Unsupervised brand name extraction using domain adaptation
dc.contributor.advisor | Makrehchi, Masoud | |
dc.contributor.advisor | Rahnamayan, Shahryar | |
dc.contributor.author | Towhidi, Afsaneh | |
dc.date.accessioned | 2019-10-17T16:25:07Z | |
dc.date.accessioned | 2022-03-29T16:49:39Z | |
dc.date.available | 2019-10-17T16:25:07Z | |
dc.date.available | 2022-03-29T16:49:39Z | |
dc.date.issued | 2019-08-01 | |
dc.degree.discipline | Electrical and Computer Engineering | |
dc.degree.level | Master of Applied Science (MASc) | |
dc.description.abstract | Business intelligence and analytics is an area of research that analyzes the existing business data to extract the insights needed for a successful business planning. Textual data derived from tweets, forum posts, and blogs are from different business domains, and contain useful information for the organizations. This thesis proposes a method for extracting brand and product names from text; brand names as a subset of named entities can give a great deal of information about the whole document. In this thesis, a context window is defined to capture the context of a word in a sentence. In addition, a word embedding model is locally trained to have a domain specific model and finally, a domain adaptation technique is employed to transfer the knowledge from one domain with labeled data to a new domain. The results indicate a significant improvement in recall measure for extracting brand names from a new domain. | en |
dc.description.sponsorship | University of Ontario Institute of Technology | en |
dc.identifier.uri | https://hdl.handle.net/10155/1079 | |
dc.language.iso | en | en |
dc.subject | Natural language processing | en |
dc.subject | Named entity recognition | en |
dc.subject | Word embedding | en |
dc.subject | Domain adaptation | en |
dc.title | Unsupervised brand name extraction using domain adaptation | en |
dc.type | Thesis | en |
thesis.degree.discipline | Electrical and Computer Engineering | |
thesis.degree.grantor | University of Ontario Institute of Technology | |
thesis.degree.name | Master of Applied Science (MASc) |