acl acl2011 acl2011-305 acl2011-305-reference knowledge-graph by maker-knowledge-mining
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Author: Xin Zhao ; Jing Jiang ; Jing He ; Yang Song ; Palakorn Achanauparp ; Ee-Peng Lim ; Xiaoming Li
Abstract: Summarizing and analyzing Twitter content is an important and challenging task. In this paper, we propose to extract topical keyphrases as one way to summarize Twitter. We propose a context-sensitive topical PageRank method for keyword ranking and a probabilistic scoring function that considers both relevance and interestingness of keyphrases for keyphrase ranking. We evaluate our proposed methods on a large Twitter data set. Experiments show that these methods are very effective for topical keyphrase extraction.
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