Please use this identifier to cite or link to this item: https://hdl.handle.net/10356/156613
Title: Machine learning for NTU canteen review analysis and recommendation
Authors: Nguyen, Duy Khanh
Keywords: Engineering::Computer science and engineering
Issue Date: 2022
Publisher: Nanyang Technological University
Source: Nguyen, D. K. (2022). Machine learning for NTU canteen review analysis and recommendation. Final Year Project (FYP), Nanyang Technological University, Singapore. https://hdl.handle.net/10356/156613
Project: SCSE21-0345
Abstract: NER, also known as entity identification, chunking, and extraction, is a sub-task of information extraction that aims to discover and categorize named entities referenced in unstructured text into preset categories such as human names, organizations, and locations. Food Named Entity Recognition is the downstream task of NER, which locates, extracts, and classifies food name entities from a sequence of words. Nowadays, there are many methods to extract food name entities from a sentence, such as Terminologydriven, Ruled-based, Corpus-based, Deep Neural Networks based. The Corpus-based method is currently utilized in the FoodHunter project but there are many limitations in this method as it cannot cover all food name entities in Singapore or may be in the world for future improvements. Therefore, this report experiments with Deep Neural Networks, utilizing 2 pretrained models namely T5 and Bart to handle the Food Named Entity Recognition task.
URI: https://hdl.handle.net/10356/156613
Schools: School of Computer Science and Engineering 
Fulltext Permission: restricted
Fulltext Availability: With Fulltext
Appears in Collections:SCSE Student Reports (FYP/IA/PA/PI)

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