You are presented with multiple paper abstracts, alongside the associated literature review and chapter titles. Given the provided information, you are going to do a literature review. Please generate a coherent literature review that synthesizes the content, draws connections between different works, highlights major findings, and identifies any gaps in the existing research.

Literature Review Title: A Survey on Personalized PageRank Computation Algorithms 
Chapter Title:  C. COMPUTATIONAL LINGUISTICS 
1. Abstract of Cited Paper (BIB001):  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 microand 89.9% in macroaccuracy on a dataset of 27.8K named entity mentions.

Literature Review:
PPR has made some achievements in the field of Computational linguistics. Liu et al.  applied PPR to generate query-based multidocument summarizations. They used PPR to rank the personalized prior probability of each sentence, which is computed using their salience model and relevance model. Agirre and Soroa BIB001 used PPR to solve the graphbased word sense disambiguation (WSD) problem. In WSD, a graph consists of nodes, which represent word senses, and edges, which represent relations between pairs of word senses. PPR is utilized when performing disambiguation by applying a ranking algorithm to a graph. Pershina et al. BIB002 introduced PPR-based random walk method to solve Named Entity Disambiguation (NED) problem. They used PPR algorithm on a graph where the vertices represent candidate links and the edges represent links in Wikipedia. They achieved state-of-the-art performance on a 27.8K named entity mention dataset.