Korean Coreference Resolution using the Multi-pass Sieve
Cheon-Eum Park, Kyoung-Ho Choi, Changki Lee · Journal of KIISE · 2014
Coreference resolution finds all expressions that refer to the same entity in a document. Coreference resolution is important for information extraction, document classification, document summary, and question answering system. In this paper, we adapt Stanford's Multi-pass sieve system, the one of the best model of rule based coreference resolution to Korean. In this paper, all noun phrases are considered to mentions. Also, unlike Stanford's Multi-pass sieve system, the dependency parse tree is used for mention extraction, a Korean acronym list is built 'dynamically'. In addition, we propose a method that calculates weights by applying transitive properties of centers of the centering theory when refer Korean pronoun. The experiments show that our system obtains MUC 59.0%, 59.5%, Ceafe 63.5%, and CoNLL(Mean) 60.7%.