TY - GEN
T1 - EventSearch
T2 - 18th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, KDD 2012
AU - Shan, Dongdong
AU - Zhao, Wayne Xin
AU - Chen, Rishan
AU - Shu, Baihan
AU - Wang, Ziqi
AU - Yao, Junjie
AU - Yan, Hongfei
AU - Li, Xiaoming
PY - 2012
Y1 - 2012
N2 - We present EventSearch, a system for event extraction and retrieval on four types of news-related historical data, i.e., Web news articles, newspapers, TV news program, and micro-blog short messages. The system incorporates over 11 million web pages extracted from "Web InfoMall", the Chinese Web Archive since 2001. The newspaper and TV news video clips also span from 2001 to 2011. The system, upon a user query, returns a list of event snippets from multiple data sources. A novel burst model is used to discover events from time-stamped texts. In addition to offline event extraction, our system also provides online event extraction to further meet the user needs. EventSearch provides meaningful analytics that synthesize an accurate description of events. Users interact with the system by ranking the identified events using different criteria (scale, recency and relevance) and submitting their own information needs in different input fields.
AB - We present EventSearch, a system for event extraction and retrieval on four types of news-related historical data, i.e., Web news articles, newspapers, TV news program, and micro-blog short messages. The system incorporates over 11 million web pages extracted from "Web InfoMall", the Chinese Web Archive since 2001. The newspaper and TV news video clips also span from 2001 to 2011. The system, upon a user query, returns a list of event snippets from multiple data sources. A novel burst model is used to discover events from time-stamped texts. In addition to offline event extraction, our system also provides online event extraction to further meet the user needs. EventSearch provides meaningful analytics that synthesize an accurate description of events. Users interact with the system by ranking the identified events using different criteria (scale, recency and relevance) and submitting their own information needs in different input fields.
KW - event detection
KW - event search
UR - https://www.scopus.com/pages/publications/84866050949
U2 - 10.1145/2339530.2339781
DO - 10.1145/2339530.2339781
M3 - 会议稿件
AN - SCOPUS:84866050949
SN - 9781450314626
T3 - Proceedings of the ACM SIGKDD International Conference on Knowledge Discovery and Data Mining
SP - 1564
EP - 1567
BT - KDD'12 - 18th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining
Y2 - 12 August 2012 through 16 August 2012
ER -