TY  - JOUR
ID  - SisLab3477
UR  - https://www.aclweb.org/anthology/N19-1298
A1  - Le, Hoang Quynh
A1  - Can, Duy Cat
A1  - Ha, Quang Thuy
A1  - Collier, Nigel
Y1  - 2019/06/02/
N2  - To extract the relationship between two entities in a sentence, two common approaches are (1) using their shortest dependency path (SDP) and (2) using an attention model to capture a context-based representation of the sentence. Each approach suffers from its own disadvantage of either missing or redundant information. In this work, we propose a novel model that combines the advantages of these two approaches. This is based on the basic information in the SDP enhanced with information selected by several attention mechanisms with kernel filters, namely RbSP (Richer-but- Smarter SDP). To exploit the representation behind the RbSP structure effectively, we develop a combined deep neural model with a LSTM network on word sequences and a CNN on RbSP. Experimental results on the SemEval-2010 dataset demonstrate improved performance over competitive baselines. The data and source code are available at https: //github.com/catcd/RbSP.
PB  - Association for Computational Linguistics
JF  - 2019 Annual Conference of the North American Chapter of the Association for Computational Linguistics
VL  - 1
TI  - A Richer-but-Smarter Shortest Dependency Path
with Attentive Augmentation for Relation Extraction
SP  - 2902
AV  - public
EP  - 2912
ER  -