Parallel Community Detection For Cross-Document Coreference
Number of Authors: 3
2014 (English)Report (Other academic)
This document presents a highly parallel solution for cross-document coreference resolution, which can deal with billions of documents that exist in the current web. At the core of our solution lies a novel algorithm for community detection in large scale graphs. We operate on graphs which we construct by representing documents' keywords as nodes and the co-location of those keywords in a document as edges. We then exploit the particular nature of such graphs where coreferent words are topologically clustered and can be efficiently discovered by our community detection algorithm. The accuracy of our technique is considerably higher than that of the state of the art, while the convergence time is by far shorter. In particular, we increase the accuracy for a baseline dataset by more than 15\% compared to the best reported result so far. Moreover, we outperform the best reported result for a dataset provided for the Word Sense Induction task in SemEval 2010.
Place, publisher, year, edition, pages
Kista, Sweden: Swedish Institute of Computer Science , 2014, 6.
SICS Technical Report, ISSN 1100-3154 ; 2014:01
Computer and Information Science
IdentifiersURN: urn:nbn:se:ri:diva-24302OAI: oai:DiVA.org:ri-24302DiVA: diva2:1043382