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International Conference on Semantic Technology and Information Retrieval

作者： Mohd, Masnizah Crestani, Fabio Ruthven, Ian Faculty of Information Science and Technology Universiti Kebangsaan Malaysia 43600 Selangor Malaysia via G. Buffi 13 Ch-6900 Lugano Switzerland Dept. of Computer and Information Sciences University of Strathclyde Glasgow G1 1XH United Kingdom

ISBN: (纸本)9781612843537

This paper discussed the construction of topics to be tracked and clusters to be detected in topic detection and tracking (TDT) tasks. Single Pass Clustering was used to cluster the news articles. As a result, the TDT tasks contained a combination of a good and poor clustering performance based on the F1-measure. Therefore, the selection of clusters and topics from the clustering experiment is important in the tracking and the detection tasks. It has contributed towards the user experimental design. © 2011 IEEE.

关键词： Classification algorithms Clustering algorithms Event detection Fl-measure Information retrieval Monitoring Single Pass Clustering Text categorization topic detection and tracking User interfaces document handling information retrieval pattern clustering single pass clustering topic detection topic tracking user tasks

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Measuring the veracity of web event via uncertainty

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JOURNAL OF SYSTEMS AND SOFTWARE 2015年 102卷 226-236页

作者： Wang, Xinzhi Luo, Xiangfeng Liu, Huiming Shanghai Univ Sch Comp Engn & Sci Shanghai 200444 Peoples R China State Key Lab Math Engn & Adv Comp Wuxi Peoples R China

Web events, whose data occur as one kind of big data, have attracted considerable interests during the past years. However, most existing related works fail to measure the veracity of web events. In this research, we propose an approach to measure the veracity of web event via its uncertainty based on its features distribution on different kind of confident websites. Firstly, the proposed approach mines various event features from the data of web event which may influence on the measuring process of uncertainty. Secondly, one computational model is introduced to simulate the influence process of the above features on the evolution process of web event. Thirdly, matrix operations are managed to facilitate practice. Finally, experiments are made based on the analysis above, and the results proved that the proposed uncertainty measuring algorithm is promising to measure the veracity of web event for big data. (C) 2014 Elsevier Inc. All rights reserved.

关键词： Web event veracity topic detection and tracking Big data

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A dynamic probabilistic model to visualise topic evolution in text streams

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JOURNAL OF INTELLIGENT INFORMATION SYSTEMS 2002年第2-3期18卷 107-125页

作者： Kabán, A Girolami, MA Univ Paisley Sch Informat & Commun Technol Paisley PA1 2BE Renfrew Scotland

We propose a novel probabilistic method, based on latent variable models, for unsupervised topographic visualisation of dynamically evolving, coherent textual information. This can be seen as a complementary tool for topic detection and tracking applications. This is achieved by the exploitation of the a priori domain knowledge available, that there are relatively homogeneous temporal segments in the data stream. In a different manner from topographical techniques previously utilized for static text collections, the topography is an outcome of the coherence in time of the data stream in the proposed model. Simulation results on both toy-data settings and an actual application on Internet chat line discussion analysis is presented by way of demonstration.

关键词： topographic mapping latent trait hidden Markov model topic segmentation topic detection and tracking

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Indices of novelty for emerging topic detection

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INFORMATION PROCESSING & MANAGEMENT 2012年第2期48卷 303-325页

作者： Tu, Yi-Ning Seng, Jia-Lang Fu Jen Catholic Univ Dept Stat & Informat Sci Coll Management New Taipei 242 Taiwan Fu Jen Catholic Univ Grad Sch Stat & Informat Sci Coll Management New Taipei 242 Taiwan Natl Chengchi Univ Dept Accounting Coll Commerce Taipei 116 Taiwan Natl Chengchi Univ Grad Sch Accounting Coll Commerce Taipei 116 Taiwan

Emerging topic detection is a vital research area for researchers and scholars interested in searching for and tracking new research trends and topics. The current methods of text mining and data mining used for this purpose focus only on the frequency of which subjects are mentioned, and ignore the novelty of the subject which is also critical, but beyond the scope of a frequency study. This work tackles this inadequacy to propose a new set of indices for emerging topic detection. They are the novelty index (NI) and the published volume index (PVI). This new set of indices is created based on time, volume, frequency and represents a resolution to provide a more precise set of prediction indices. They are then utilized to determine the detection point (DP) of new emerging topics. Following the detection point, the intersection decides the worth of a new topic. The algorithms presented in this paper can be used to decide the novelty and life span of an emerging topic in a specific field. The entire comprehensive collection of the ACM Digital Library is examined in the experiments. The application of the NI and PVI gives a promising indication of emerging topics in conferences and journals. Crown Copyright (C) 2011 Published by Elsevier Ltd. All rights reserved.

关键词： topic detection and tracking Text mining Information retrieval Novelty index Published volume index Aging theory

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Mining Event Structures from Web Videos

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IEEE MULTIMEDIA 2011年第1期18卷 38-51页

作者： Wu, Xiao Lu, Yi-Jie Peng, Qiang Ngo, Chong-Wah SW Jiaotong Univ Chengdu Peoples R China City Univ Hong Kong Hong Kong Hong Kong Peoples R China

This article explores the issues of mining event structures from Web video search results using text analysis, burst detection, clustering, and other techniques.

关键词： Web videos event structure mining feature trajectory topic detection and tracking text processing mining Web video

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Detecting and predicting the topic change of Knowledge-based Systems: A topic-based bibliometric analysis from 1991 to 2016

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KNOWLEDGE-BASED SYSTEMS 2017年 133卷 255-268页

作者： Zhang, Yi Chen, Hongshu Lu, Jie Zhang, Guangquan Univ Technol Sydney Decis Syst & E Serv Intelligence Res Lab Ctr Artificial Intelligence Fac Engn & Informat Technol Sydney NSW Australia

The journal Knowledge-based Systems (KnoSys) has been published for over 25 years, during which time its main foci have been extended to a broad range of studies in computer science and artificial intelligence. Answering the questions: "What is the KnoSys community interested in?" and "How does such interest change over time?" are important to both the editorial board and audience of KnoSys. This paper conducts a topic-based bibliometric study to detect and predict the topic changes of KnoSys from 1991 to 2016. A Latent Dirichlet Allocation model is used to profile the hotspots of KnoSys and predict possible future trends from a probabilistic perspective. A model of scientific evolutionary pathways applies a learning-based process to detect the topic changes of KnoSys in sequential time slices. Six main research areas of KnoSys are identified, i.e., expert systems, machine learning, data mining, decision making, optimization, and fuzzy, and the results also indicate that the interest of KnoSys communities in the area of computational intelligence is raised, and the ability to construct practical systems through knowledge use and accurate prediction models is highly emphasized. Such empirical insights can be used as a guide for KnoSys submissions. (C) 2017 Elsevier B.V. All rights reserved.

关键词： topic analysis topic detection and tracking Bibliometrics Text mining Knowledge-based Systems

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Soft orthogonal non-negative matrix factorization with sparse representation: Static and dynamic

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NEUROCOMPUTING 2018年 310卷 148-164页

作者： Chen, Yong Zhang, Hui Liu, Rui Ye, Zhiwen Beihang Univ Sch Comp Sci & Engn State Key Lab Software Dev Environm Beijing 100191 Peoples R China Beihang Univ Beijing Adv Innovat Ctr Big Data & Brain Comp Beijing 100191 Peoples R China

NMF owns the ability to well interpret practical problems owing to its non-negative elements and surprisingly could dig out data's latent factors as human cognition. However, structures recognized from classic NMF are usually not fully localized and always accompanied with noises. In light of this, we introduce an improved framework called SONMFSR (Soft Orthogonal NMF with Sparse Representation), which makes full use of soft orthogonality and sparsity constraints to tackle such problems in this paper. Related experiments show that SONMFSR can excavate diverse and local structures with compact representations. Motivated by these characteristics, as well as the unprecedented need and further study for dynamic topic model (DTM) for timely event analysis, we then extend static SONMFSR to a novel DTM named SONMFSRd by introducing the constraints of emerging, evolving and fading topics. Extensive experiments on two public corpora also demonstrate the superiority of SONMFSRd to some state-of-the-art DTMs in both topic detection and tracking. In particular, SONMFSRd exhibits great potential in real-world applications, where popular topics in Two Sessions 2015 are captured and traced dynamically for possible insights. Moreover, we also provide theoretical support for the proposed schemes. (C) 2018 Elsevier B.V. All rights reserved.

关键词： Soft orthogonality Sparse representations Non-negative matrix factorization (NMF) Dynamic topic model topic detection and tracking Diversity regularization

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A Bayesian analysis of spherical pattern based on finite Langevin mixture

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APPLIED SOFT COMPUTING 2016年 38卷 373-383页

作者： Amayri, Ola Bouguila, Nizar Concordia Univ Fac Engn & Comp Sci Montreal PQ H3G 2W1 Canada

Parameter estimation is a cornerstone of most fundamental problems of statistical research and practice. In particular, finite mixture models have long been heavily relied on deterministic approaches such as expectation maximization (EM). Despite their successful utilization in wide spectrum of areas, they have inclined to converge to local solutions. An alternative approach is the adoption of Bayesian inference that naturally addresses data uncertainty while ensuring good generalization. To this end, in this paper we propose a fully Bayesian approach for Langevin mixture model estimation and selection via MCMC algorithm based on Gibbs sampler, Metropolis-Hastings and Bayes factors. We demonstrate the effectiveness and the merits of the proposed learning framework through synthetic data and challenging applications involving topic detection and tracking and image categorization. (C) 2015 Elsevier B.V. All rights reserved.

关键词： Langevin mixture Bayesian inference MCMC Spherical data topic detection and tracking Image categorization

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Hot topic extraction based on timeline analysis and multidimensional sentence modeling

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IEEE TRANSACTIONS ON KNOWLEDGE AND DATA ENGINEERING 2007年第8期19卷 1016-1025页

作者： Chen, Kuan-Yu Luesukprasert, Luesak Chou, Seng-Cho T. Natl Taiwan Univ Dept Informat Management Taipei 106 Taiwan

With the vast amount of digitized textual materials now available on the Internet, it is almost impossible for people to absorb all pertinent information in a timely manner. To alleviate the problem, we present a novel approach for extracting hot topics from disparate sets of textual documents published in a given time period. Our technique consists of two steps. First, hot terms are extracted by mapping their distribution over time. Second, based on the extracted hot terms, key sentences are identified and then grouped into clusters that represent hot topics by using multidimensional sentence vectors. The results of our empirical tests show that this approach is more effective in identifying hot topics than existing methods.

关键词： aging theory clustering hot topic detection term weighting topic detection and tracking

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Visual topic discovering, tracking and summarization from social media streams

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MULTIMEDIA TOOLS AND APPLICATIONS 2017年第8期76卷 10855-10879页

作者： Lu, Zhao Lin, Yu-Ru Huang, Xiaoxia Xiong, Naixue Fang, Zhijun East China Normal Univ Shanghai Key Lab Multidimens Informat Proc Dept Comp Sci & Technol 500 Dongchuan Rd Shanghai 200241 Peoples R China Univ Pittsburgh Sch Informat Sci 135 North Bellefield Ave Pittsburgh PA 15260 USA Southwestern Oklahoma State Univ Dept Business & Comp Sci 100 Campus Dr Weatherford OK 73096 USA Shanghai Univ Engn Sci Sch Elect & Elect Engn 333 Longteng Rd Shanghai 201620 Peoples R China

Nowadays, microblogging has become popular, with hundreds of millions of short messages being posted and shared every minute on a variety of topics in social media such as Facebook, Twitter and Weibo. Many of such messages contain videos that captured particular events or moments in people’s life. In this work, we seek to automatically identify the video topics posted in the social media streams on Weibo. While topic detection and tracking (TDT) task has been extensively studied in multimedia retrieval, automatically discovering, tracking and summarizing video topics from social media streams is still challenging due to short and noisy content, diverse and fast changing topics, and large data volume. In this paper, we propose a K-partite graph based approach to address these challenges. We introduce a K-partite graph representation to simultaneously model the relationships among videos contained in the Weibo streams, their textural features and visual features. We propose a novel joint clustering algorithm to capture global structure of the K-partite graph in a “relation cluster network” (RCN) where latent, meta-nodes are added to the network to represent video clusters. Based on this network we propose methods for tracking and summarizing the videos in streams through fusing various types of features and multiple ranking schemes. The experiment results based on a real dataset show the effectiveness of our method with significant improvement over baseline.

关键词： topic detection and tracking Social media stream K-partite graph Multi-modality feature representation

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