JVN-TDT Entity Linking Systems at TAC-KBP2012
نویسندگان
چکیده
We present two methods for entity linking in two of our systems submitted to TAC-KBP 2012. The first one, implemented in JVNTDT1 system, learns coherence among cooccurrence entities referred to within a text by exploiting Wikipedia’s link structure and the second one, implemented in JVN_TDT2 system, combines some heuristics with a statistical model, for entity linking. The method implemented in JVN-TDT1 exploits two features to train a classifier and exploits coreference relations among co-occurring mentions for entity linking. The method implemented in JVNTDT2 is a hybrid method that performs entity linking in two phases. The first phase is a rulebased phase that filters candidates and, if possible, it disambiguates mentions with high reliability. The second phase employs a statistical model to rank the candidates of each remaining mention and choose the one with the highest ranking as the right referent of that mention. Experiments are conducted to evaluate two methods on two datasets – TAC-KBP2011 and TAC-KBP2012 datasets.
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