Quantitative analysis of Sesotho sa Leboa part-of-speech taggers
Bibliographic Data
| ID | 21490924 |
|---|---|
| Authors | Dimakatso S Mathe (0000-0001-9369-2672, North-West University, corresponding author), Roald Eiselen (0000-0002-8612-5175, North-West University) |
| Year | 2021 |
| Volume | 41 |
| Issue | 3 |
| Pages | 259-269 |
| Publication date | 2021-09-02 |
| Open Access | No |
| Type | ARTICLE |
| Venue | South African Journal of African Languages (JOURNAL) |
| Journal identifiers | ISSN: 0257-2117 • E-ISSN: 2305-1159 |
| Publisher | Informa UK Limited (PUBLISHER • GB) |
| DOI | 10.1080/02572117.2021.2010921 |
| OpenAlex | W4200437425 |
| Language | EN |
| References cited | 20 |
This study reported in this article conducts a quantitative analysis of the Sesotho sa Leboa National Centre for Human Language Technology (NCHLT) part-of-speech annotated data set, and compares the quality of the NCHLT and CTexT part-of-speech taggers based on the data set. The two taggers were developed as part of the NCHLT Text project and are both based on Taljard et al.’s fine-grained tagset, aligned to the morphological structure of Sesotho sa Leboa. A gold standard data set of 7 153 tokens is utilised for comparison and evaluation of the overall performance of the two part-of-speech taggers and to perform fine-grained error analysis. We find that the NCHLT and CTexT taggers obtain 88.40% and 94.18% accuracy respectively and describe the linguistic nature of the most frequent errors observed in the two taggers
Natural language processing · Part of speech · Part-of-speech tagging · Speech recognition · Computer Science · Lexicography and Language Studies · Natural Language Processing Techniques · Topic Modeling · Artificial Intelligence
| Highly cited | No |
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