TY - GEN
T1 - Unsupervised web name disambiguation using semantic similarity and single-pass clustering
AU - Iosif, Elias
PY - 2010
Y1 - 2010
N2 - In this paper, we propose a method for name disambiguation. For a given set of names and documents we cluster the documents and map each cluster to the appropriate name. The proposed method incorporates an unsupervised metric for semantic similarity computation and a computationally low-cost clustering algorithm. We experimented with the data used in Web People Search Task of SemEval-2007, in which 16 different teams were participated. The proposed system has an equal performance compared to the officially best system.
AB - In this paper, we propose a method for name disambiguation. For a given set of names and documents we cluster the documents and map each cluster to the appropriate name. The proposed method incorporates an unsupervised metric for semantic similarity computation and a computationally low-cost clustering algorithm. We experimented with the data used in Web People Search Task of SemEval-2007, in which 16 different teams were participated. The proposed system has an equal performance compared to the officially best system.
UR - http://www.scopus.com/inward/record.url?scp=78650507705&partnerID=8YFLogxK
U2 - 10.1007/978-3-642-12842-4_17
DO - 10.1007/978-3-642-12842-4_17
M3 - Conference contribution
AN - SCOPUS:78650507705
SN - 3642128416
SN - 9783642128417
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 133
EP - 141
BT - Artificial Intelligence
T2 - 6th Hellenic Conference on Artificial Intelligence: Theories, Models and Applications, SETN 2010
Y2 - 4 May 2010 through 7 May 2010
ER -