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IEEE InternationalConference on Intelligence and Security Informatics (ISI 2006)

作者： Yang, Christopher C. Shi, Xiaodong Wei, Chih-Ping Chinese Univ Hong Kong Dept Syst Engn & Engn Management Hong Kong Hong Kong Peoples R China Natl Tsing Hua Univ Inst Technol Management Hsinchu Taiwan

ISBN: (纸本)3540344780

Since the September 11(th) terror attack at New York in 2001, the frequency of terror attacks around the world has been increasing and it draws more attention of the public. On January 20 of 2006, CNN reported that al Qaeda leader Osama bin Laden had released a tape claiming that a series of terror attacks were planned in US. These attacks and messages from terrorists are threatening everyone in the world. As an intelligence officer or a citizen in any countries. we are interested in the development of the terror attacks around us. We can easily extract hundreds or thousands of news stories of any terror attack incidents from newswires Such as *** but the volume of information is too large to capture the information we need. Information retrieval techniques such as topic detection and tracking are able to organize the news stories as events within a topic of terror attack. However. they are incapable to present the complex evolution relationships between the events. We are interested to learn what the major events but also how they develop within the topic of a terror attack. It is beneficial to identify the starting and ending events, the seminal events and the evolution of these events. In this work, we propose to Utilize the temporal relationship, event similarity. temporal proximity and document distributional proximity to identify the event evolution relationships between events in a terror attack incident. An event evolution graph is utilized to present the Underlying structure of events for efficient browsing and extracting information. Case study and experiment are presented to illustrate and show the performance of our proposing technique.

关键词： security informatics topic detection and tracking event evolution

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Online Discussion Transition Analysis for Group Learning Support

Online Discussion Transition Analysis for Group Learning Sup...

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

作者： Sumikawa, Yasunobu Takada, Akikazu Ichinose, Akira Murakami, Ari Toyono, Yuki Ikejiri, Ryohei Sakasegawa, Meiko Sekine, Kaoru Yamauchi, Yuhei Tukushoku Univ Tokyo Japan Ddrive KK Ehime Japan First Torrent LLC Fukuoka Japan Univ Tokyo Tokyo Japan Thhoku Univ Miyagi Japan

ISBN: (纸本)9781665494021

Group learning has been recognized as an effective learning method. Recently, group learning using online communication tools and face-to-face group learning have been increasing. This change in the learning environment has highlighted the importance of supporting teachers in monitoring the learning progress of each group. In this study, we propose an algorithm to analyze the discussion transitions. The algorithm first extracts the entities and Wikipedia categories from the discussion text. It then applies a queue model, which limits the analysis to the most recent topic, or a memory model, which emphasizes repeated topics. Subsequently, it triggers another algorithm that allocates a Wikipedia category as the main theme for each discussion text. To evaluate the effectiveness of the proposed algorithms, we tested them on the W2E dataset, which includes topics comprising multiple events and an actual conversational dataset. The test results confirmed that the algorithms analyzed the discussion transitions and main themes with high accuracy.

关键词： Discussion analysis transition analysis topic detection and tracking Wikipedia

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Adaptive topic Modeling with Probabilistic Pseudo Feedback in Online topic detection

Adaptive Topic Modeling with Probabilistic Pseudo Feedback i...

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15th International Conference on Applications of Natural Language to Information Systems

作者： Tang, Guoyu Xia, Yunqing Tsinghua Univ Dept Comp Sci & Technol Beijing 100084 Peoples R China

ISBN: (纸本)9783642138805

Online topic detection (OTD) system seeks to analyze sequential stories in a real-time manner so as to detect new topics or to associate stories with certain existing topics. To handle new stories more precisely, an adaptive topic modeling method that incorporates probabilistic pseudo feedback is proposed in this paper to tune every topic model with a changed environment. Differently, this method considers every incoming story as pseudo feedback with certain probability, which is the similarity between the story and the topic. Experiment results show that probabilistic pseudo feedback brings promising improvement to online topic detection.

关键词： Adaptive topic modeling probabilistic pseudo feedback online topic detection topic detection and tracking

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CStory: A Chinese Large-scale News Storyline Dataset 22

CStory: A Chinese Large-scale News Storyline Dataset

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31st ACM International Conference on Information and Knowledge Management (CIKM)

作者： Shi, Kaijie Wang, Xiaozhi Yu, Jifan Hou, Lei Li, Juanzi Wu, Jingtong Yong, Dingyu Xiao, Jinghui Liu, Qun Tsinghua Univ Beijing Peoples R China Tsinghua Univ BNRist Dept Comp Sci & Technol Beijing Peoples R China Beijing Huawei Digital Technol Co Ltd Beijing Peoples R China Huawei Device Co Ltd Beijing Peoples R China Huawei Noahs Ark Lab Beijing Peoples R China

ISBN: (纸本)9781450392365

In today's massive news streams, storylines can help us discover related event pairs and understand the evolution of hot events. Hence many efforts have been devoted to automatically constructing news storylines. However, the development of these methods is strongly limited by the size and quality of existing storyline datasets since news storylines are expensive to annotate as they contain a myriad of unlabeled relationships growing quadratically with the number of news events. Working around these difficulties, we propose a sophisticated pre-processing method to filter candidate news pairs by entity co-occurrence and semantic similarity. With the filter reducing annotation overhead, we construct CStory, a large-scale Chinese news storyline dataset, which contains 11, 978 news articles, 112, 549 manually labeled storyline relation pairs, and 49, 832 evidence sentences for annotation judgment. We conduct extensive experiments on CStory using various algorithms and find that constructing news storylines is challenging even for pre-trained language models. Empirical analysis shows that the sample unbalance issue significantly influences model performance, which shall be the focus of future works. Our dataset is now publicly available at https://***/THU-KEG/CStory.

关键词： Storyline Datasets Event Evolution Storyline Relation topic detection and tracking Imbalanced Dataset

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Building a hierarchy of events and topics for newspaper digital libraries

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25th European Conference on Information Retrieval Research (ECIR 2003)

作者： Pons-Porrata, A Berlanga-Llavori, R Ruiz-Shulcloper, J Univ Oriente Santiago De Cuba Cuba Univ Jaume 1 Castellon de La Plana Spain Inst Cybernet Math & Phys Havana Cuba

ISBN: (纸本)3540012745

In this paper we propose an incremental hierarchical clustering algorithm for on-line event detection. This algorithm is applied to a set of newspaper articles in order to discover the structure of topics and events that they describe. In the first level, articles with a high temporal-semantic similarity are clustered together into events. In the next levels of the hierarchy, these events are successively clustered so that composite events and topics can be discovered. The results obtained for the F1-measure and the detection Cost demonstrate the validity of our algorithm for on-line event detection tasks.

关键词： topic detection and tracking text clustering

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Phoneme Lattice based TextTiling towards Multilingual Story Segmentation

Phoneme Lattice based TextTiling towards Multilingual Story ...

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11th Annual Conference of the International-Speech-Communication-Association 2010

作者： Wang, Xiaoxuan Xie, Lei Ma, Bin Chng, Eng Siong Li, Haizhou Northwestern Polytech Univ Sch Comp Sci Xian Peoples R China Inst Infocomm Res Singapore Singapore Nanyang Technol Univ Sch Comp Engn Singapore Singapore

ISBN: (纸本)9781617821233

This paper proposes a phoneme lattice based Text Tiling approach towards multilingual story segmentation. The phoneme is the smallest segmental unit in a language and the number of phonemes in a language is usually far smaller than the number of words. Furthermore, many phonemes are shared by different languages. These properties make phonemes particularly appropriate for representing multilingual speech. As phoneme recognition is far from perfect, phoneme lattices, which carry much richer statistics than the 1-best hypotheses, are adopted in this paper as the input to the Text Tiling approach. The term frequencies used in traditional Text Tiling are replaced by the expected counts of phoneme n-gram units calculated from phoneme lattices. Experiments on TDT2 English and Mandarin corpora show that the phoneme lattice based Text Tiling outperforms the phoneme 1-best based Text Tiling and word based Text Tiling in broadcast news story segmentation.

关键词： story segmentation topic detection and tracking spoken document retrieval phoneme lattice speech processing

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Related topic network

Related topic network

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2010 IEEE International Conference on Service Operations and Logistics, and Informatics, SOLI 2010

作者： Chang, Chao Zeng, Daniel Zhao, Huimin Key Laboratory of Complex Systems and Intelligence Science Institute of Automation Chinese Academy of Sciences Beijing China Department of Management Information Systems University of Arizona United States Sheldon B. Lubar School of Business University of Wisconsin Milwaukee United States

ISBN: (纸本)9781424471188

topic detection and tracking provides a flat and unorganized view of a document collection and cannot adequately reflect the content of the complete collection as some of the information is lost in the process. topic models account for more information and lead to a more organized view of the document collection. In this paper, we propose a more efficient model named Related topic Network with a new term weighting method. Empirical evaluation using two real-world datasets consisting of 953 and 5,550 news documents demonstrates the utility of the proposed model and shows that the new term weighting method leads to performance improvement. copy;2010 IEEE.

关键词： Related topic network topic detection and tracking topic model

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Network Structures and User Behavior Analysis Based Hot topic detection for Internet Forum

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JOURNAL OF INTERNET TECHNOLOGY 2008年第3期9卷 267-272页

作者： Zhang, Li Liu, Yun Beijing Jiaotong Univ Sch Elect & Informat Engn Beijing Peoples R China Beijing Jiaotong Univ Key Lab Commun & Informat Syst Beijing Peoples R China Beijing Municipal Commiss Educ Beijing Peoples R China

The development of Internet links the users from all over the world and these users generate and reply topics in many dynamic web applications like blog and internet forums. It is almost impossible for people to focus the key point. As a solution, we present a novel method to detect the hot topics mainly focused on the Internet forum. Our solution considers two important characteristics of Internet forum: the network structures and the user behavior patterns. First, we construct a network of the users' "replied to" relationships in a given Internet forum. Second, behavior patterns for all users are computed. Finally, we combine the results from first two steps to determine which topics are hot topics. Results of our empirical experiments show that our approach can identify hot topics easily and effectively.

关键词： hot topic detection complex networks topic detection and tracking behavior patterns

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A temporal ontology guided clustering methodology with a case study on detection and tracking of artificial intelligence topics

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EXPERT SYSTEMS WITH APPLICATIONS 2024年 247卷

作者： Vahidnia, Sahand Abbasi, Alireza Abbass, Hussein Univ New South Wales UNSW Sch Engn & IT Canberra ACT 2612 Australia

detection and tracking of topics from publicly available academic data can benefit the scientific community and other stakeholders throughout their investment and other decisions, by informing the decisions regarding the field of science, its evolution, and its dynamics. In this study, we introduce a novel temporal clustering method for topic detection, using document abstracts, keywords, and their corresponding textual representations. In this method, the temporal dimension is employed to parameterise the effect of older data on the clusters, while ontology guidance is utilised to guide their evolution. Ontology is used for both enhancing the representations, and decision -making for the evolutionary steps of split and merging of the clusters. We show the effectiveness of the representations of documents in a single time slice, before demonstrating the evolution of topics in a case study of AI -related publications. Finally, the resulting topic evolutionary map is evaluated after automatically labelling the clusters using ranked author keywords, facilitating the assessment of the topics and observing their evolution.

关键词： topic detection and tracking Document clustering Document topic models Temporal clustering Representation learning

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Detecting and tracking topics and Events from Web Search Logs

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ACM TRANSACTIONS ON INFORMATION SYSTEMS 2012年第4期30卷 21-21页

作者： Liu, Hongyan He, Jun Gu, Yingqin Xiong, Hui Du, Xiaoyong Tsinghua Univ Dept Management Sci & Engn Beijing Peoples R China Renmin Univ China Sch Informat Minist Educ Key Labs Data Engn & Knowledge Engn Beijing Peoples R China Rutgers State Univ Management Sci & Informat Syst Dept Piscataway NJ 08855 USA

Recent years have witnessed increased efforts on detecting topics and events from Web search logs, since this kind of data not only capture web content but also reflect the users' activities. However, the majority of existing work is focused on exploiting clustering techniques for topic and event detection. Due to the huge size and the evolving nature of Web data, existing clustering approaches are limited to meet the real-time demand. To that end, in this article, we propose a method called LETD to detect evolving topics in a timely manner. Also, we design the techniques to extract events from topics and to infer the evolving relationship among the events. For topic detection, we first provide a measurement to select the important URLs, which are most likely to describe a real-life topic. Then, starting from these selected URLs, we exploit the local expansion method to find other topic-related URLs. Moreover, in the LETD framework, we design algorithms based on Random Walk and Markov Random Fields (MRF), respectively. Because the LETD method exploits a divide-and-conquer strategy to process the data, it is more efficient than existing methods based on clustering techniques. To better illustrate the LETD framework, we develop a demo system Story Teller which can discover hot topics and events, infer the evolving relationships among events, and visualize information in a storytelling way. This demo system can provide a global view of the topic development and help users target the interesting events more conveniently. Finally, experimental results on real-world Microsoft click-through data have shown that Story Teller can find real-life hot topics and meaningful evolving relationships among events, and has also demonstrated the efficiency and effectiveness of the LETD method.

关键词： Algorithm Experimentation Performance topic detection and tracking Random Walk Markov Random Fields Web search log

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