Enabling Cross-lingual Information Retrieval for African Languages

Language diversity in NLP is critical in enabling the development of tools for a wide range of users. However, there are limited resources for building such tools for many languages, particularly those spoken in Africa. For search, most existing datasets feature few to no African languages, directly impacting researchers’ ability to build and improve information access capabilities in those languages. Motivated by this, we created AfriCLIRMatrix, a test collection for cross-lingual information retrieval research in 15 diverse African languages automatically created from Wikipedia. The dataset comprises 6 million queries in English and 23 million relevance judgments automatically extracted from Wikipedia inter-language links. We extract 13,050 test queries with relevant judgments across 15 languages, covering a significantly broader range of African languages than other existing information retrieval test collections. In addition to providing a much-needed resource for researchers, we also release BM25, dense retrieval, and sparse-dense hybrid baselines to establish a starting point for the development of future systems. We hope that our efforts will stimulate further research in information retrieval for African languages and lead to the creation of more effective tools for the benefit of users

Semi-automatic retrieval of definitional information: a northern Sotho case study

Corpus-based terminology is currently gaining ground on the international front. It is therefore important that terminologists working on the South African Bantu languages not only take note of this development, but that they should also follow this trend, even if they do not have the same measure of access to highly sophisticated software. The aim of this article is therefore to establish whether it is possible to retrieve definitional information on key concepts from untagged, running text by making use of affordable and easily accessible software such as WordSmith Tools. In order to answer this question, a case study is done in Northern Sotho, using textual material on linguistics as basis for a special field corpus. Syntactic and lexical patterns serving as textual markers of definitional information are identified and the success rate of the computational retrieval of definitional information is analysed and evaluated. Attention is also paid to the retrieval of specifically conceptual information, which turned out to be a fortunate by-product of semi-automatic retrieval of definitional information. Finally, it is illustrated how definitional information retrieved can be utilised in the writing of a formal terminological definition. Keywords: terminology, south african bantu languages, definitional information, semi-automatic information retrieval, terminological definitions, conceptual relationships, lexical patterns, syntactic patterns, textual markers, keyword-in-context (kwic), wordsmith tool

The African Stopwords project:curating stopwords for African languages

Stopwords are fundamental in Natural Language Processing (NLP) techniques for information retrieval. One of the common tasks in preprocessing of text data is the removal of stopwords. Currently, while high-resource languages like English benefit from the availability of several stopwords, low-resource languages, such as those found in the African continent, have none that are standardized and available for use in NLP packages. Stopwords in the context of African languages are understudied and can reveal information about the crossover between languages. The \textit{African Stopwords} project aims to study and curate stopwords for African languages. In this paper, we present our current progress on ten African languages as well as future plans for the project

AfriQA:Cross-lingual Open-Retrieval Question Answering for African Languages

African languages have far less in-language content available digitally, making it challenging for question answering systems to satisfy the information needs of users. Cross-lingual open-retrieval question answering (XOR QA) systems -- those that retrieve answer content from other languages while serving people in their native language -- offer a means of filling this gap. To this end, we create AfriQA, the first cross-lingual QA dataset with a focus on African languages. AfriQA includes 12,000+ XOR QA examples across 10 African languages. While previous datasets have focused primarily on languages where cross-lingual QA augments coverage from the target language, AfriQA focuses on languages where cross-lingual answer content is the only high-coverage source of answer content. Because of this, we argue that African languages are one of the most important and realistic use cases for XOR QA. Our experiments demonstrate the poor performance of automatic translation and multilingual retrieval methods. Overall, AfriQA proves challenging for state-of-the-art QA models. We hope that the dataset enables the development of more equitable QA technology

The transliteration principle: is this the best procedure in African language lexicography and terminology?

The creation of target language equivalents in bilingual dictionaries have always been a challenge to both lexicographers and terminologists. Lexicographers, terminologists, subject specialists and linguists face a challenging task of supplying term equivalents for foreign international languages. The lack of a sufficient technical vocabulary in the African languages poses challenges to lexicographers and terminologists when supplying technical equivalents. They often regard transliteration as the quickest lexicographical and terminological procedure without taking the practical demands of users into account. This impedes the optimal retrieval of semantic information by the target users of dictionaries. This article discusses possible reasons why transliteration should not be regarded as the first but the last resort in lexicography and terminology. It proposes different lexicographical and terminological procedures lexicographers and terminologists can use to supply accurate and appropriate translation equivalents without making excessive use of transliteration. In this way a scientific language will develop which could assist users to communicate successfully in the mother tongue. Keywords: terms, term equivalents, terminologists, subject specialists, linguists, international languages, international scientific languages, underdeveloped languages, transliteration, terminological principle, translation equivalents, semantic information, borrowing, target language

Classifying Amharic News Text Using Self-Organizing Maps

The paper addresses using artificial neural networks for classification of Amharic news items. Amharic is the language for countrywide communication in Ethiopia and has its own writing system containing extensive systematic redundancy. It is quite dialectally diversified and probably representative of the languages of a continent that so far has received little attention within the language processing field. The experiments investigated document clustering around user queries using Self-Organizing Maps, an unsupervised learning neural network strategy. The best ANN model showed a precision of 60.0% when trying to cluster unseen data, and a 69.5% precision when trying to classify it

Conceptual graph-based knowledge representation for supporting reasoning in African traditional medicine

Although African patients use both conventional or modern and traditional healthcare simultaneously, it has been proven that 80% of people rely on African traditional medicine (ATM). ATM includes medical activities stemming from practices, customs and traditions which were integral to the distinctive African cultures. It is based mainly on the oral transfer of knowledge, with the risk of losing critical knowledge. Moreover, practices differ according to the regions and the availability of medicinal plants. Therefore, it is necessary to compile tacit, disseminated and complex knowledge from various Tradi-Practitioners (TP) in order to determine interesting patterns for treating a given disease. Knowledge engineering methods for traditional medicine are useful to model suitably complex information needs, formalize knowledge of domain experts and highlight the effective practices for their integration to conventional medicine. The work described in this paper presents an approach which addresses two issues. First it aims at proposing a formal representation model of ATM knowledge and practices to facilitate their sharing and reusing. Then, it aims at providing a visual reasoning mechanism for selecting best available procedures and medicinal plants to treat diseases. The approach is based on the use of the Delphi method for capturing knowledge from various experts which necessitate reaching a consensus. Conceptual graph formalism is used to model ATM knowledge with visual reasoning capabilities and processes. The nested conceptual graphs are used to visually express the semantic meaning of Computational Tree Logic (CTL) constructs that are useful for formal specification of temporal properties of ATM domain knowledge. Our approach presents the advantage of mitigating knowledge loss with conceptual development assistance to improve the quality of ATM care (medical diagnosis and therapeutics), but also patient safety (drug monitoring)

Problems and opportunities of applying data-& audio-mining techniques to ethnic music

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Natural Language Processing at the School of Information Studies for Africa

The lack of persons trained in computational linguistic methods is a severe obstacle to making the Internet and computers accessible to people all over the world in their own languages. The paper discusses the experiences of designing and teaching an introductory course in Natural Language Processing to graduate computer science students at Addis Ababa University, Ethiopia, in order to initiate the education of computational linguists in the Horn of Africa region