Name
Abstract | ||
|---|---|---|
Explicit Semantic Analysis(ESA) is an effective method that adopts Wikipedia articles to represent text and compute semantic relatedness(SR). Most related studies do not take advantage of the semantics carried by Wikipedia categories. We develop a SR computing framework exploiting Wikipedia category structure to generate abstract features for texts and considering the lexical overlap between a pair of text. Experiments on three datasets show that our framework could gain better performance against ESA and most other methods. It indicates that Wikipedia category graph is a promising resource to aid natural language text analysis. |
Year | Venue | Keywords |
|---|---|---|
2014 | Lecture Notes in Computer Science | Semantic Relatedness,Explicit Semantic Analysis,Wikipedia,Hierarchy |
Field | DocType | Volume |
Semantic similarity,Text mining,Effective method,Computer science,Explicit semantic analysis,Natural language,Natural language processing,Artificial intelligence,Hierarchy,Semantic computing,Semantics | Conference | 8835 |
ISSN | ISBN | Citations |
0302-9743 | 978-3-319-12640-1 | 1 |
PageRank | References | Authors |
0.36 | 0 | 4 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Zheng Hai-Tao | 1 | 142 | 24.39 |
| Wu Wenzhen | 2 | 1 | 0.70 |
| Jiang Yong | 3 | 156 | 41.60 |
| Xia Shu-Tao | 4 | 342 | 75.29 |