Kamba Part of Speech Tagger Using Memory Based Approach

Kituku, Benson; George, Musumba; Wagacha, Peter Waiganjo

doi:10.5121/ijnlc.2015.4204

Cited by 3 publications

(3 citation statements)

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“…Annotators also need some training in the use of these tools. In such scenarios and as an alternative, POS annotation can be done using spreadsheets as was done for some low resource languages of Kenya such as Kikamba (Kituku et al, 2015;Pauw et al, 2006). In this research, we adopted the use of spreadsheets as the project duration was short and it would have taken longer to train the linguistic annotators on the use of a toolkit such as GATE (Cunningham, 2002).…”

Section: Creation Of Annotation Datasetsmentioning

confidence: 99%

See 1 more Smart Citation

Kencorpus: A Kenyan Language Corpus of Swahili, Dholuo and Luhya for Natural Language Processing Tasks

Wanjawa

Wanzare

Indede

et al. 2023

JLCL

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Indigenous African languages are categorized as under-served in Natural Language Processing. They therefore experience poor digital inclusivity and information access. The processing challenge with such languages has been how to use machine learning and deep learning models without the requisite data. The Kencorpus project intends to bridge this gap by collecting and storing text and speech data that is good enough for data-driven solutions in applications such as machine translation, question answering and transcription in multilingual communities. The Kencorpus dataset is a text and speech corpus for three languages predominantly spoken in Kenya: Swahili, Dholuo and Luhya (three dialects of Lumarachi, Lulogooli and Lubukusu). Data collection was done by researchers who were deployed to the various data collection sources such as communities, schools, media, and publishers. The Kencorpus' dataset has a collection of 5,594 items, being 4,442 texts of 5.6 million words and 1,152 speech files worth 177 hours. Based on this data, other datasets were also developed such as Part of Speech tagging sets for Dholuo and the Luhya dialects of 50,000 and 93,000 words tagged respectively. We developed 7,537 Question-Answer pairs from 1,445 Swahili texts and also created a text translation set of 13,400 sentences from Dholuo and Luhya into Swahili. The datasets are useful for downstream machine learning tasks such as model training and translation. Additionally, we developed two proof of concept systems: for Kiswahili speech-to-text and a machine learning system for Question Answering task. These proofs provided results of a performance of 18.87% word error rate for the former, and 80% Exact Match (EM) for the latter system. These initial results give great promise to the usability of Kencorpus to the machine learning community. Kencorpus is one of few public domain corpora for these three low resource languages and forms a basis of learning and sharing experiences for similar works especially for low resource languages. Challenges in developing the corpus included deficiencies in the data sources, data cleaning challenges, relatively short project timelines and the Coronavirus disease (COVID-19) pandemic that restricted movement and hence the ability to get the data in a timely manner.

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Section: Creation Of Annotation Datasetsmentioning

confidence: 99%

“…POS annotations require tagsets. Different languages tend to have different tags e.g., the Kiswahili tagset (Hurskainen, 2016), Kamba tagset (Kituku et al, 2015). This realization therefore leads to the need for some middle ground tagset, such as the universal target set proposed by Petrov et al (2011).…”

Section: Creation Of Annotation Datasetsmentioning

confidence: 99%

Kencorpus: A Kenyan Language Corpus of Swahili, Dholuo and Luhya for Natural Language Processing Tasks

Wanjawa

Wanzare

Indede

et al. 2023

JLCL

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“…The concord for possessive pronouns, morph phonological changes in adjectives and verbs, the morphology of compound Nouns and adjectives are yet to be done. With respect to language resource tools, there are only two language tools for this language to the best of our knowledge-these are a Part of Speech tagger and a named entity recognizer [10] [11].…”

Section: Introductionmentioning

confidence: 99%

Towards Kikamba Computational Grammar

Kituku¹,

Nganga²,

Muchemi³

2019

JDAIP

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The under-resourced Kikamba language has few language technology tools since the more efficient and popular data driven approaches for developing them suffer from data sparseness due to lack of digitized corpora. To address this challenge, we have developed a computational grammar for the Kikamba language within the multilingual Grammatical Framework (GF) toolkit. GF uses the Interlingua rule-based translation approach. To develop the grammar, we used the morphology driven strategy. Therefore, we first developed regular expressions for morphology inflection and thereafter developed the syntax rules. Evaluation of the grammar was done using one hundred sentences in both English and Kikamba languages. The results were an encouraging four n-gram BLEU score of 83.05% and the Position independent error rate (PER) of 10.96%. Finally, we have made a contribution to the language technology resources for Kikamba including multilingual machine translation, a morphology analyzer, a computational grammar which provides a platform for development of multilingual applications and the ability to generate a variety of bilingual corpora for Kikamba for all languages currently defined in GF, making it easier to experiment with data driven approaches.

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