A probabilistic model for retrospective news event detection

Zhiwei Li; Bin Wang; Mingjing Li; Wei-Ying Ma

doi:10.1145/1076034.1076055

A probabilistic model for retrospective news event detection

Zhiwei Li
, Bin Wang
, Mingjing Li
, Wei-Ying Ma

Research output: Chapters, Conference Papers, Creative and Literary Works › RGC 32 - Refereed conference paper (with host publication) › peer-review

166 Citations (Scopus)

Abstract

Retrospective news event detection (RED) is defined as the discovery of previously unidentified events in historical news corpus. Although both the contents and time information of news articles are helpful to RED, most researches focus on the utilization of the contents of news articles. Few research works have been carried out on finding better usages of time information. In this paper, we do some explorations on both directions based on the following two characteristics of news articles. On the one hand, news articles are always aroused by events; on the other hand, similar articles reporting the same event often redundantly appear on many news sources. The former hints a generative model of news articles, and the latter provides data enriched environments to perform RED. With consideration of these characteristics, we propose a probabilistic model to incorporate both content and time information in a unified framework. This model gives new representations of both news articles and news events. Furthermore, based on this approach, we build an interactive RED system, HISCOVERY, which provides additional functions to present events, Photo Story and Chronicle. © 2005 ACM.

Original language	English
Title of host publication	SIGIR 2005 - Proceedings of the 28th Annual International ACM SIGIR Conference on Research and Development in Information Retrieval
Pages	106-113
DOIs	https://doi.org/10.1145/1076034.1076055
Publication status	Published - 2005
Externally published	Yes
Event	28th Annual International ACM SIGIR Conference on Research and Development in Information Retrieval, SIGIR 2005 - Salvador, Brazil Duration: 15 Aug 2005 → 19 Aug 2005 https://dl.acm.org/doi/proceedings/10.1145/1076034

Publication series

Name	SIGIR 2005 - Proceedings of the 28th Annual International ACM SIGIR Conference on Research and Development in Information Retrieval

Conference

Conference	28th Annual International ACM SIGIR Conference on Research and Development in Information Retrieval, SIGIR 2005
Place	Brazil
City	Salvador
Period	15/08/05 → 19/08/05
Internet address	https://dl.acm.org/doi/proceedings/10.1145/1076034

Bibliographical note

Publication details (e.g. title, author(s), publication statuses and dates) are captured on an “AS IS” and “AS AVAILABLE” basis at the time of record harvesting from the data source. Suggestions for further amendments or supplementary information can be sent to [email protected].

Research Keywords

clustering
expectation maximization
maximum likelihood
retrospective news event detection

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10.1145/1076034.1076055

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Li, Z., Wang, B., Li, M., & Ma, W.-Y. (2005). A probabilistic model for retrospective news event detection. In SIGIR 2005 - Proceedings of the 28th Annual International ACM SIGIR Conference on Research and Development in Information Retrieval (pp. 106-113). (SIGIR 2005 - Proceedings of the 28th Annual International ACM SIGIR Conference on Research and Development in Information Retrieval). https://doi.org/10.1145/1076034.1076055

Li, Zhiwei ; Wang, Bin ; Li, Mingjing et al. / A probabilistic model for retrospective news event detection. SIGIR 2005 - Proceedings of the 28th Annual International ACM SIGIR Conference on Research and Development in Information Retrieval. 2005. pp. 106-113 (SIGIR 2005 - Proceedings of the 28th Annual International ACM SIGIR Conference on Research and Development in Information Retrieval).

@inproceedings{bca6eb83e11f47eab080119474f5f20b,

title = "A probabilistic model for retrospective news event detection",

abstract = "Retrospective news event detection (RED) is defined as the discovery of previously unidentified events in historical news corpus. Although both the contents and time information of news articles are helpful to RED, most researches focus on the utilization of the contents of news articles. Few research works have been carried out on finding better usages of time information. In this paper, we do some explorations on both directions based on the following two characteristics of news articles. On the one hand, news articles are always aroused by events; on the other hand, similar articles reporting the same event often redundantly appear on many news sources. The former hints a generative model of news articles, and the latter provides data enriched environments to perform RED. With consideration of these characteristics, we propose a probabilistic model to incorporate both content and time information in a unified framework. This model gives new representations of both news articles and news events. Furthermore, based on this approach, we build an interactive RED system, HISCOVERY, which provides additional functions to present events, Photo Story and Chronicle. {\textcopyright} 2005 ACM.",

keywords = "clustering, expectation maximization, maximum likelihood, retrospective news event detection",

author = "Zhiwei Li and Bin Wang and Mingjing Li and Wei-Ying Ma",

note = "Publication details (e.g. title, author(s), publication statuses and dates) are captured on an “AS IS” and “AS AVAILABLE” basis at the time of record harvesting from the data source. Suggestions for further amendments or supplementary information can be sent to [email protected].; 28th Annual International ACM SIGIR Conference on Research and Development in Information Retrieval, SIGIR 2005 ; Conference date: 15-08-2005 Through 19-08-2005",

year = "2005",

doi = "10.1145/1076034.1076055",

language = "English",

isbn = "1595930345",

series = "SIGIR 2005 - Proceedings of the 28th Annual International ACM SIGIR Conference on Research and Development in Information Retrieval",

pages = "106--113",

booktitle = "SIGIR 2005 - Proceedings of the 28th Annual International ACM SIGIR Conference on Research and Development in Information Retrieval",

url = "https://dl.acm.org/doi/proceedings/10.1145/1076034",

}

Li, Z, Wang, B, Li, M & Ma, W-Y 2005, A probabilistic model for retrospective news event detection. in SIGIR 2005 - Proceedings of the 28th Annual International ACM SIGIR Conference on Research and Development in Information Retrieval. SIGIR 2005 - Proceedings of the 28th Annual International ACM SIGIR Conference on Research and Development in Information Retrieval, pp. 106-113, 28th Annual International ACM SIGIR Conference on Research and Development in Information Retrieval, SIGIR 2005, Salvador, Brazil, 15/08/05. https://doi.org/10.1145/1076034.1076055

A probabilistic model for retrospective news event detection. / Li, Zhiwei; Wang, Bin; Li, Mingjing et al.
SIGIR 2005 - Proceedings of the 28th Annual International ACM SIGIR Conference on Research and Development in Information Retrieval. 2005. p. 106-113 (SIGIR 2005 - Proceedings of the 28th Annual International ACM SIGIR Conference on Research and Development in Information Retrieval).

Research output: Chapters, Conference Papers, Creative and Literary Works › RGC 32 - Refereed conference paper (with host publication) › peer-review

TY - GEN

T1 - A probabilistic model for retrospective news event detection

AU - Li, Zhiwei

AU - Wang, Bin

AU - Li, Mingjing

AU - Ma, Wei-Ying

N1 - Publication details (e.g. title, author(s), publication statuses and dates) are captured on an “AS IS” and “AS AVAILABLE” basis at the time of record harvesting from the data source. Suggestions for further amendments or supplementary information can be sent to [email protected].

PY - 2005

Y1 - 2005

N2 - Retrospective news event detection (RED) is defined as the discovery of previously unidentified events in historical news corpus. Although both the contents and time information of news articles are helpful to RED, most researches focus on the utilization of the contents of news articles. Few research works have been carried out on finding better usages of time information. In this paper, we do some explorations on both directions based on the following two characteristics of news articles. On the one hand, news articles are always aroused by events; on the other hand, similar articles reporting the same event often redundantly appear on many news sources. The former hints a generative model of news articles, and the latter provides data enriched environments to perform RED. With consideration of these characteristics, we propose a probabilistic model to incorporate both content and time information in a unified framework. This model gives new representations of both news articles and news events. Furthermore, based on this approach, we build an interactive RED system, HISCOVERY, which provides additional functions to present events, Photo Story and Chronicle. © 2005 ACM.

AB - Retrospective news event detection (RED) is defined as the discovery of previously unidentified events in historical news corpus. Although both the contents and time information of news articles are helpful to RED, most researches focus on the utilization of the contents of news articles. Few research works have been carried out on finding better usages of time information. In this paper, we do some explorations on both directions based on the following two characteristics of news articles. On the one hand, news articles are always aroused by events; on the other hand, similar articles reporting the same event often redundantly appear on many news sources. The former hints a generative model of news articles, and the latter provides data enriched environments to perform RED. With consideration of these characteristics, we propose a probabilistic model to incorporate both content and time information in a unified framework. This model gives new representations of both news articles and news events. Furthermore, based on this approach, we build an interactive RED system, HISCOVERY, which provides additional functions to present events, Photo Story and Chronicle. © 2005 ACM.

KW - clustering

KW - expectation maximization

KW - maximum likelihood

KW - retrospective news event detection

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UR - https://www.scopus.com/record/pubmetrics.uri?eid=2-s2.0-84885589518&origin=recordpage

U2 - 10.1145/1076034.1076055

DO - 10.1145/1076034.1076055

M3 - RGC 32 - Refereed conference paper (with host publication)

SN - 1595930345

SN - 9781595930347

T3 - SIGIR 2005 - Proceedings of the 28th Annual International ACM SIGIR Conference on Research and Development in Information Retrieval

SP - 106

EP - 113

BT - SIGIR 2005 - Proceedings of the 28th Annual International ACM SIGIR Conference on Research and Development in Information Retrieval

T2 - 28th Annual International ACM SIGIR Conference on Research and Development in Information Retrieval, SIGIR 2005

Y2 - 15 August 2005 through 19 August 2005

ER -

Li Z, Wang B, Li M, Ma WY. A probabilistic model for retrospective news event detection. In SIGIR 2005 - Proceedings of the 28th Annual International ACM SIGIR Conference on Research and Development in Information Retrieval. 2005. p. 106-113. (SIGIR 2005 - Proceedings of the 28th Annual International ACM SIGIR Conference on Research and Development in Information Retrieval). doi: 10.1145/1076034.1076055

A probabilistic model for retrospective news event detection

Abstract

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Bibliographical note

Research Keywords

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