Please use this identifier to cite or link to this item:
Title: Open-set pattern recognition and its application in information extraction from text
Authors: Ke, Yizhen
Keywords: Engineering::Computer science and engineering::Computing methodologies::Artificial intelligence
Issue Date: 2022
Publisher: Nanyang Technological University
Source: Ke, Y. (2022). Open-set pattern recognition and its application in information extraction from text. Master's thesis, Nanyang Technological University, Singapore.
Project: ISM-DISS-03094
Abstract: In traditional supervised learning, the training set contains the same classes that appear in the testing set. However, the classifier may encounter previously unseen classes in the actual world, which is likely to create errors if a close-set classifier divides these data into the original category. The open-set classifier is designed to classify known samples accurately and reject unrelated samples. However, there are fewer applications in text classification. The goal of this paper is to achieve the application of open-set recognition on text classification tasks. This paper first reviews the work related to text classification and open-set classification identification. Subsequently, this paper determines the use of GloVe technique to map files to vector space. Considering that CNN and LSTM are superior in text classification, this article conducted a preliminary experiment and selected CNN with better performance as the base model. On this basis, SVDD and OpenMax methods are used in the 10 domains and 20 domains of the data set, respectively, and are compared with existing text classifiers. SVDD has similar training results to the currently open-set classifier based on SVM. The performance of OpenMax in the text classifier does not greatly vibrate by Openness and has good accuracy.
Schools: School of Electrical and Electronic Engineering 
Fulltext Permission: restricted
Fulltext Availability: With Fulltext
Appears in Collections:EEE Theses

Files in This Item:
File Description SizeFormat 
EEE_MSc_Dissertation_KE YIZHEN.pdf
  Restricted Access
1.05 MBAdobe PDFView/Open

Page view(s)

Updated on Apr 20, 2024


Updated on Apr 20, 2024

Google ScholarTM


Items in DR-NTU are protected by copyright, with all rights reserved, unless otherwise indicated.