Login | Register

Dependency Encoding for Relation Extraction


Dependency Encoding for Relation Extraction

Lathiff, Fathima Nihatha Abdul (2022) Dependency Encoding for Relation Extraction. Masters thesis, Concordia University.

[thumbnail of Lathiff_MCompSc_F2022.pdf]
Text (application/pdf)
Lathiff_MCompSc_F2022.pdf - Accepted Version


The surge in information in the form of textual data demands automated systems to extract structured information from unstructured data. Relation extraction plays a key role in the process, with the aim of extracting semantic relations between entities in a text. Since dependency parse trees are capable of capturing the grammatical structure of sentences, this thesis experiments with different encodings of the dependency parse tree to distinguish different semantic relationships. Experiments are conducted on three different data sets that vary in domain and complexity and experimented with varying encoding schemas that can be grouped into two. The first group focuses on encoding the structure of the dependency parse tree with a Deep Graph Convolution Neural Network (DGCNN). The second group focuses on encoding the linguistic features obtained from the dependency parse tree with classical machine learning models such as Random Forest, Support Vector Machine, and Feed-Forward Network, and deep models such as BERT and Transformer encoder stack. The objective of this thesis is not to achieve state-of-the-art (SOTA) performance, rather to evaluate how dependency parse tree based linguistic features perform on different encoding schemas, including deep transformer-based models, on the relation extraction task. The results of the experiments show that these features on certain data sets being less computationally demanding are competitive for complex language models such as BERT, and incorporating them externally to BERT improves the performance rather than confounding.

Divisions:Concordia University > Gina Cody School of Engineering and Computer Science > Computer Science and Software Engineering
Item Type:Thesis (Masters)
Authors:Lathiff, Fathima Nihatha Abdul
Institution:Concordia University
Degree Name:M. Comp. Sc.
Program:Computer Science
Date:June 2022
Thesis Supervisor(s):Bergler, Sabine
ID Code:990694
Deposited By: Fathima Lathiff
Deposited On:27 Oct 2022 14:13
Last Modified:27 Oct 2022 14:13
All items in Spectrum are protected by copyright, with all rights reserved. The use of items is governed by Spectrum's terms of access.

Repository Staff Only: item control page

Downloads per month over past year

Research related to the current document (at the CORE website)
- Research related to the current document (at the CORE website)
Back to top Back to top