TY - GEN
T1 - Top-down and bottom-up
T2 - 6th Asia Information Retrieval Societies Conference, AIRS 2010
AU - Chen, Zheng
AU - Tamang, Suzanne
AU - Lee, Adam
AU - Li, Xiang
AU - Passantino, Marissa
AU - Ji, Heng
N1 - This work was supported by the U.S. Army Research Laboratory under Cooperative Agreement Number W911NF-09-2-0053, the U.S. NSF CAREER Award under Grant IIS-0953149, Google, Inc., DARPA GALE Program, CUNY Research Enhancement Program, PSC-CUNY Research Program, Faculty Publication Program and GRTI Program. The views and conclusions contained in this document are those of the authors and should not be interpreted as representing the official policies, either expressed or implied, of the Army Research Laboratory or the U.S. Government. The U.S. Government is authorized to reproduce and distribute reprints for Government purposes notwithstanding any copyright notation here on.
PY - 2010
Y1 - 2010
N2 - The Slot Filling task requires a system to automatically distill information from a large document collection and return answers for a query entity with specified attributes ('slots'), and use them to expand the Wikipedia infoboxes. We describe two bottom-up Information Extraction style pipelines and a top-down Question Answering style pipeline to address this task. We propose several novel approaches to enhance these pipelines, including statistical answer re-ranking and Markov Logic Networks based cross-slot reasoning. We demonstrate that our system achieves state-of-the-art performance, with 3.1% higher precision and 2.6% higher recall compared with the best system in the KBP2009 evaluation.
AB - The Slot Filling task requires a system to automatically distill information from a large document collection and return answers for a query entity with specified attributes ('slots'), and use them to expand the Wikipedia infoboxes. We describe two bottom-up Information Extraction style pipelines and a top-down Question Answering style pipeline to address this task. We propose several novel approaches to enhance these pipelines, including statistical answer re-ranking and Markov Logic Networks based cross-slot reasoning. We demonstrate that our system achieves state-of-the-art performance, with 3.1% higher precision and 2.6% higher recall compared with the best system in the KBP2009 evaluation.
KW - Information Extraction
KW - Question Answering
KW - Slot Filling
UR - http://www.scopus.com/inward/record.url?scp=78650898651&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=78650898651&partnerID=8YFLogxK
U2 - 10.1007/978-3-642-17187-1_29
DO - 10.1007/978-3-642-17187-1_29
M3 - Conference contribution
AN - SCOPUS:78650898651
SN - 3642171869
SN - 9783642171864
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 300
EP - 309
BT - Information Retrieval Technology - 6th Asia Information Retrieval Societies Conference, AIRS 2010, Proceedings
Y2 - 1 December 2010 through 3 December 2010
ER -