According to the Constitution of South Africa, the government is required to make all the infor-mation in the ten indigenous languages of South Africa (excluding English), available to the public. For this reason, the government made the information, that already existed for these ten languages, available to the public and an effort is also been made to increase the amount of resources available in these languages (Groenewald & Du Plooy, 2010). This release of infor-mation further helps to implement Krauwer‟s (2003) idea that there is an inventory for the mini-mal number of language-related resources required for a language to be competitive at the level of research and teaching. This inventory is known as the "Basic Language Resource Kit" (BLARK). Since most of the languages in South Africa are resource scarce, it is of the best in-terest for the cultural growth of the country, that each of the indigenous South African languages develops their own BLARK. In Chapter 1, the need for the development of an implementable named entity recogniser (NER) for Afrikaans is discussed by first referring to the Constitution of South Africa’s (Republic of South Africa, 2003) language policy. Secondly, the guidelines of BLARK (Krauwer, 2003) are discussed, which is followed by a discussion of an audit that focuses on the number of re-sources and the distribution of human language technology for all eleven South African languages (Sharma Grover, Van Huyssteen & Pretorius, 2010). In respect of an audit conducted by Sharma Grover et al. (2010), it was established that there is a shortage of text-based tools for Afrikaans. This study focuses on this need for text-based tools, by focusing on the develop-ment of a NER for Afrikaans. In Chapter 2 a description is given on what an entity and a named entity is. Later in the chapter the process of technology recycling is explained, by referring to other studies where the idea of technology recycling has been applied successfully (Rayner et al., 1997). Lastly, an analysis is done on the differences that may occur between Afrikaans and Dutch named entities. These differences are divided into three categories, namely: identical cognates, non-identical cognates and unrelated entities. Chapter 3 begins with a description of Frog (van den Bosch et al, 2007), the Dutch NER used in this study, and the functions and operation of its NER-component. This is followed by a description of the Afrikaans-to-Dutch-converter (A2DC) (Van Huyssteen & Pilon, 2009) and finally the various experiments that were completed, are explained. The study consists of six experiments, the first of which was to determine the results of Frog on Dutch data. The second experiment evaluated the effectiveness of Frog on unchanged (raw) Afrikaans data. The following two experiments evaluated the results of Frog on “Dutched” Afrikaans data. The last two experiments evaluated the effectiveness of Frog on raw and “Dutched” Afrikaans data with the addition of gazetteers as part of the pre-processing step. In conclusion, a summary is given with regards to the comparisons between the NER for Afri-kaans that was developed in this study, and the NER-component that Puttkammer (2006) used in his tokeniser. Finally a few suggestions for future research are proposed. / MA (Applied Language and Literary Studies), North-West University, Vaal Triangle Campus, 2013

According to the Constitution of South Africa, the government is required to make all the infor-mation in the ten indigenous languages of South Africa (excluding English), available to the public. For this reason, the government made the information, that already existed for these ten languages, available to the public and an effort is also been made to increase the amount of resources available in these languages (Groenewald & Du Plooy, 2010). This release of infor-mation further helps to implement Krauwer‟s (2003) idea that there is an inventory for the mini-mal number of language-related resources required for a language to be competitive at the level of research and teaching. This inventory is known as the "Basic Language Resource Kit" (BLARK). Since most of the languages in South Africa are resource scarce, it is of the best in-terest for the cultural growth of the country, that each of the indigenous South African languages develops their own BLARK. In Chapter 1, the need for the development of an implementable named entity recogniser (NER) for Afrikaans is discussed by first referring to the Constitution of South Africa’s (Republic of South Africa, 2003) language policy. Secondly, the guidelines of BLARK (Krauwer, 2003) are discussed, which is followed by a discussion of an audit that focuses on the number of re-sources and the distribution of human language technology for all eleven South African languages (Sharma Grover, Van Huyssteen & Pretorius, 2010). In respect of an audit conducted by Sharma Grover et al. (2010), it was established that there is a shortage of text-based tools for Afrikaans. This study focuses on this need for text-based tools, by focusing on the develop-ment of a NER for Afrikaans. In Chapter 2 a description is given on what an entity and a named entity is. Later in the chapter the process of technology recycling is explained, by referring to other studies where the idea of technology recycling has been applied successfully (Rayner et al., 1997). Lastly, an analysis is done on the differences that may occur between Afrikaans and Dutch named entities. These differences are divided into three categories, namely: identical cognates, non-identical cognates and unrelated entities. Chapter 3 begins with a description of Frog (van den Bosch et al, 2007), the Dutch NER used in this study, and the functions and operation of its NER-component. This is followed by a description of the Afrikaans-to-Dutch-converter (A2DC) (Van Huyssteen & Pilon, 2009) and finally the various experiments that were completed, are explained. The study consists of six experiments, the first of which was to determine the results of Frog on Dutch data. The second experiment evaluated the effectiveness of Frog on unchanged (raw) Afrikaans data. The following two experiments evaluated the results of Frog on “Dutched” Afrikaans data. The last two experiments evaluated the effectiveness of Frog on raw and “Dutched” Afrikaans data with the addition of gazetteers as part of the pre-processing step. In conclusion, a summary is given with regards to the comparisons between the NER for Afri-kaans that was developed in this study, and the NER-component that Puttkammer (2006) used in his tokeniser. Finally a few suggestions for future research are proposed. / MA (Applied Language and Literary Studies), North-West University, Vaal Triangle Campus, 2013

When working in the terrain of text processing, metadata about a particular text plays an important role. Metadata is often generated using automatic text classification systems which classifies a text into one or more predefined classes or categories based on its contents. One of the dimensions by which a text can be can be classified, is the genre of a text. In this study the development of an automatic genre classification system in a resource scarce environment is postulated. This study aims to: i) investigate the techniques and approaches that are generally used for automatic genre classification systems, and identify the best approach for Afrikaans (a resource scarce language), ii) transfer this approach to other indigenous South African resource scarce languages, and iii) investigate the effectiveness of technology recycling for closely related languages in a resource scarce environment. To achieve the first goal, five machine learning approaches were identified from the literature that are generally used for text classification, together with five common approaches to feature extraction. Two different approaches to the identification of genre classes are presented. The machine learning-, feature extraction- and genre class identification approaches were used in a series of experiments to identify the best approach for genre classification for a resource scarce language. The best combination is identified as the multinomial naïve Bayes algorithm, using a bag of words approach as features to classify texts into three abstract classes. This results in an f-score (performance measure) of 0.929 and it was subsequently shown that this approach can be successfully applied to other indigenous South African languages. To investigate the viability of technology recycling for genre classification systems for closely related languages, Dutch test data was classified using an Afrikaans genre classification system and it is shown that this approach works well. A pre-processing step was implemented by using a machine translation system to increase the compatibility between Afrikaans and Dutch by translating the Dutch texts before classification. This results in an f-score of 0.577, indicating that technology recycling between closely related languages has merit. This approach can be used to promote and fast track the development of genre classification systems in a resource scarce environment. / MA (Linguistics and Literary Theory), North-West University, Potchefstroom Campus, 2013

When working in the terrain of text processing, metadata about a particular text plays an important role. Metadata is often generated using automatic text classification systems which classifies a text into one or more predefined classes or categories based on its contents. One of the dimensions by which a text can be can be classified, is the genre of a text. In this study the development of an automatic genre classification system in a resource scarce environment is postulated. This study aims to: i) investigate the techniques and approaches that are generally used for automatic genre classification systems, and identify the best approach for Afrikaans (a resource scarce language), ii) transfer this approach to other indigenous South African resource scarce languages, and iii) investigate the effectiveness of technology recycling for closely related languages in a resource scarce environment. To achieve the first goal, five machine learning approaches were identified from the literature that are generally used for text classification, together with five common approaches to feature extraction. Two different approaches to the identification of genre classes are presented. The machine learning-, feature extraction- and genre class identification approaches were used in a series of experiments to identify the best approach for genre classification for a resource scarce language. The best combination is identified as the multinomial naïve Bayes algorithm, using a bag of words approach as features to classify texts into three abstract classes. This results in an f-score (performance measure) of 0.929 and it was subsequently shown that this approach can be successfully applied to other indigenous South African languages. To investigate the viability of technology recycling for genre classification systems for closely related languages, Dutch test data was classified using an Afrikaans genre classification system and it is shown that this approach works well. A pre-processing step was implemented by using a machine translation system to increase the compatibility between Afrikaans and Dutch by translating the Dutch texts before classification. This results in an f-score of 0.577, indicating that technology recycling between closely related languages has merit. This approach can be used to promote and fast track the development of genre classification systems in a resource scarce environment. / MA (Linguistics and Literary Theory), North-West University, Potchefstroom Campus, 2013

To be able to mitigate the climate change and disasters that will come with it and to ensure economic growth, there is a need for change. A good start is more renewable energy and less harmful emissions. It is known that solar energy is sustainable and made from an endless source, the sun. However, it is not known how much impact the photovoltaic solar panels have through its entire lifecycle, from extraction of raw materials to End of Life management. This study has investigated photovoltaic solar panels' full life cycle to see how sustainable they really are. Including where the biggest opportunities for improvement of environmental, financial and social sustainability within the value chain is found. The results have been obtained by conducting a literature study, interviews with people with expertise of different parts of the value chain and finally calculations have been made to compare and visualize the findings. Two main ways to improve the PV panels’ negative impact in terms of environmental, financial and social sustainability have been established. Firstly, the study suggests the importance of implementing advanced recycling within the value chain. Recycling a high percentage of materials in the PV panel, and reusing the recovered material in production will decrease the energy consumption and harmful emissions significantly, alongside increasing circularity of critical materials and bring both financial and social benefits. Secondly, moving the better part of the production to Europe from China would also decrease the negative impact of the PV panels, especially the environmental and social impact, the study could however not find sufficiently good arguments for financial improvement to move the production to Europe. To be conclusive, this subject would need further studies. / För att kunna lindra klimatförändringar och de medföljande katastroferna och säkerställa den ekonomiska tillväxten finns det ett stort behov av förändring. En bra start är att använda mer förnybar energi och som bidrar till färre skadliga utsläpp. Det är känt att solenergi är hållbart med bränsle från en oändlig källa, solen. Det är emellertid inte känt hur stor påverkan solcellspanelerna har under hela dess livscykel, från utvinning av råvaror till dess panelens liv är över. Denna studie har undersökt solcellspanelernas hela livscykel för att se hur hållbara de egentligen är. Studien har även studerat var de största möjligheterna för förbättring av miljömässig, finansiell och social hållbarhet inom värdekedjan finns. Resultaten har erhållits genom att genomföra en litteraturstudie, intervjuer av personer med expertis inom olika delar av värdekedjan och slutligen har beräkningar gjorts för att jämföra och visualisera resultaten. Två huvudsakliga sätt att förbättra solpanelernas negativa påverkan när det gäller miljömässig, ekonomisk och social hållbarhet har identifierats. För det första föreslår studien vikten av att implementera avancerad återvinning inom värdekedjan. Återvinning av en hög andel material i solcellspanelen och återanvändning av det återvunna materialet i produktionen kommer att minska energiförbrukningen och skadliga utsläpp avsevärt samt förbättra cirkuläriteten av kritiska material och medföra både ekonomiska och sociala fördelar. För det andra skulle förflyttning av den större delen av produktionen till Europa från Kina också minska de negativa effekterna av solcellspaneler, särskilt de miljömässiga och sociala effekterna, studien kunde dock inte hitta tillräckligt med goda argument för att en förflyttning av produktionen till Europa skulle leda till en ekonomisk förbättring. För att detta ska vara avgörande skulle detta ämne behöva ytterligare studier.