Event detection and evolution in multi-lingual social streams
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 automa...
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| Published in | Frontiers of Computer Science Vol. 14; no. 5; p. 145612 |
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| Main Authors | , , , , |
| Format | Journal Article |
| Language | English |
| Published |
Beijing
Higher Education Press
01.10.2020
Springer Nature B.V |
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| 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. |
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| AbstractList | 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. 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. |
| ArticleNumber | 145612 |
| Author | LIU, Yaopeng PENG, Hao SONG, Yangqiu LI, Jianxin LI, Xiong |
| Author_xml | – sequence: 1 givenname: Yaopeng surname: LIU fullname: LIU, Yaopeng organization: State Key Laboratory of Software Development Environment, Beihang University, Beijing 100083, China – sequence: 2 givenname: Hao surname: PENG fullname: PENG, Hao organization: State Key Laboratory of Software Development Environment, Beihang University, Beijing 100083, China – sequence: 3 givenname: Jianxin surname: LI fullname: LI, Jianxin email: lijx@act.buaa.edu.cn organization: State Key Laboratory of Software Development Environment, Beihang University, Beijing 100083, China – sequence: 4 givenname: Yangqiu surname: SONG fullname: SONG, Yangqiu organization: Department of Computer Science and Engineering, HKUST, Hong Kong 99907, China – sequence: 5 givenname: Xiong surname: LI fullname: LI, Xiong organization: National Computer Network Emergency Response Technical Team/Coordination Center of China, Beijing 100029, China |
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| Copyright | Copyright reserved, 2019, Higher Education Press and Springer-Verlag GmbH Germany, part of Springer Nature Higher Education Press and Springer-Verlag GmbH Germany, part of Springer Nature 2020 Higher Education Press and Springer-Verlag GmbH Germany, part of Springer Nature 2020. |
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| Keywords | multi-lingual anomaly detection stream processing event detection event evolution |
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| SubjectTerms | Computer Science Correlation analysis Earthquakes event detection event evolution Evolution Keywords multi-lingual anomaly detection Multilingualism Research Article Semantics stream processing |
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| Title | Event detection and evolution in multi-lingual social streams |
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