Siswati Part of Speech Tagger: A Quantitative Evaluation.
This article evaluates the performance of the Siswati Text Annotation Tool part of speech (STAT POS) tagger using Recall, Precision and F1 score metrics. A quantitative research design was adopted for analysis, and data was collected through purposive sampling. Python 3 was utilised to calculate the...
| Published in: | Journal of the Digital Humanities Association of Southern Africa (DHASA) Vol. 6; no. 2; pp. 1 - 8 |
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| Format: | Article |
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Digital Humanities Association of Southern Africa (DHASA)
2025
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| Online Access: | View this record in EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=hlh&AN=191564082&site=ehost-live header: @attributes: shortDbName: hlh uiTerm: 191564082 longDbName: Humanities International Complete uiTag: AN controlInfo: bkinfo: jinfo: jid: N79T jtl: Journal of the Digital Humanities Association of Southern Africa (DHASA) maglogo: N pubinfo: dt: 2025 vid: 6 iid: 2 pid: 74161 pub: Digital Humanities Association of Southern Africa (DHASA) artinfo: ui: 191564082 ppf: 1 ppct: 7 formats: tig: atl: Siswati Part of Speech Tagger: A Quantitative Evaluation. aug: au: Matfunjwa, Muzi affil: South African Centre for Digital Language Resources, North-West University. su: Natural language processing Quantitative research Python programming language Language & languages sug: subj: Natural language processing Quantitative research Python programming language Language & languages keyword: F1 score Part of speech tagger Precision Recall Siswati ab: This article evaluates the performance of the Siswati Text Annotation Tool part of speech (STAT POS) tagger using Recall, Precision and F1 score metrics. A quantitative research design was adopted for analysis, and data was collected through purposive sampling. Python 3 was utilised to calculate the Recall and Precision of the STAT POS tagger outputs. The results show that the Recall for nouns was 0.761, Precision 0.417, with an F1 score of 0.54; for verbs, the Recall was 0.756, Precision 0.798 and F1 score 0.54; for adverbs, the Recall was 0.571, Precision 0.8, and F1 score 0.67; for possessives, the Recall was 0.963, Precision 0.813 and F1 score 0.88. For relatives (REL), the Recall was 0.706, Precision 0.523, and the F1 score 0.60; for class-indicating demonstratives, the Recall was 0.333, Precision 0.25, and the F1 score 0.29; and for copulatives (COP), the Recall was 0.75, Precision 0.75, and the F1 score 0.75. For conjunctions, the Recall was 0.85, the Precision was 0.68, and the F1 score was 0.76; for pronouns, the Recall was 0.563, the Precision was 1.0, and the F1 score was 0.72; for adjectives, the Recall was 0.75, the Precision was 0.75, and the F1 score was 0.75. However, question words, interjections and ideophones received 0.0, highlighting the need for refinement of the STAT POS tagger. pubtype: Academic Journal doctype: Article src: R language: English refInfo: copyright: @attributes: flag: Y dt: @attributes: year: 2025 holdings: @attributes: islocal: N |
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