The task of Named Entity Disambiguation is to map entity mentions in the document to their correct entries in some knowledge base. We present a novel graph-based disambiguation approach based on Personalized PageRank (PPR) that combines local and global evidence for disambiguation and effectively filters out noise introduced by incorrect candidates. Experiments show that our method outperforms state-of-the-art approaches by achieving 91.7% in micro-and 89.9% in macroaccuracy on a dataset of 27.8K named entity mentions.
Pershina et al. presented a graphbased method based on the Personalized PageRank (PPR) algorithm to this task, incorporating both local and global coherence REF .
Personalized Page Rank for Named Entity Disambiguation
HLT-NAACL
Computer Science