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...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 human-generated dataset containing real world events.Experimental results show that our new model MLEM outperforms the baseline method both in efficiency and effectiveness.展开更多
基金This work was supported by NSFC program(Grant Nos.61872022,61421003,U1636123)SKLSDE-2018ZX-16 and partly by the Beijing Advanced Innovation Center for Big Data and Brain Computing.
文摘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 human-generated dataset containing real world events.Experimental results show that our new model MLEM outperforms the baseline method both in efficiency and effectiveness.
