%0 Journal Article
%A Le, Hoang Quynh
%A Can, Duy Cat
%A Ha, Quang Thuy
%A Collier, Nigel
%D 2019
%F SisLab:3477
%I Association for Computational Linguistics
%J 2019 Annual Conference of the North American Chapter of the Association for Computational Linguistics
%P 2902-2912
%T A Richer-but-Smarter Shortest Dependency Path  with Attentive Augmentation for Relation Extraction
%U https://eprints.uet.vnu.edu.vn/eprints/id/eprint/3477/
%V 1
%X 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.