Improving Semantic Relation Extraction System with Compositional Dependency Unit on Enriched 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 Enriched Shortest Dependency Path. In: 2019 11th Asian Conference on Intelligent Information and Database Systems (ACIIDS), 8-11 April 2019, Yogyakarta, Indonesia.

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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 enriched 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:	03 Jun 2019 04:16
Last Modified:	03 Jun 2019 04:16
URI:	http://eprints.uet.vnu.edu.vn/eprints/id/eprint/3441

Available Versions of this Item

• Improving Semantic Relation Extraction System with Compositional Dependency Unit on Diverse Shortest Dependency Path. (deposited 17 Dec 2018 06:54)
  • Improving Semantic Relation Extraction System with Compositional Dependency Unit on Enriched Shortest Dependency Path. (deposited 03 Jun 2019 04:16) [Currently Displayed]

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