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检索条件"主题词=Topic Detection and Tracking"

共 76 条记录，以下是41-50 订阅

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Complementary Aspect-based Opinion Mining Across Asymmetric Collections 15

Complementary Aspect-based Opinion Mining Across Asymmetric ...

引用

IEEE International Conference on Data Mining (ICDM)

作者： Zuo, Yuan Wu, Junjie Zhang, Hui Wang, Deqing Lin, Hao Wang, Fei Xu, Ke Beihang Univ Sch Comp Sci & Engn Beijing 100191 Peoples R China Beihang Univ Sch Econ & Management Beijing 100191 Peoples R China

ISBN: (纸本)9781467395038

Aspect-based opinion mining is to find elaborate opinions towards an underlying theme, perspective or viewpoint as to a subject such as a product or an event. Nowadays, with rapid growing of opinionated text on the Web, mining aspect-level opinions has become a promising means for online public opinion analysis. In particular, the booming of various types of online media provide diverse yet complementary information, bringing unprecedented opportunities for public opinion analysis across different populations. Along this line, in this paper, we propose CAMEL, a novel topic model for complementary aspect-based opinion mining across asymmetric collections. CAMEL gains complementarity by modeling both common and specific aspects across different collections, and keeping all the corresponding opinions for contrastive study. To further boost CAMEL, we propose AME, an automatic labeling scheme for maximum entropy model, to help discriminate aspect and opinion words without heavy human labeling. Extensive experiments on synthetic multi-collection data sets demonstrate the superiority of CAMEL to baseline methods, in leveraging cross-collection complementarity to find higher-quality aspects and more coherent opinions as well as aspect-opinion relationships. This is particularly true when the collections get seriously imbalanced. Experimental results also show that the AME model indeed outperforms manual labeling in suggesting true opinion words. Finally, case study on two public events further demonstrates the practical value of CAMEL for real-world public opinion analysis.

关键词： Aspect-based Opinion Mining topic detection and tracking LDA Model Maximum Entropy Model

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tracking topic evolution in on-line postings: 2006 IBM innovation jam data

Tracking topic evolution in on-line postings: 2006 IBM innov...

引用

12th Pacific-Asia Conference on Knowledge Discovery and Data Mining

作者： Kobayashi, Mei Yung, Raylene IBM Tokyo Res Lab Kanagawa 2428502 Japan

ISBN: (纸本)9783540681243

Participants in on-line discussion forums and decision makers are interested in understanding real-time communications between large numbers of parties on the internet and intranet. As a first step towards addressing this challenge, we developed a prototype to quickly identify and track topics in large, dynamic data sets based on assignment of documents to time slices, fast approximation of cluster centroids to identify discussion topics, and inter-slice correspondence mappings of topics. To verify our method, we conducted implementation studies with data from Innovation Jam 2006, an on-line brainstorming session, in which participants around the globe posted more than 37,000 opinions. Results from our prototype are consistent with the text in the postings, and would have required considerable effort to discover manually.

关键词： discussion mining topic detection and tracking

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Story Link detection in Turkish Corpus

Story Link Detection in Turkish Corpus

引用

12th IEEE/WIC/ACM International Joint Conferences on Web Intelligence and Intelligent Agent Technology (WI-IAT)

作者： Kose, Guven Tonta, Yasar Ahmadlouei, Hamid Polatkan, Aydin Can Hacettepe Univ Dept Informat Management Ankara Turkey Univ Tubingen Fac Sci Ctr Bioinformat Tubingen Tubingen Germany

ISBN: (纸本)9781479929023

Story Link detection (SLD) is known as a sub-task of topic detection and tracking (TDT). SLD aims to specify whether two randomly selected stories discuss the same topic or not. This sub-task drew special attention within the TDT research community as many tasks in TDT are thought to be solved automatically once SLD performs as expected. In this study, performance tests were carried out on the BilCol-2005 Turkish news corpus composed of approximately 209,000 news items using vector space model (VSM) and relevance model (RM) methods with respect to varied index term counts. Accordingly, best results obtained were as follows: the VSM method performed best with 30 terms (F-measure=0.2970) while RM method did with 4 terms (F-measure=0.1910). Furthermore, the combination of two methods using the AND and OR functions increased the precision ratio by 7.9% and recall ratio by 1.2%, respectively, indicating that retrieval performance of SLD algorithms can be increased to some extent by employing both VSM and RM models.

关键词： story link detection topic detection and tracking vector space model relevance model

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topic Model Allocation of Conversational Dialogue Records by Latent Dirichlet Allocation

Topic Model Allocation of Conversational Dialogue Records by...

引用

Annual Summit and Conference of Asia-Pacific-Signal-and-Information-Processing-Association (APSIPA)

作者： Yeh, Jui-Feng Lee, Chen-Hsien Tan, Yi-Shiuan Yu, Liang-Chih Natl Chiayi Univ Dept Comp & Informat Sci Chiayi 60004 Taiwan Yuan Zu Univ Dept Informat Management Taoyuan Taiwan

ISBN: (纸本)9786163618238

The topic information of conversational content is important for continuation with communication, so topic detection and tracking is one of important research. Due to there are many topic transform occurring frequently in long time communication, and the conversation maybe have many topics, so it's important to detect different topics in conversational content. This paper detects topic information by using agglomerative clustering of utterances and Dynamic Latent Dirichlet Allocation topic model, uses proportion of verb and noun to analyze similarity between utterances and cluster all utterances in conversational content by agglomerative clustering algorithm. The topic structure of conversational content is friability, so we use speech act information and gets the hypernym information by E-HowNet that obtains robustness of word categories. Latent Dirichlet Allocation topic model is used to detect topic in file units, it just can detect only one topic if uses it in conversational content, because of there are many topics in conversational content frequently, and also uses speech act information and hypernym information to train the latent Dirichlet allocation models, then uses trained models to detect different topic information in conversational content. For evaluating the proposed method, support vector machine is developed for comparison. According to the experimental results, we can find the proposed method outperforms the approach based on support vector machine in topic detection and tracking in spoken dialogue.

关键词： Conversational dialogue latent Dirichlet allocation topic detection and tracking spoken language processing

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A Network Alert Degree Evaluation Model Based on Cooperative Computing

A Network Alert Degree Evaluation Model Based on Cooperative...

引用

7th International Conference on Grid and Cooperative Computing

作者： Hu Chang-Jun Weng Yu Shi Peng Univ Sci & Technol Beijing Sch Informat & Engn Beijing 100083 Peoples R China

ISBN: (纸本)9780769534497

Oriented to the deficiency of cooperative analysis ability between different network domains in traditional topic detection and tracking (TDT), this paper proposes a network alert degree evaluation model based on cooperative computing (named CCAE). The model not only considers the influence of inner network domain, but also evaluates the cooperation impacts of inter-network domains. With the help of alert case knowledge base (ACKB), CCAE mines the alert information of cacti network domain by matching the alert cases with Web text. Through scheduling the local alert cases with high matching frequency between different network domains, CCAE effectively improves the ability of alert spreading analysis, optimizes the local alert case knowledge base and sharply shortens the time-consuming of alert detection. Considering the influence of page hyperlinks on alert spreading, CCAE retrieves the alert degree of the linked pages, and further realizes the evaluation in cooperative computing environment. For validating the performance of CCAE, we present a comparison experiment on a cosine classifier with TF-IDF in a corpus about "Campus Network Culture". The experimental results further validate the analysis ability of alert spreading and higher detection accuracy of CCAE.

关键词： Alert Degree Evaluation Cooperative Computing topic detection and tracking

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Stochastic Approach to Aspect tracking 23rd

Stochastic Approach to Aspect Tracking

引用

23rd International Conference on Applications of Natural Language to Information Systems (NLDB)

作者： Inoue, Maoto Miura, Takao Hosei Univ Dept Adv Sci 3-7-2 KajinoCho Koganei Tokyo 1848584 Japan

ISBN: (纸本)9783319919478;9783319919461

In this investigation, we discuss aspect tracking, i.e., how to identify tracking storylines of document topics. Since there happen huge amount of fragment information, it is hard to see what they mean and how they go within topics by hands. Here we attack to this kind of problems by means of stochastic models. Our basic idea is that we consider state transitions as internal structure of stories based on HMM, and we extract several storylines as aspects of topics by probabilistic likelihood. We utilize KL divergence to screen topics.

关键词： Aspect tracking Hidden Markov Model (HMM) topic detection and tracking

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Discovering Event Evolution Graphs based on News Articles Relationships 11

Discovering Event Evolution Graphs based on News Articles Re...

引用

11th IEEE International Conference on e-Business Engineering (ICEBE)

作者： Huang, Dongping Hu, Shuyu Cai, Yi Min, Huaqing S China Univ Technol Sch Software Engn Guangzhou 510641 Guangdong Peoples R China

ISBN: (纸本)9781479965632

There are many news articles reported online everyday. Within an ongoing topic, people can find a huge amount of news articles. A topic often consists of several events, and people are interested in the whole evolution of a topic along a timeline. This requests for finding and identifying the dependent relationships between events. In order to understand the whole evolution of a topic effectively, we propose a framework of event relationship analysis. We define three kinds of event relationships which are co-occurrence dependence relationship, event reference relationship, and temporal proximity relationship for modeling how an event is dependent on another event within a topic. Through combining three kinds of relationships, we can discover an Event Evolution Graph (EEG) for users to view the evolution of a topic. Experiments conducted on a real data set show that our method outperforms baseline methods.

关键词： Event Evolution Relationship Analysis topic detection and tracking topic Model

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SUBtopic-BASED MULTI-DOCUMENT SUMMARIZATION

SUBTOPIC-BASED MULTI-DOCUMENT SUMMARIZATION

引用

International Conference on Machine Learning and Cybernetics

作者： Dai, Lin Tang, Ji-Liang Xia, Yun-Qing Beijing Inst Technol Sch Comp Sci Beijing Lab Intelligent Informat Technol Beijing 100081 Peoples R China

ISBN: (纸本)9781424447053

This paper proposes a novel approach for multi-document summarization based on subtopic segmentation. It firstly detects the subtopics in a topic, and then finds the central sentence for each subtopic. The sentences are scored based on their importance in the document and in the subtopic. Two anti-redundancy strategies are used to extract sentences to form summarization. Since our approach is intrinsically incremental, it is effective when new documents are added to the document set. Experimental results indicate that the proposed approach is effective and efficient.

关键词： Multi-document summarization topic segmentation topic detection and tracking Anti-redundancy strategy

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tracking Aspects in News Documents 18

Tracking Aspects in News Documents

引用

18th IEEE International Conference on Data Mining Workshops (ICDMW)

作者： Inoue, Maoto Miura, Takao Hosei Univ Dept Adv Sci Kajinocho 3-7-2 Koganei Tokyo 1868584 Japan

ISBN: (纸本)9781538692882

In this investigation, we discuss aspect tracking, i.e., how to identify tracking story-lines of document topics. Because a huge amount of fragment information occurs, it is difficult to see what they mean and how they go within topics by hand. Here we are addressing this type of problem using stochastic models. Our basic idea is that we consider state transitions as internal structure of stories based on HMM and we extract some story-lines as aspects of topics by probabilistic likelihood. We will use KL divergence to screen topics. Also, we discuss topic detection, i.e., how to construct clusters of events to discuss the same topic.

关键词： topic detection and tracking Aspect tracking Hidden Markov Model (HMM) Backward Viterbi Algorithm

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topic Diffusion Discovery based on Deep Non-negative Autoencoder 21

Topic Diffusion Discovery based on Deep Non-negative Autoenc...

引用

21st IEEE International Conference on Information Reuse and Integration for Data Science (IEEE IRI)

作者： Huang, Sheng-Tai Kang, Yihuang Hung, Shao-Min Kuo, Bowen Cheng, I-Ling Natl Sun Yat Sen Univ Dept Informat Management Kaohsiung Taiwan Natl Chung Hsing Univ Grad Inst Lib & Informat Sci Taichung Taiwan

ISBN: (纸本)9781728110547

Researchers have been overwhelmed by the explosion of research articles published by various research communities. Many research scholarly websites, search engines, and digital libraries have been created to help researchers identify potential research topics and keep up with recent progress on research of interests. However, it is still difficult for researchers to keep track of the research topic diffusion and evolution without spending a large amount of time reviewing numerous relevant and irrelevant articles. In this paper, we consider a novel topic diffusion discovery technique. Specifically, we propose using a Deep Non-negative Autoencoder with information divergence measurement that monitors evolutionary distance of the topic diffusion to understand how research topics change with time. The experimental results show that the proposed approach is able to identify the evolution of research topics as well as to discover topic diffusions in online fashions.

关键词： Deep Autoencoder Online Machine Learning topic Modeling topic Diffusion topic detection and tracking

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