SADiLaR Language Resource Repository
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Autshumato Monolingual Afrikaans Corpus
No full textMonolingual corpus for Afrikaans. The data is given as a single UTF-8 text file, with each segment on a newline. The data was specifically selected and formatted for use in the training of machine translation systems. Further clean-up and processing might be required depending on the task the data is reused for
CTexT fastText Skipgram String Embeddings
No full textThe CTexT Afrikaans fastText Skipgram String Embeddings is a 300 dimensional Afrikaans embedding model based on the Skipgram fastText architecture that provides real-valued vector representations for Afrikaans text. The embedding was trained on a corpus of 230 million words
Autshumato Monolingual Setswana Corpus
No full textMonolingual corpus for Setswana. The data is given as a single UTF-8 text file, with each segment on a newline. The data was specifically selected and formatted for use in the training of machine translation systems. Further clean-up and processing might be required depending on the task the data is reused for
CTexT Afrikaans FLAIR Named Entity Recognition model
No full textThe CTexT Afrikaans FLAIR Named Entity Recognition model is a neural NER model based on the FLAIR framework (Akbik et al. 2019), and includes Afrikaans fastText (Bojanowski et al., 2017) and FLAIR embeddings (Akbik et al. 2018) from the CTexT Afrikaans word and string embeddings. The model is trained on the NCHLT Afrikaans Named Entity Annotated Corpus
African Wordnet version 1.0
No full textDeveloped using the expand model with Princeton WordNet 3.1 as basis.
Please see https://africanwordnet.wordpress.com/ for all details on the project. This work builds on previously released data and is under active development. New releases will be made available at the end of every significant development phase
Autshumato English-Siswati Parallel Corpora
No full textAligned parallel corpora for the following language pair: English-SiSwati. The data is given as four separate UTF-8 text files, with each segment on a newline. Dataset contains existing data sourced for the DSAC funded Autshumato project as well as new data sourced for the SADiLaR: Parallel corpora for English into SiSwati project. The dataset contains the following types of bilingual data: Translations from English to Siswati and crawled parallel data for English-Siswati. The dataset comprises a total of 114,839 segments with 2,002,293 English words and 1, 423,414 SiSwati words.
(A new version issued since the title was changed
COVID-19 Multilingual Terminology
Full text linkCOVID-19 multilingual terminology list document in all the South African languages. The development of this terminology list was initiated by City of Tshwane and sponsored by the South African Centre for Digital Language Resources and the Department of Science and Innovation. PanSALB's national language boards assisted in the verification of the terminology list
English-IsiNdebele Glossary of Medical Terms
Full text linkThis is the PhD project, where English-isiNdebele glossary of medical terms was compiled by a PhD candidate
South African Multilingual Learner Corpus of Academic Texts (SAMuLCAT)
No full textNOTE: THIS HAS BEEN SUPERSEDED. See
https://hdl.handle.net/20.500.12185/585
The South African Multilingual Learner Corpus of Academic Texts (SAMuLCAT) is a multi-genre, multi-level learner corpus developed by the Inter-institutional Centre for Language Development and Assessment (ICELDA) in collaboration with the South African Centre for Digital Language Resources (SADiLaR). This corpus includes shorter and longer pieces of texts, from an array of genres, different fields of study, and at all levels of study. The corpus was, and continues to be, contributed to by several institutions of higher education that are part of the ICELDA network. Ethical clearance has been granted at all partnering institutions to collect data; this includes informed consent by all students who contributed to SAMULCAT. The corpus is augmented by two sets of metadata. The first set includes mainly biographical detail about students (completed by students themselves); the second set includes more information on different task types and texts included in the corpus (completed by e.g. lecturers, writing centre staff, etc.). Data can be filtered through the metadata filters available in the search functionality of the corpus. The corpus is available under the creative commons 4.0 license and is open source. Use of the corpus for research purposes requires permission from SADiLaR, and applications should include evidence of ethical clearance from the research institutions to which staff and students are affiliated to.
More information about the design of the corpus and metadata available in the corpus can be found in the following article:
Carstens, A. and Eiselen, R., 2019. Designing a South African multilingual learner corpus of academic texts (SAMuLCAT). Language Matters, 50(1), pp.64-83.
Annotation
Corpora for the indigenous South African languages are automatically annotated for lemmas and part of speech using the available NCHLT Text lemmatisers and part of speech taggers. Information on the accuracy and tag sets for these languages are available here: NCHLT Web Service. No quality control of the automatic annotations was performed.
The English data is annotated using the open-source NLP4J library available here: https://emorynlp.github.io/nlp4j
Generic Bilingual Academic Wordlist with Definitions
Full text linkThe academic wordlist has been developed to serve as a resource to students to assist them to better understand words used within the information they gather to complete their academic assignments and as a result enhance their academic career.
The bilingual wordlist contain 2427 terms with their part of speech indicated as well as definition and usage example provided in both Afrikaans and English.
A multilingual version of this wordlist is available here: https://repo.sadilar.org/handle/20.500.12185/66