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Latent semantic models : a study of probabilistic models for text in information retrieval
Mini Dissertation (MSc)--University of Pretoria, 2020.
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| Format: | Thesis |
| Language: | English |
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University of Pretoria
2020
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| _version_ | 1867613529957203968 |
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| access_status_str | Open Access |
| author2 | De Waal, Alta |
| author_browse | De Waal, Alta |
| author_facet | De Waal, Alta |
| collection | Thesis |
| dc_rights_str_mv | © 2019 University of Pretoria. All rights reserved. The copyright in this work vests in the University of Pretoria. No part of this work may be reproduced or transmitted in any form or by any means, without the prior written permission of the University of Pretoria. |
| description | Mini Dissertation (MSc)--University of Pretoria, 2020. |
| format | Thesis |
| id | oai:repository.up.ac.za:2263/73881 |
| institution | University of Pretoria (South Africa) |
| language | English |
| last_indexed | 2026-06-10T12:37:36.202Z |
| license_str | Other — see source repository |
| provenance_str_mv | Harvested via OAI-PMH from UPSpace — University of Pretoria Institutional Repository |
| publishDate | 2020 |
| publishDateRange | 2020 |
| publishDateSort | 2020 |
| publisher | University of Pretoria |
| publisherStr | University of Pretoria |
| record_format | dspace |
| source_str | UPSpace — University of Pretoria Institutional Repository |
| spelling | oai:repository.up.ac.za:2263/73881 Latent semantic models : a study of probabilistic models for text in information retrieval De Waal, Alta siyabongamjali@gmail.com Mjali, Siyabonga Zimozoxolo UCTD Mini Dissertation (MSc)--University of Pretoria, 2020. Large volumes of text is being generated every minute which necessitates effective and robust tools to retrieve relevant information. Supervised learning approaches have been explored extensively for this task, but it is difficult to secure large collections of labelled data to train this set of models. Since a supervised approach is too expensive in terms of annotating data, we consider unsupervised methods such as topic models and word embeddings in order to represent corpora in lower dimensional semantic spaces. Furthermore, we investigate different distance measures to capture similarity between indexed documents based on their semantic distributions. These include cosine, soft cosine and Jensen-Shannon similarities. This collection of methods discussed in this work allows for the unsupervised association of semantic similar texts which has a wide range of applications such as fake news detection, sociolinguistics and sentiment analysis. The Hub Internship Centre for Artificial Intelligence Research Statistics MSc (Mathematical Statistics) Unrestricted 2020-03-31T07:21:02Z 2020-03-31T07:21:02Z 2020-09 2020 Mini Dissertation Mjali, SZ 2020, Latent semantic models: A study of probabilistic models for text in information retrieval, Masters mini dissertation, University of Pretoria, Pretoria S2020 http://hdl.handle.net/2263/73881 en © 2019 University of Pretoria. All rights reserved. The copyright in this work vests in the University of Pretoria. No part of this work may be reproduced or transmitted in any form or by any means, without the prior written permission of the University of Pretoria. application/pdf University of Pretoria |
| spellingShingle | UCTD Latent semantic models : a study of probabilistic models for text in information retrieval |
| title | Latent semantic models : a study of probabilistic models for text in information retrieval |
| title_full | Latent semantic models : a study of probabilistic models for text in information retrieval |
| title_fullStr | Latent semantic models : a study of probabilistic models for text in information retrieval |
| title_full_unstemmed | Latent semantic models : a study of probabilistic models for text in information retrieval |
| title_short | Latent semantic models : a study of probabilistic models for text in information retrieval |
| title_sort | latent semantic models a study of probabilistic models for text in information retrieval |
| topic | UCTD |
| url | http://hdl.handle.net/2263/73881 |