Name
Title | ||
|---|---|---|
Extracting Events From Web Documents For Social Media Monitoring Using Structured Svm |
Abstract | ||
|---|---|---|
Event extraction is vital to social media monitoring and social event prediction. In this paper, we propose a method for social event extraction from web documents by identifying binary relations between named entities. There have been many studies on relation extraction, but their aims were mostly academic. For practical application, we try to identify 130 relation types that comprise 31 predefined event types, which address business and public issues. We use structured Support Vector Machine, the state of the art classifier to capture relations. We apply our method on news, blogs and tweets collected from the Internet and discuss the results. |
Year | DOI | Venue |
|---|---|---|
2013 | 10.1587/transinf.E96.D.1410 | IEICE TRANSACTIONS ON INFORMATION AND SYSTEMS |
Keywords | Field | DocType |
relation extraction, structured SVM, natural language processing, information extraction | Structured support vector machine,Social media,Pattern recognition,Information retrieval,Computer science,Binary relation,Information extraction,Artificial intelligence,Classifier (linguistics),The Internet,Relationship extraction | Journal |
Volume | Issue | ISSN |
E96D | 6 | 0916-8532 |
Citations | PageRank | References |
0 | 0.34 | 14 |
Authors | ||
4 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Yoonjae Choi | 1 | 364 | 18.68 |
| Pum-Mo Ryu | 2 | 43 | 5.85 |
| Hyun-Ki Kim | 3 | 61 | 21.35 |
| Changki Lee | 4 | 279 | 26.18 |