A Survey on Personalized PageRank Computation Algorithms <s> C. COMPUTATIONAL LINGUISTICS <s> In this paper we propose a new graph-based method that uses the knowledge in a LKB (based on WordNet) in order to perform unsupervised Word Sense Disambiguation. Our algorithm uses the full graph of the LKB efficiently, performing better than previous approaches in English all-words datasets. We also show that the algorithm can be easily ported to other languages with good results, with the only requirement of having a wordnet. In addition, we make an analysis of the performance of the algorithm, showing that it is efficient and that it could be tuned to be faster. <s> BIB001 </s> A Survey on Personalized PageRank Computation Algorithms <s> C. COMPUTATIONAL LINGUISTICS <s> 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. <s> BIB002
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.