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Automated story capture from internet weblogs
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Source
International Conference On Knowledge Capture archive
Proceedings of the 4th international conference on Knowledge capture table of contents
Whistler, BC, Canada
POSTER SESSION: Posters table of contents
Pages: 167 - 168  
Year of Publication: 2007
ISBN:978-1-59593-643-1
Authors
Andrew S. Gordon  University of Southern California, Marina del Rey, CA
Qun Cao  University of Southern California, Marina del Rey, CA
Reid Swanson  University of Southern California, Marina del Rey, CA
Sponsors
SIGART: ACM Special Interest Group on Artificial Intelligence
ACM: Association for Computing Machinery
Publisher
ACM  New York, NY, USA
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ABSTRACT

Among the most interesting ways that people share knowledge is through the telling of stories, i.e. first-person narratives about real-life experiences. Millions of these stories appear in Internet weblogs, offering a potentially valuable resource for future knowledge management and training applications. In this paper we describe efforts to automatically capture stories from Internet weblogs by extracting them using statistical text classification techniques. We evaluate the precision and recall performance of competing approaches. We describe the large-scale application of story extraction technology to Internet weblogs, producing a corpus of stories with over a billion words.



Collaborative Colleagues:
Andrew S. Gordon: colleagues
Qun Cao: colleagues
Reid Swanson: colleagues