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WEB EVENT STATE PREDICTION MODEL: COMBINING PRIOR KNOWLEDGE WITH REAL TIME DATA

JOURNAL OF WEB ENGINEERING 2014年第5-6期13卷 483-506页

作者： Luo, Xiangfeng Xuan, Junyu Liu, Huimin Shanghai Univ Shanghai 200041 Peoples R China

The state prediction plays a key role in the evolution analysis of web events. There are two issues for the state prediction of web events: one is what factors impact on the state transition of web events;and the other is how the prior knowledge can guide the state transition of web events. For the first issue, we discuss two types of temporal features observed from the real time webpages covering an event, i.e., the statistical ones and the knowledge structural ones. For the second issue, Fuzzy Cognitive Map (FCM) and conditional dependency matrix are mined from the training web events. As the prior knowledge, they represent the relations between the states transition and the relations of unobserved space (i.e., the six states of web events) and observed space (i.e., the two types of features). Based on that, an improved hidden Markov model is developed to predict the state transition of web events. Experimental results show that the model has good performance and robustness because it combines the prior knowledge and the real time data of web events.

关键词： web event hidden Markov model topic detection and tracking multi-factor analysis

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Robust techniques for organizing and retrieving spoken documents

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EURASIP JOURNAL ON APPLIED SIGNAL PROCESSING 2003年第2期2003卷 103-114页

作者： Allan, J Univ Massachusetts Dept Comp Sci Ctr Informat Retrieval Amherst MA 01003 USA

Information retrieval tasks such as document retrieval and topic detection and tracking (TDT) show little degradation when applied to speech recognizer output. We claim that the robustness of the process is because of inherent redundancy in the problem: not only are words repeated, but semantically related words also provide support. We show how document and query expansion can enhance that redundancy and make document retrieval robust to speech recognition errors. We show that the same effect is true for TDT's tracking task, but that recognizer errors are more of an issue for new event and story link detection.

关键词： spoken document retrieval topic detection and tracking information retrieval

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Personalized Emerging topic detection Based on a Term Aging Model

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ACM TRANSACTIONS ON INTELLIGENT SYSTEMS AND TECHNOLOGY 2013年第1期5卷 7-7页

作者： Cataldi, Mario Di Caro, Luigi Schifanella, Claudio Ecole Cent Paris MAS Lab Paris France Univ Turin Dipartimento Informat I-10124 Turin Italy

Twitter is a popular microblogging service that acts as a ground-level information news flashes portal where people with different background, age, and social condition provide information about what is happening in front of their eyes. This characteristic makes Twitter probably the fastest information service in the world. In this article, we recognize this role of Twitter and propose a novel, user-aware topic detection technique that permits to retrieve, in real time, the most emerging topics of discussion expressed by the community within the interests of specific users. First, we analyze the topology of Twitter looking at how the information spreads over the network, taking into account the authority/influence of each active user. Then, we make use of a novel term aging model to compute the burstiness of each term, and provide a graph-based method to retrieve the minimal set of terms that can represent the corresponding topic. Finally, since any user can have topic preferences inferable from the shared content, we leverage such knowledge to highlight the most emerging topics within her foci of interest. As evaluation we then provide several experiments together with a user study proving the validity and reliability of the proposed approach.

关键词： Algorithms Experimentation Social network analysis Twitter topic detection and tracking trends aging theory

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BAYESIAN-BASED TYPE DISCRIMINATION OF WEB EVENTS

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JOURNAL OF WEB ENGINEERING 2015年第5-6期14卷 525-544页

作者： Ma, Qichen Luo, Xiangfeng Xuan, Junyu Liu, Huimin Shanghai Univ Sch Comp Engn & Sci Shanghai Peoples R China

There are a large number of web events emerging on the web and attracting people's attention every day, and it is of great interest and significance to distinguish the different types of these web events in practice. For example, the distinguished emergent web events should be paid more attentions by the departments of the government to save lives and damages or by news websites to increase their hit-rates using limited resources. However, how to efficiently distinguish the types of web events remains a challenge issue due to the seldom efforts paid to this issue in the community. In this paper, we conduct a thorough consideration on this problem and then propose an innovative Bayesian-based model to distinguish the different types of web events. To be specific, all web events are firstly assumed within three types whose formal definitions are given by considering their properties. Aiming to sufficiently describe and distinguish three types web events, a set of specially designed features are then extracted from the volume and the content of web events. Finally, a Bayesian-based model is proposed based on the designed features. The experimental results demonstrate the capability of the proposed model to distinguish types of web events, and the comparisons with other state-of-the-art classifiers also show the efficiency of the proposed model.

关键词： topic detection and tracking event classification Bayesian model web mining

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A Temporal topic Model for New Event detection

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International Journal of Computer Processing of Languages 2009年第1期22卷 99-130页

作者： YU HONG YU ZHANG TING LIU SHENG LI School of Computer Science and Technology Harbin Institute of Technology P. O. Box 321 Harbin Heilongjiang 150001 P. R. China

As an important task in topic detection and tracking (TDT), New Event detection (NED) aims to monitor the stream of news stories and detect new events reported by the first story of a topic. This paper proposes a Temporal topic Model (TTM) to describe a topic as a series of events corresponding to different time. NED, based on TTM, firstly identifies whether a story includes the same time expressions with old topics, and then it verifies whether the story and the topics include relevant events corresponding to the expressions. Thus, a story will be determined as a new event, if it includes much few simultaneous relevant events of old topics. Additionally, this paper analyzes the distribution of time expressions to identify both seminal and novel events, by which NED modifies the probabilities of stories to be new events based on whether they include seminal or novel events of old topics. We compare our methods with some existing NED systems on TDT4 corpus, which demonstrate our methods substantially improve the efficiency and accuracy of NED.

关键词： New event detection topic detection and tracking temporal topic model seminal event novel event

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Multiple developing news stories identified and tracked by social insects and visualized using the new galactic streams and concurrent streams metaphors

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International Journal of Hybrid Intelligent Systems 2015年第1期12卷 27-39页

作者： Sabo, Stefan Kovarova, Alena Navrat, Pavol Faculty of Informatics and Information Technologies Slovak University of Technology Bratislava Slovakia

We have developed an approach to identification and tracking of currently unfolding news stories extracted from the news articles published on the Web. Our approach employs a set of agents to retrieve those articles from the Web that might refer to some developing news story. The set of agents is inspired by social insects, in particular by a bee colony. Bees identify popular terms, referred to as story words, relevant to the ongoing news stories and use them in foraging articles. This allows for a dynamic approach that reflects the changes in article space as new stories unfold and new articles are added. Subsequently a graph representation of the article space is constructed that contains retrieved articles and identified story words interconnected by edges according to relationships of relevance identified between elements of the graph. Stories are then extracted from the constructed graph by using Louvain method, commonly used to identify communities within modular graphs. Using this approach we have been able to identify news stories in a stream of articles retrieved from the Web with precision of 75.56%, with best precision generally achieved for recent news stories described by popular story words. Further we developed ways of visualization of multiple stories represented by sets of articles ordered in time. We propose two new metaphors both employing an exponential timeline. Both galactic streams and concurrent streams are highly suitable for visualizing multiple developing stories.

关键词： Beehive metaphor community detection news stories social insect bee hive topic detection and tracking visualisation galactic streams concurrent streams

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An Automatic Participant detection Framework for Event tracking on Twitter

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ALGORITHMS 2021年第3期14卷 92-92页

作者： Mamo, Nicholas Azzopardi, Joel Layfield, Colin Univ Malta Fac ICT MSD-2080 Msida Malta

topic detection and tracking (TDT) on Twitter emulates human identifying developments in events from a stream of tweets, but while event participants are important for humans to understand what happens during events, machines have no knowledge of them. Our evaluation on football matches and basketball games shows that identifying event participants from tweets is a difficult problem exacerbated by Twitter's noise and bias. As a result, traditional Named Entity Recognition (NER) approaches struggle to identify participants from the pre-event Twitter stream. To overcome these challenges, we describe Automatic Participant detection (APD) to detect an event's participants before the event starts and improve the machine understanding of events. We propose a six-step framework to identify participants and present our implementation, which combines information from Twitter's pre-event stream and Wikipedia. In spite of the difficulties associated with Twitter and NER in the challenging context of events, our approach manages to restrict noise and consistently detects the majority of the participants. By empowering machines with some of the knowledge that humans have about events, APD lays the foundation not just for improved TDT systems, but also for a future where machines can model and mine events for themselves.

关键词： information retrieval automatic participant detection twitter event understanding topic detection and tracking event modelling

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Network Online Comments and Sentiment Features Analysis

Network Online Comments and Sentiment Features Analysis

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8th World Congress on Intelligent Control and Automation (WCICA)

作者： Wu, Hongjiang Mou, Dan Peng, Qinke Huang, Yongxuan Xi An Jiao Tong Univ Sch Elect & Informat Engn Xian 710049 Shaanxi Peoples R China

ISBN: (纸本)9781424467129

With the rapid development of Internet technology, it is more convenient for people to communicate with each other at any time, public opinion is created on Internet and influences people's daily life deeply, for example the decision-making of government, organization and person. Network online comments which carry public opinion become a hotspot for researching during these years. In this paper, we summarize network online comments and sentiment features analysis. We get the extraction, expression and quantification of sentiment features. We look for the law of sentiment changes in the sequence, in order to provide a basis for decision-making. In the future we will research the stock index prediction algorithm based on sentiment features extracted from network online comments.

关键词： network online comments topic detection and tracking sentiment features stock index

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An Improved Single-Pass Clustering Algorithm Internet-oriented Network topic detection

An Improved Single-Pass Clustering Algorithm Internet-orient...

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4th International Conference on Intelligent Control and Information Processing (ICICIP)

作者： Yi Xiaolin Zhao Xiao Ke Nan Zhao Fengchao Beijing Univ Technol Coll Comp Sci Beijing Peoples R China

ISBN: (纸本)9781467362498;9781467362481

The Single-Pass clustering algorithm, its two main disadvantages are easily affected by the orders of inputs of text and low precision when we use it to process the network text clustering. Through introducing the concept of seeds of topic, the paper proposed an improved Single-Pass clustering algorithm which inherited the main means of Single-Pass clustering algorithm. The experiment results showed that the improved algorithm could not only improve the speed of clustering, but also decrease the probabilities of miss detection, false detection, and the cost of wrong detection. The improved Single-Pass clustering algorithm that has improved the quality of clustering and topic detection both has high practicability and good reference value to the research of analysis for internet public opinion.

关键词： topic detection and tracking text clustering incremental clustering nearest neighbor-clustering

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SUBtopic-BASED MULTI-DOCUMENT SUMMARIZATION

SUBTOPIC-BASED MULTI-DOCUMENT SUMMARIZATION

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2009 International Conference on Machine Learning and Cybernetics(2009机器学习与控制论国际会议)

作者： LIN DAI JI-LIANG TANG YUN-QING XIA Beijing Laboratory of Intelligent Information Technology School of Computer Science Beijing Instit Tsinghua National Lab for Information Science and Technology Tsinghua University Beijing 100084 C

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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