TY - GEN
T1 - Connecting the dots between news articles
AU - Shahaf, Dafna
AU - Guestrin, Carlos
PY - 2011
Y1 - 2011
N2 - The process of extracting useful knowledge from large datasets has become one of the most pressing problems in today's society. The problem spans entire sectors, from scientists to intelligence analysts and web users, all of whom are constantly struggling to keep up with the larger and larger amounts of content published every day. With this much data, it is often easy to miss the big picture. In this paper, we investigate methods for automatically connecting the dots - providing a structured, easy way to navigate within a new topic and discover hidden connections. We focus on the news domain: given two news articles, our system automatically finds a coherent chain linking them together. For example, it can recover the chain of events leading from the decline of home prices (2007) to the health-care debate (2009). We formalize the characteristics of a good chain and provide efficient algorithms to connect two fixed endpoints. We incorporate user feedback into our framework, allowing the stories to be refined and personalized. Finally, we evaluate our algorithm over real news data. Our user studies demonstrate the algorithm's effectiveness in helping users understanding the news.
AB - The process of extracting useful knowledge from large datasets has become one of the most pressing problems in today's society. The problem spans entire sectors, from scientists to intelligence analysts and web users, all of whom are constantly struggling to keep up with the larger and larger amounts of content published every day. With this much data, it is often easy to miss the big picture. In this paper, we investigate methods for automatically connecting the dots - providing a structured, easy way to navigate within a new topic and discover hidden connections. We focus on the news domain: given two news articles, our system automatically finds a coherent chain linking them together. For example, it can recover the chain of events leading from the decline of home prices (2007) to the health-care debate (2009). We formalize the characteristics of a good chain and provide efficient algorithms to connect two fixed endpoints. We incorporate user feedback into our framework, allowing the stories to be refined and personalized. Finally, we evaluate our algorithm over real news data. Our user studies demonstrate the algorithm's effectiveness in helping users understanding the news.
UR - http://www.scopus.com/inward/record.url?scp=84871983535&partnerID=8YFLogxK
U2 - 10.5591/978-1-57735-516-8/IJCAI11-455
DO - 10.5591/978-1-57735-516-8/IJCAI11-455
M3 - ???researchoutput.researchoutputtypes.contributiontobookanthology.conference???
AN - SCOPUS:84871983535
SN - 9781577355120
T3 - IJCAI International Joint Conference on Artificial Intelligence
SP - 2734
EP - 2739
BT - IJCAI 2011 - 22nd International Joint Conference on Artificial Intelligence
T2 - 22nd International Joint Conference on Artificial Intelligence, IJCAI 2011
Y2 - 16 July 2011 through 22 July 2011
ER -