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topic detection and tracking for conversational content by using conceptual dynamic latent Dirichlet allocation

NEUROCOMPUTING 2016年 216卷 310-318页

作者： Yeh, Jui-Feng Tan, Yi-Shan Lee, Chen-Hsien Natl Chiayi Univ Dept Comp Sci & Informat Engn Chiayi Taiwan

This study proposes a conceptual dynamic latent Dirichlet allocation (CDLDA) model for topic detection and tracking in conversational content. topic detection and tracking is vital for conversational communication, especially for spoken interactions. Because topic transitions occur frequently during conversational communication (i.e., a conversation usually contains many topics), language processors must detect different topics in conversational content. Considering the structure of spoken dialogue, the dynamic model was employed in this study to capture the sequence of two adjacent topics in spoken content. The proposed model applies the proportions of verbs and nouns to analyze the similarity between utterances. An agglomerative clustering algorithm, based on an ontology defined in E-HowNet, clusters conversational utterances. Because the topic structure of conversational content is friable, E-HowNet uses hypernym relationships of speech acts to obtain robust solutions, even for sparse data. Compared with the traditional latent Dirichlet allocation (LDA) model, which detects topics only through a bag-of-words technique, the proposed model considers temporal features by introducing dynamic concepts. Experimental results revealed that the proposed approach outperformed the traditional DLDA and LDA and support vector machine models, in addition to achieving excellent performance for topic detection and tracking in conversations. (C) 2016 Elsevier B.V. All rights

关键词： Latent Dirichlet allocation Hypernym Speech act Spoken language understanding topic detection and tracking

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Online web video topic detection and tracking with semi-supervised learning

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MULTIMEDIA SYSTEMS 2016年第1期22卷 115-125页

作者： Li, Guorong Jiang, Shuqiang Zhang, Weigang Pang, Junbiao Huang, Qingming Univ Chinese Acad Sci Sch Comp & Control Engn Beijing Peoples R China Chinese Acad Sci Inst Comp Technol Key Lab Intelligent Informat Proc Beijing Peoples R China Harbin Inst Technol Sch Comp Sci & Technol Harbin 150006 Peoples R China Beijing Univ Technol Beijing Municipal Key Lab Multimedia & Intelligen Beijing Peoples R China

With the pervasiveness of online social media and rapid growth of web data, a large amount of multi-media data is available online. However, how to organize them for facilitating users' experience and government supervision remains a problem yet to be seriously investigated. topic detection and tracking, which has been a hot research topic for decades, could cluster web videos into different topics according to their semantic content. However, how to online discover topic and track them from web videos and images has not been fully discussed. In this paper, we formulate topic detection and tracking as an online tracking, detection and learning problem. First, by learning from historical data including labeled data and plenty of unlabeled data using semi-supervised multi-class multi-feature method, we obtain a topic tracker which could also discover novel topics from the new stream data. Second, when new data arrives, an online updating method is developed to make topic tracker adaptable to the evolution of the stream data. We conduct experiments on public dataset to evaluate the performance of the proposed method and the results demonstrate its effectiveness for topic detection and tracking.

关键词： topic detection and tracking Web video Multi-feature fusion Semi-supervised learning

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Incorporating topic transition in topic detection and tracking algorithms

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EXPERT SYSTEMS WITH APPLICATIONS 2009年第1期36卷 227-232页

作者： Zeng, Jianping Zhang, Shiyong Fudan Univ Dept Comp & Informat Technol Shanghai 200433 Peoples R China

topics often transit among documents in a document collection. To improve the accuracy of the topic detection and tracking (TDT) algorithms in discovering topics or classifying documents, it is necessary to make full use of this kind of topic transition information. However, TDT algorithms usually. find topics based on topic models, such as LDA, pLSI, etc., which are a kind of mixture model and make the topic transition difficult to be denoted and implemented. A topic transition model representation based on hidden Markov model is present, and learning the topic transition from documents is discussed. Based on the model, two TDT algorithms incorporating topic transition, i.e. topic discovering and document classifying, are provided to show the application of the proposed model. Experiments on two real-world document collections are done with the two algorithms, and performance comparison with other similar algorithm shows that the accuracy can achieve 93% for topic discovering in Reuters-21578, and 97.3% in document classifying. Furthermore, topic transition discovered by the algorithm on a dataset which was collected from a BBS website is consistent with the manual analysis results. (C) 2007 Elsevier Ltd. All rights reserved.

关键词： topic transition topic detection and tracking Hidden Markov model

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Dependency structure language model for topic detection and tracking

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INFORMATION PROCESSING & MANAGEMENT 2007年第5期43卷 1249-1259页

作者： Lee, Changki Lee, Gary Geunbae Jang, Myunggil Elect & Telecommun Res Inst Knowledge Min Lab Speech Language Technol Res Dept Taejon 305350 South Korea Pohang Univ Sci & Technol Dept Comp Sci & Engn Pohang 790784 South Korea

In this paper, we propose a new language model, namely, a dependency structure language model, for topic detection and tracking (TDT) to compensate for weakness of unigram and bigram language models. The dependency structure language model is based on the Chow expansion theory and the dependency parse tree generated by a linguistic parser. So, long-distance dependencies can be naturally captured by the dependency structure language model. We carried out extensive experiments to verify the proposed model on topic tracking and link detection in TDT. In both cases, the dependency structure language models perform better than strong baseline approaches. (c) 2006 Published by Elsevier Ltd.

关键词： dependency structure language model term dependence dependency parse tree topic detection and tracking

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Simple semantics in topic detection and tracking

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INFORMATION RETRIEVAL 2004年第3-4期7卷 347-368页

作者： Makkonen, J Ahonen-Myka, H Salmenkivi, M Univ Helsinki Dept Comp Sci FIN-00014 Helsinki Finland

topic detection and tracking (TDT) is a research initiative that aims at techniques to organize news documents in terms of news events. We propose a method that incorporates simple semantics into TDT by splitting the term space into groups of terms that have the meaning of the same type. Such a group can be associated with an external ontology. This ontology is used to determine the similarity of two terms in the given group. We extract proper names, locations, temporal expressions and normal terms into distinct sub-vectors of the document representation. Measuring the similarity of two documents is conducted by comparing a pair of their corresponding sub-vectors at a time. We use a simple perceptron to optimize the relative emphasis of each semantic class in the tracking and detection decisions. The results suggest that the spatial and the temporal similarity measures need to be improved. Especially the vagueness of spatial and temporal terms needs to be addressed.

关键词： topic detection and tracking retrieval model information extraction temporal expression geographical ontology

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Clustering-Based Online News topic detection and tracking Through Hierarchical Bayesian Nonparametric Models 21

Clustering-Based Online News Topic Detection and Tracking Th...

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44th International ACM SIGIR Conference on Research and Development in Information Retrieval

作者： Fan, Wentao Guo, Zhiyan Bouguila, Nizar Hou, Wenjuan Huaqiao Univ Dept Comp Sci & Technol Xiamen Fujian Peoples R China Concordia Univ CIISE Montreal PQ Canada Huaqiao Univ Instrumental Anal Ctr Xiamen Fujian Peoples R China

ISBN: (纸本)9781450380379

In this paper, we propose a clustering-based online news topic detection and tracking (TDT) approach based on hierarchical Bayesian nonparametric framework that allows topics to be shared across different news stories in a corpus. Our approach is formulated using the hierarchical Pitman-Yor process mixture model with the inverted Beta-Liouville (IBL) distribution as its component density, which has shown superior performance in modeling text data than the widely used Gaussian distribution. Moreover, we theoretically develop a convergence-guaranteed online learning algorithm that can effectively learn the proposed TDT model from a stream of news stories based on varational Bayes. The merits of our TDT approach are illustrated by comparing it with other well-defined clustering-based TDT approaches on different news data sets.

关键词： Clustering topic detection and tracking hierarchical Bayesian model

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Semi-Supervised Dirichlet-Hawkes Process with Applications of topic detection and tracking in Twitter 4

Semi-Supervised Dirichlet-Hawkes Process with Applications o...

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4th IEEE International Conference on Big Data (Big Data)

作者： Ding, Wanying Zhang, Yue Chen, Chaomei Hu, Xiaohua Drexel Univ Coll Comp & Informat Philadelphia PA 19104 USA

ISBN: (纸本)9781467390057

Understanding ongoing topics and their evolutions in social media is of great importance. Although topic analysis is not a novel research question, social media environment has presented new challenges. First, with insufficient co-occurrence information, short text have undermined many word co-occurrence oriented topic models' applicability. Second, real time message streams make traditional discretized topic tracking methods hard to function. Third, topics' evolution mechanisms are of great importance in social media context, but many studies have ignored them. Forth, topics have more complicated correlation among each other. Considering the existing problems, this paper has proposed a Semi-Supervised Dirichlet-Hawkes Process (SDHP) to deal with topic detection and tracking from social media. The main contributions of this paper are reflected in: (1) SDHP can handle short text problem efficiently;(2) SDHP can track topics from continuous message stream;(3) SDHP can reveal topics' underlying evolution patterns;and (4) SDHP can capture topics' correlations We have evaluated SDHP's ability in both topic detection and tracking in 8 real datasets from Twitter, and the algorithm's performances are very promising.

关键词： topic detection and tracking Twitter Hawkes Process Dirichlet Process

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A Month to topic detection and tracking in Hindi

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ACM Transactions on Asian Language Information Processing 2003年第2期2卷 85-100页

作者： Allan, James Lavrenko, Victor Connell, Margaret E. Center for Intelligent Information Retrieval Department of Computer Science University of Massachusetts Amherst 140 Governors Drive MA 01003 United States

We describe the one-month (June 2003) effort to create a topic detection and tracking (TDT) system to support news stories in Hindi. The University of Massachusetts submitted results for three different TDT tasks in the DARPA surprise language evaluation. The official task was topic tracking, but we also provided results for the new event detection and topic detection (clustering) tasks. Our approach to all three tasks was based on the vector-space model of information retrieval. We also describe the process we used to create the relevance judgments used to evaluate the system. Results suggest that topic tracking effectiveness is comparable to that of TDT tracking systems in other languages. Results for clustering and new event detection indicate that parameter settings for those tasks are sensitive to the language being used. © 2003, ACM. All rights reserved.

关键词： Algorithms Hindi Languages Measurement Performance surprise language TDT topic detection and tracking

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An Improved Single-Pass Algorithm For Chinese Microblog topic detection and tracking 5

An Improved Single-Pass Algorithm For Chinese Microblog Topi...

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IEEE International Congress on Big Data (BigData Congress)

作者： Yan, Danfeng Hua, Enzheng Hu, Bo Beijing Univ Posts & Telecommun State Key Lab Networking & Switching Technol Beijing Peoples R China

ISBN: (纸本)9781509026227

Microblog is a very popular social platform, as the source of news and popular information dissemination. detection and tracking of hot topics through Microblog research has arose the domestic and foreign scholar's attention. So, this paper mainly focuses on financial domain topic detection and tracking of Chinese Microblog. In this paper, we propose incremental TF-IWF-IDF of terms part-of-speech and position weight calculation method. This weight calculation method can solve the problem that IDF of TF-IDF is a constant value and can't change with the dataset dynamically. The traditional feature vector doesn't consider the semantic and context of terms. The paper proposes a new feature vector representation method to solve this problem by incorporating IWF into TF-IDF. This text representation method is called Word vector based on an incremental TF-IWF-IDF of terms part-of-speech and position. This paper proposes Two Steps of Single-Pass based on Multi topic Centers (MC-TSP) to overcome the shortcomings of the traditional Single-Pass algorithm. By experimental comparison, the improved algorithm has better performance than the traditional Single-Pass algorithm. With improved algorithm, financial hot topic detection and tracking model is designed and implemented. The application of this model in financial domain improved the accuracy of topic detection and tracking.

关键词： Incremental TF-IWF-IDF Word2Vec Single-Pass Algorithm topic detection and tracking financial domain

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Development and Research of topic detection and tracking 7

Development and Research of Topic Detection and Tracking

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2016 IEEE 7th International Conference on Software Engineering and Service Science

作者： Yuning Chen Lianzhong Liu Key Laboratory of Beijing Network Technology Beihang University

ISBN: (纸本)9781467399050

topic detection and tracking(TDT) is one of the issue in the field of Natural Language Processing(NLP) from the *** becomes a hot spot and many classic models and methods have been made currently,as it faces the information retrieval and extraction detecting on unknown topics and track exist *** paper reviews the development status of TDT *** definitions of topic,Story,Event and Activity are *** corpus and primary tasks in current research are *** integral framework and key technologies including topic models,correlation calculation and clustering and classification,are *** status of development of topic detection and tracking is concluded in the end.

关键词： topic detection and tracking evaluation cluster models

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