R. Swan, J. Allan, "Automatic generation of overview timelines," Proc. SIGIR Intl. Conf. on Information Retrieval, 2000.  Home/Search   Document Not in Database   Summary   Related Articles   Check

....time scale, the published literature in a particular research field can be meaningfully understood in this way as well, with particular research themes growing and diminishing in visibility across a period of years. Work in the areas of topic detection and tracking [2, 3, 6, 67, 68] text mining [39, 62, 63, 64], and visualization [29, 47, 66] has explored techniques for identifying topics in document streams comprised of news stories, using a combination of content analysis and time series modeling. Underlying a number of these techniques is the following intuitive premise that the appearance of a ....

....many other datasets of an analogous flavor; in Section 4, I also discuss an example based on U.S. Presidential State of the Union Addresses from 1790 to 2002. Section 5 discusses the connections to related work in a range of areas, particularly the striking recent work of Swan, Allan, and Jensen [62, 63, 64] on overview timelines, which forms the body of research closest to the approach here. Finally, Section 6 discusses some further applications of the 4 methodology how burstiness in arrivals can help to identify certain messages as landmarks in a large corpus of e mail; and how the overall ....

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R. Swan, J. Allan, "Automatic generation of overview timelines," Proc. SIGIR Intl. Conf. Information Retrieval, 2000.

.... 02 Edmonton, Alberta, Canada Copyright 2001 ACM X XXXXX XX X XX XX . 5.00. ingfully understood in this way as well, with particular research themes growing and diminishing in visibility across a period of years. Work in the areas of topic dection and tracking [2, 3, 5, 61, 62] text mining [36, 56, 57, 58], and visualization [26, 43, 60] has explored techniques for identifying topics in document streams comprised of news stories, using a combination of content analysis and time series modeling. Underlying a number of these techniques is the following intuitive premise that the appearance of a ....

....the past several decades; the set of bursts in this stream corresponds roughly to the appearance and disappearance of certain terms of interest in the papers. Section 5 discusses the connections to related work in a range of areas, particularly the striking recent work of Swan, Allan, and Jensen [56, 57, 58] on overview timelines, which forms the body of research closest to the approach here. Finally, Section 6 discusses some further applications of the methodology how burstiness in arrivals can help to identify certain messages as landmarks in a large corpus of e mail; and how the overall ....

[Article contains additional citation context not shown here]

R. Swan, J. Allan, "Automatic generation of overview timelines," Proc. SIGIR Intl. Conf. Information Retrieval, 2000.

....Edmonton, Alberta, Canada Copyright 2002 ACM 1 58113 567 X 02 0007 . 5.00. ingfully understood in this way as well, with particular research themes growing and diminishing in visibility across a period of years. Work in the areas of topic detection and tracking [2, 3, 5, 64, 65] text mining [37, 59, 60, 61], and visualization [27, 45, 63] has explored techniques for identifying topics in document streams comprised of news stories, using a combination of content analysis and time series modeling. Underlying a number of these techniques is the following intuitive premise that the appearance of a ....

....many other datasets of an analogous flavor; in Section 4, I also discuss an example based on U.S. Presidential State of the Union Addresses from 1790 to 2002. Section 5 discusses the connections to related work in a range of areas, particularly the striking recent work of Swan, Allan, and Jensen [59, 60, 61] on overview timelines, which forms the body of research closest to the approach here. Finally, Section 6 discusses some further applications of the methodology how burstiness in arrivals can help to identify certain messages as landmarks in a large corpus of e mail; and how the overall ....

[Article contains additional citation context not shown here]

R. Swan, J. Allan, "Automatic generation of overview timelines," Proc. SIGIR Intl. Conf. Information Retrieval, 2000.

....time scale, the published literature in a particular research field can be meaningfully understood in this way as well, with particular research themes growing and diminishing in visibility across a period of years. Work in the areas of topic detection and tracking [2, 3, 5, 61, 62] text mining [36, 56, 57, 58], and visualization [26, 43, 60] has explored techniques for identifying topics in document streams comprised of news stories, using a combination of content analysis and time series modeling. Underlying a number of these techniques is the following intuitive premise that the appearance of a ....

....the past several decades; the set of bursts in this stream corresponds roughly to the appearance and disappearance of certain terms of interest in the papers. Section 5 discusses the connections to related work in a range of areas, particularly the striking recent work of Swan, Allan, and Jensen [56, 57, 58] on overview timelines, which forms the body of research closest to the approach here. Finally, Section 6 discusses some further applications of the methodology how burstiness in arrivals can help to identify certain messages as landmarks in a large corpus of e mail; and how the overall ....

[Article contains additional citation context not shown here]

R. Swan, J. Allan, "Automatic generation of overview timelines," Proc. SIGIR Intl. Conf. Information Retrieval, 2000.

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Russell Swan and James Allan. Automatic generation of overview timelines. In Proceedings of SIGIR, pages 49--56, Athens, Greece, 2000. ACM.

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R. Swan and J. Allan. Automatic Generation of Overview Timelines. Proceedings of SIGIR 2000.

....automatically constructing overviews of text corpora suitable for browsing using timelines. We have built a system, TimeMines, that takes a time tagged collection and generates an interactive overview timeline, showing the major topics covered by a corpus, and the dates of coverage of the topics[16]. We refer to these as overview timelines . Figures 1 and 2 are sample timelines generated by TimeMines from a 1995 and a 1998 news corpus. TimeMines makes few assumptions about the data the data are free text, and explicit date tags are associated with each document. While some databases ....

....TimeMines uses statistical techniques, based on classical hypothesis testing, that are simple, extremely fast, and work surprisingly well at gisting a corpus and finding the most salient temporal features. We have presented initial research results at information re trieval conferences[15, 16]. We present our research here because we feel our results are interesting from a knowledge discovery perspective our selection of features is based on statistical techniques to find hidden patterns implicit within the data. The statistical model we propose is not novel; it is based on ....

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Russell Swan and James Allan. Automatic generation of overview timelines. In Proceedings of the 23rd Annual International A CM SIGIR Conference on Research and Development in Information Retrieval (these proceedings), Athens, Greece, 2000. Association for Computing Machinery.

....that almost every sentence in the news contains some new information even if it is just the age of a person in the news. In this research, we have chosen a looser definition of event that makes this less of a problem. This research is also related to work on automatic timeline construction [38]. That work focused on using the # 2 measure to extract unusual words and phrases from a stream of news, and on grouping those features to isolate topics within the news. They suggested the idea of looking within the topics to create an eventlevel timeline, but have not yet done so. Further, ....

R. Swan and J. Allan. Automatic generation of overview timelines. In Proceedings of SIGIR, pages 49--56, Athens, Greece, 2000. ACM.

....to be high content bearing. We are then able to group these features into clusters that correspond strongly with the notion of topic as defined in the Topic Detection and Tracking (TDT) study. Figures 4 and 5 show examples of the system running. This work is described in more detail elsewhere. [5, 4] With the model that tokens are emitted by random processes, we assume two hypotheses as defaults. The assumptions are 1: the random processes generating tokens are stationary, meaning that they do not vary over time, and 2: the random processes for any pair of January 26 27 28 29 30 31 ....

Russell Swan and James Allan. Automatic generation of overview timelines. Technical Report IR-198, University of Massachusetts, Department of Computer Science (CIIR), 2000. Conference submission.

....to be high content bearing. We are then able to group these features into clusters that correspond strongly with the notion of topic as defined in the Topic Detection and Tracking (TDT) study. Figures 4 and 5 show examples of the system running. This work is described in more detail elsewhere. [5, 4] With the model that tokens are emitted by random processes, we assume two hypotheses as defaults. The assumptions are 1: the random processes generating tokens are stationary, meaning that they do not vary over time, and 2: the random processes for any pair of January 26 27 28 29 30 31 ....

Russell Swan and James Allan. Automatic generation of overview timelines. Technical Report IR-198, University of Massachusetts, Department of Computer Science (CIIR), 2000. Conference submission.

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R. Swan, J. Allan, "Automatic generation of overview timelines," Proc. SIGIR Intl. Conf. on Information Retrieval, 2000.

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Swan, R., & Allan, J. (2000). Automatic generation of overview timelines. SIGIR '00.

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R. C. Swan and J. Allan. Automatic generation of overview timelines. In Proceedings of the 23rd ACM SIGIR International Conference on Research and Development in Information Retrieval (SIGIR'00), 2000.

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R. Swan and J. Allan. 2000. Automatic generation of overview timelines. In Proceedings of the Special Interest Group in Information Retrieval, pages 47--55.

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R. Swan, J. Allan, "Automatic generation of overview timelines," Proc. SIGIR Intl. Conf. Information Retrieval, 2000.

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Russell Swan and James Allan. Automatic generation of overview timelines. In Proceedings of the 23rd Annual International ACM SIGIR Conference on Research and Development in Information Retrieval, pages 49--56, Athens, Greece, July 2000.

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Russell Swan and James Allan. Automatic generation of overview timelines. In Proceedings of the 23rd Annual International ACM SIGIR Conference on Research and Development in Information Retrieval, pages 49--56, Athens, Greece, July 2000.

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