and Knowledge Engineering School of Information Renmin University of China Beijing China Beijing Venustech Cybervision Co. Ltd Beijing 100094 China

ISBN: (纸本)9781424469406

In recent years, the spread of spam comments has become a main obstacle which limits the development of commercialized social networks. This paper analyzes the differences of behavioral characteristics between normal users and malicious users. Based on these characteristics, we propose several heuristic methods to detect spam comments. These methods evaluate comments from three perspectives, including time-frequency characteristic of comments, text similarity of comments and the number of target domains each user's comments refer to. In our collected dataset, our experimental results indicate the accuracy of our detection strategy I (strategy for high accuracy) and strategy II (strategy for wide coverage) are 100% and 92.6%, respectively. The preliminary evaluation of the proposed detection methods shows promising result. © 2010 IEEE.

关键词： Heuristic methods

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