@inproceedings{4471be70c48f4036b135e2c55c2603e5,
title = "Event extraction with deep contextualized word representation and multi-attention layer",
abstract = "One common application of text mining is event extraction. The purpose of an event extraction task is to identify event triggers of a certain event type in the text and to find related arguments. In recent years, the technology to automatically extract events from text has drawn researchers{\textquoteright} attention. However, the existing works including feature based systems and neural network base models don{\textquoteright}t capture the contextual information well. Besides, it is still difficult to extract deep semantic relations when finding related arguments for events. To address these issues, we propose a novel model for event extraction using multi-attention layers and deep contextualized word representation. Furthermore, we put forward an attention function suitable for event extraction tasks. Experimental results show that our model outperforms the state-of-the-art models on ACE2005.",
keywords = "Deep contextualized word representation, Event extraction, Muti-attention layer",
author = "Ruixue Ding and Zhoujun Li",
note = "Publisher Copyright: {\textcopyright} 2018, Springer Nature Switzerland AG.; 14th International Conference on Advanced Data Mining and Applications, ADMA 2018 ; Conference date: 16-11-2018 Through 18-11-2018",
year = "2018",
doi = "10.1007/978-3-030-05090-0\_17",
language = "英语",
isbn = "9783030050894",
series = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",
publisher = "Springer Verlag",
pages = "189--201",
editor = "Guojun Gan and Xue Li and Shuliang Wang and Bohan Li",
booktitle = "Advanced Data Mining and Applications - 14th International Conference, ADMA 2018, Proceedings",
address = "德国",
}