Chinese Event Extraction Based on Attention and Semantic Features: A Bidirectional Circular Neural Network

Yue Wu and Junyi Zhang
Additional contact information
Yue Wu: School of Computer Engineering and Science, Shanghai University, Shanghai 200444, China
Junyi Zhang: School of Computer Engineering and Science, Shanghai University, Shanghai 200444, China

Future Internet, 2018, vol. 10, issue 10, 1-10

Abstract: 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.

Keywords: event extraction; attention mechanism; feature representation; Bi-LSTM (search for similar items in EconPapers)
JEL-codes: O3 (search for similar items in EconPapers)
Date: 2018
References: View complete reference list from CitEc
Citations: View citations in EconPapers (1)

Downloads: (external link)
https://www.mdpi.com/1999-5903/10/10/95/pdf (application/pdf)
https://www.mdpi.com/1999-5903/10/10/95/ (text/html)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:gam:jftint:v:10:y:2018:i:10:p:95-:d:172223

Access Statistics for this article

Future Internet is currently edited by Ms. Grace You

More articles in Future Internet from MDPI
Bibliographic data for series maintained by MDPI Indexing Manager ().