Practical Approach on Implementation of W ord N ets for S outh A frican Languages (2021)

Abstract: This paper proposes the implementation of WordNets for five South African languages, namely, Sepedi, Setswana, Tshivenda, isiZulu and isiXhosa to be added to open multilingual WordNets (OMW) on natural language toolkit (NLTK). The African WordNets are converted from Princeton WordNet (PWN) 2.0 to 3.0 to match the synsets in PWN 3.0. After conversion, there were 7157, 11972, 1288, 6380, and 9460 lemmas for Sepedi, Setswana, Tshivenda, isiZulu and isiX- hosa respectively. Setswana, isiXhosa, Sepedi contains more lemmas compared to 8 languages in OMW and isiZulu contains more lemmas compared to 7 languages in OMW. A library has been published for continuous development of African WordNets in OMW using NLTK.

## introduction wordnet consists of information about adverbs, adjectives, verbs and nouns in english and it organizes the words according to the notion of a synset. a synset can be defined as a set of words that are interchangeable in certain context. for example, the set {house, home, building} form a synset since the words can be used interchangeably referring to the same concept. synsets can be linked to each other by means of semantic relations such as meronymy (leaf-tree), hypernymy versus hyponymy relation (flower-rose). the interlinked synsets create a strong semantic network that allows researchers (a) to automatically expand their search queries in information retrieval tasks @xcite , (b) to artificially expand their dataset by making use of data augmentation in natural language processing (nlp) tasks @xcite , (c) to improve cybercrime investigation in social network mining tasks @xcite . wordnets have been applied in many domains such as machine learning classification to improve the performance of classification algorithms. an example is the role of wordnets is to increase the amount of nlp task data via data augmentation @xcite . this has been done for many well-resourced (european) languages. for low-resource languages such as south african languages, few studies have been done to build wordnets under low resources. african wordnets (bosch and @xcite is a project that develop aligned wordnets for african languages spoken in south africa. initially, the project included five languages sepedi, setswana, tshivenda, isixhosa and isizulu. during the development, debvisdic (wordnet editor) was used to build semantic networks. due to limited resources, the expand model was followed during the development of the african wordnets. the expand model in wordnets creation is when the structure of princeton wordnet is used to create other wordnets in other languages. the goal of this paper is to build a multilingual lexical database with wordnets for south african languages based on the princeton wordnet 3 to be utilized using the python nltk foot_0 library via open multilingual wordnets (omw) foot_1 . we utilize the wordnets previously built by bosch and @xcite for sepedi, setswana, tshivenda, isixhosa and isizulu, to be compatible with omw standard. the main contributions of this paper are as fol-lows: the library can be found on github foot_2 and python repository foot_3 . the outline of this paper is as follows: in section 2 we discuss literature review of wordnets and their applications. section 3 describes the methodology taken to create the resource. section 4 concludes the paper with future work. ## literature review this section discusses the current wordnets and their applications in various domains. ## methodology this section discusses the design and implementation of the wordnets for south african languages. it first discusses sense map preparation, then wordnets conversion, and finally implementation.