Name
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. |
Year | DOI | Venue |
|---|---|---|
2011 | null | ACL |
Keywords | Field | DocType |
score function,information system | PageRank,Ranking,Information retrieval,Computer science,Artificial intelligence,Natural language processing,Probabilistic logic | Conference |
Volume | Issue | Citations |
1 | null | 44 |
PageRank | References | Authors |
1.86 | 13 | 7 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Wayne Xin Zhao | 1 | 1275 | 66.73 |
| Jing Jiang | 2 | 3843 | 191.63 |
| Jing He | 3 | 537 | 19.00 |
| Yang Song | 4 | 83 | 4.71 |
| Palakorn Achananuparp | 5 | 302 | 23.16 |
| Ee-Peng Lim | 6 | 5889 | 754.17 |
| Xiaoming Li | 7 | 1669 | 92.16 |