Please use this identifier to cite or link to this item: https://hdl.handle.net/10356/78682
Title: Relation identification for reasoning
Authors: Wang, Xuehan
Keywords: Engineering::Electrical and electronic engineering
Issue Date: 2019
Abstract: This thesis aims to implement relation identification between entities in one sentence, which is a basic project for further applications in nature language processing. Two pre-labelled corpuses including Sem-Eval 2010 task 8 and Tacred Relation Extraction Dataset are utilised here, and sentence modelling is done by extracting sentence-level features incorporating word embedding and position embedding. The classification task is supported by deep learning algorithms including Convolutional Neural Network and Recurrent Neural Network with Long ShortTerm Memory cell. The Effectiveness of CNN, LSTM and two combinations of these two models were investigated aiming to achieve better performance in relation identification. The whole project is implemented on python and the results were shown that the combination of LSTM-CNN model delivered highest accuracy rate and F1 score among all four models.
URI: http://hdl.handle.net/10356/78682
Schools: School of Electrical and Electronic Engineering 
Fulltext Permission: restricted
Fulltext Availability: With Fulltext
Appears in Collections:EEE Theses

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