Event relationship extraction is a new research domain which has attracted more and more attentions. It is because that the relationships among different events can provide a lot of important information on special fields such as national defense, crime-solving or Anti-Terrorism. But there are innumerous event description web pages on the internet and the relationship among them is perplexing, so obviously it is impossible to extract them manually. In order to extract the relationship automatically, recognizing the key elements of the event is an indispensable preprocessing phase. At this step, semi-CRFs is a good Name Entity Recognition model, but it is too slow to fit the requirements of large scale processing. This paper has accelerated the semi-CRFs inference algorithm and presents a fast events elements extraction method. Based on it, the events relationship map is also extracted automatically. All event elements such as the agents, location, time or related organization are used as slots to connect different events so as to provide more plentiful information for the supervisor.
[1]
Hai Leong Chieu,et al.
Query based event extraction along a timeline
,
2004,
SIGIR '04.
[2]
James Allan,et al.
Temporal summaries of new topics
,
2001,
SIGIR '01.
[3]
William W. Cohen,et al.
Semi-Markov Conditional Random Fields for Information Extraction
,
2004,
NIPS.
[4]
Kathleen R. McKeown,et al.
Columbia multi-document summarization : Approach and evaluation
,
2001
.
[5]
Seung-Shik Kang,et al.
Event Sentence Extraction in Korean Newspapers
,
2003,
CICLing.
[6]
Jakub Piskorski,et al.
Real-Time News Event Extraction for Global Crisis Monitoring
,
2008,
NLDB.
[7]
Katsumi Tanaka,et al.
Creating Personal Histories from the Web Using Namesake Disambiguation and Event Extraction
,
2007,
ICWE.