| dc.contributor.author | Muhanuzi, Stewart | |
| dc.date.accessioned | 2024-06-07T13:18:37Z | |
| dc.date.available | 2024-06-07T13:18:37Z | |
| dc.date.issued | 2020-12-15 | |
| dc.identifier.citation | Muhanuzi, Stewart. (2020). Natural Language Processing: A Luganda Part of Speech Tagger. (Unpublished undergraduate dissertation). Makerere University; Kampala, Uganda. | en_US |
| dc.identifier.uri | http://hdl.handle.net/20.500.12281/18673 | |
| dc.description | A research report submitted in partial fulfillment of the requirements for the award of Degree of
Bachelor of Science in Computer Engineering of the College of Engineering, Design, Art and Technology of Makerere University. | en_US |
| dc.description.abstract | This research study describes the initial experiment in designing a Hidden Markov Model (HMM)-based part-of-speech tagger for the Luganda language. Part-of-speech tagging involves assigning the proper tag to each word in a text based on its context. The process was accomplished in two primary steps: morphological analysis and disambiguation. This study focuses on tagging accuracy, specifically the challenge of correctly tagging each token and handling new tokens. We constructed a first-order stochastic disambiguation algorithm, using supervised learning techniques, to learn HMM parameters from hand-crafted corpora. The Viterbi algorithm was employed to determine the most probable tag for each word. | en_US |
| dc.language.iso | en | en_US |
| dc.publisher | Makerere Univeristy | en_US |
| dc.subject | Luganda Language | en_US |
| dc.subject | Natural Language Processing (NLP) | en_US |
| dc.subject | Hidden Markov Model (HMM) | en_US |
| dc.subject | Corpus Design | en_US |
| dc.subject | Morphological Analysis | en_US |
| dc.subject | Stochastic Disambiguation Algorithm | en_US |
| dc.subject | Viterbi Algorithm | en_US |
| dc.title | Natural Language Processing: A Luganda Part of Speech Tagger | en_US |
| dc.type | Thesis | en_US |
