Chinese Event Extraction Based on Attention and Semantic Features: A Bidirectional Circular Neural Network
School of Computer Engineering and Science, Shanghai University, Shanghai 200444, China
*
Author to whom correspondence should be addressed.
Future Internet 2018, 10(10), 95; https://doi.org/10.3390/fi10100095
Received: 6 September 2018 / Revised: 20 September 2018 / Accepted: 24 September 2018 / Published: 26 September 2018
(This article belongs to the Section Big Data and Augmented Intelligence)
Chinese event extraction uses word embedding to capture similarity, but suffers when handling previously unseen or rare words. From the test, we know that characters may provide some information that we cannot obtain in words, so we propose a novel architecture for combining word representations: character–word embedding based on attention and semantic features. By using an attention mechanism, our method is able to dynamically decide how much information to use from word or character level embedding. With the semantic feature, we can obtain some more information about a word from the sentence. We evaluate different methods on the CEC Corpus, and this method is found to improve performance.
View Full-Text
Keywords:
event extraction; attention mechanism; feature representation; Bi-LSTM
▼
Show Figures
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited
MDPI and ACS Style
Wu, Y.; Zhang, J. Chinese Event Extraction Based on Attention and Semantic Features: A Bidirectional Circular Neural Network. Future Internet 2018, 10, 95. https://doi.org/10.3390/fi10100095
AMA Style
Wu Y, Zhang J. Chinese Event Extraction Based on Attention and Semantic Features: A Bidirectional Circular Neural Network. Future Internet. 2018; 10(10):95. https://doi.org/10.3390/fi10100095
Chicago/Turabian StyleWu, Yue; Zhang, Junyi. 2018. "Chinese Event Extraction Based on Attention and Semantic Features: A Bidirectional Circular Neural Network" Future Internet 10, no. 10: 95. https://doi.org/10.3390/fi10100095
Find Other Styles
Note that from the first issue of 2016, MDPI journals use article numbers instead of page numbers. See further details here.
Search more from Scilit