Improving the Computational Morphological Analysis of a Swahili Corpus for Lexicographic Purposes

Pauw, G De; Schryver, G-M De

doi:10.4314/lex.v18i1.47257

Cited by 12 publications

(11 citation statements)

References 7 publications

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“…An increasing number of publications however are showing that carefully selected corpus-based procedures can indeed bootstrap language technology for languages such as Amharic (Gambäck et al 2009), Northern Sotho (de Schryver andFaaß et al 2009), Swahili (De Pauw et al 2006De Pauw and de Schryver 2008;Steinberger et al this volume), Tswana (Groenewald 2009) and even very resource-scarce African languages Badenhorst et al, this volume;Scannell, this volume). This paper continues this novel and promising new trend in African language technology research, by presenting the development and deployment of the SAWA corpus, a two-million-word parallel corpus English-Swahili.…”

Section: Introductionmentioning

confidence: 93%

Exploring the sawa corpus: collection and deployment of a parallel corpus English—Swahili

Pauw

Wagacha

Schryver

2011

Lang Resources & Evaluation

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Research in machine translation and corpus annotation has greatly benefited from the increasing availability of word-aligned parallel corpora. This paper presents ongoing research on the development and application of the SAWA corpus, a two-million-word parallel corpus English-Swahili. We describe the data collection phase and zero in on the difficulties of finding appropriate and easily accessible data for this language pair. In the data annotation phase, the corpus was semi-automatically sentence and word-aligned and morphosyntactic information was added to both the English and Swahili portion of the corpus. The annotated parallel corpus allows us to investigate two possible uses. We describe experiments with the projection of part-of-speech tagging annotation from English onto Swahili, as well as the development of a basic statistical machine translation system for this language pair, using the parallel corpus and a consolidated database of existing English-Swahili translation dictionaries. We particularly focus on the difficulties of translating English into the morphologically more complex Bantu language of Swahili.

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Section: Introductionmentioning

confidence: 93%

Exploring the sawa corpus: collection and deployment of a parallel corpus English—Swahili

Pauw

Wagacha

Schryver

2011

Lang Resources & Evaluation

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“…To perform this kind of morphological analysis, we developed a machine learning system trained and evaluated on the Helsinki corpus of Swahili (Hurskainen, 2004). Experimental results show that the data-driven approach achieves stateof-the-art performance in a direct comparison with a rule-based method, with the added advantage of being robust to word forms for previously unseen lemmas (De Pauw and de Schryver, 2008). We can consequently use morphological deconstruction as a preprocessing step for the alignment task, similar to the method described by Goldwater and McClosky (2005), Oflazer (2008) and Stymne et al (2008).…”

Section: Nimemkataliamentioning

confidence: 99%

The SAWA corpus

Pauw

Wagacha

Schryver

2009

Proceedings of the First Workshop on Language Technologies for African Languages - AfLaT '09

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Research in data-driven methods for Machine Translation has greatly benefited from the increasing availability of parallel corpora. Processing the same text in two different languages yields useful information on how words and phrases are translated from a source language into a target language. To investigate this, a parallel corpus is typically aligned by linking linguistic tokens in the source language to the corresponding units in the target language. An aligned parallel corpus therefore facilitates the automatic development of a machine translation system and can also bootstrap annotation through projection. In this paper, we describe data collection and annotation efforts and preliminary experimental results with a parallel corpus English -Swahili.

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“…There is scarcity of sources in the sense that the digital text resources are few. The recent effort on the same is handled carefully with selected procedure for Swahili [2,3]. For language technology applications such as speech recognition system, text-to-speech synthesis, machine aided translation and web related issues there is a great need for translation and usability of the Swahili language.…”

Section: Introductionmentioning

confidence: 99%

Development of Isolated Numeric Speech Corpus for Swahili Language for Development of Automatic Speech Recognition System

Oirere¹,

Deshmukh²,

Shrishrimal³

2013

IJCA

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Speech corpus being the basic requirement for the development of Automatic speech recognition (ASR) system, it should be done with much accuracy in order to enhance the performance of the system. This paper describes the proposed procedure to abide while collecting the speech corpus of Swahili language from the native and non native speaker for the development of Automatic Speech Recognition system in Swahili language.

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Improving the Computational Morphological Analysis of a Swahili Corpus for Lexicographic Purposes

Cited by 12 publications

References 7 publications

Exploring the sawa corpus: collection and deployment of a parallel corpus English—Swahili

Exploring the sawa corpus: collection and deployment of a parallel corpus English—Swahili

The SAWA corpus

Development of Isolated Numeric Speech Corpus for Swahili Language for Development of Automatic Speech Recognition System

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