Event detection and evolution in multi-lingual social streams |
| |
Authors: | Yaopeng LIU Hao PENG Jianxin LI Yangqiu SONG Xiong LI |
| |
Affiliation: | 1. Beijing Advanced Innovation Center for Big Data and Brain Computing, Beihang University, Beijing 100083, China2. State Key Laboratory of Software Development Environment, Beihang University, Beijing 100083, China3. Department of Computer Science and Engineering, HKUST, Hong Kong 99907, China4. National Computer Network Emergency Response Technical Team/Coordination Center of China, Beijing 100029, China |
| |
Abstract: | Real-life events are emerging and evolving in social and news streams. Recent methods have succeeded in capturing designed features of monolingual events, but lack of interpretability and multi-lingual considerations. To this end, we propose a multi-lingual event mining model, namely MLEM, to automatically detect events and generate evolution graph in multilingual hybrid-length text streams including English, Chinese, French, German, Russian and Japanese. Specially, we merge the same entities and similar phrases and present multiple similarity measures by incremental word2vec model. We propose an 8-tuple to describe event for correlation analysis and evolution graph generation. We evaluate the MLEM model using a massive humangenerated dataset containing real world events. Experimental results show that our new model MLEM outperforms the baseline method both in efficiency and effectiveness. |
| |
Keywords: | event detection event evolution stream processing multi-lingual anomaly detection |
|
| 点击此处可从《Frontiers of Computer Science》浏览原始摘要信息 |
|
点击此处可从《Frontiers of Computer Science》下载全文 |
|