VNU-UET Repository

Improving Semantic Relation Extraction System with Compositional Dependency Unit on Diverse Shortest Dependency Path

Can, Duy Cat and Le, Hoang Quynh and Ha, Quang Thuy (2019) Improving Semantic Relation Extraction System with Compositional Dependency Unit on Diverse Shortest Dependency Path. In: 11th Asian Conference on Intelligent Information and Database Systems (ACIIDS 2019), 8-11 April 2019, Yogyakarta, Indonesia. (In Press)

WarningThere is a more recent version of this item available.
[img] PDF - Accepted Version
Restricted to Registered users only

Download (1MB) | Request a copy

Abstract

Experimental performance on the task of relation extraction/classification has generally improved using deep neural network architectures. In which, data representation has been proven to be one of the most influential factors to the model's performance but still has many limitations. In this work, we take advantage of compressed information in the shortest dependency path (SDP) between two corresponding entities to classify the relation between them. We propose (i) a compositional embedding that combines several dominant linguistic as well as architectural features and (ii) dependency tree normalization techniques for generating rich representations for both words and dependency relations in the SDP. We also present a Convolutional Neural Network (CNN) model to process the proposed SDP diverse representation. Experimental results for both general and biomedical data demonstrate the effectiveness of compositional embedding, dependency tree normalization technique as well as the suitability of the CNN model.

Item Type: Conference or Workshop Item (Paper)
Subjects: Information Technology (IT)
Divisions: Faculty of Information Technology (FIT)
Depositing User: Duy-Cat Can
Date Deposited: 17 Dec 2018 06:54
Last Modified: 18 Dec 2018 07:00
URI: http://eprints.uet.vnu.edu.vn/eprints/id/eprint/3321

Available Versions of this Item

Actions (login required)

View Item View Item