Noun Class Disambiguation in R unyankore and Related Languages (2022)

Abstract: Bantu languages are spoken by communities in more than half of the countries on the African continent by an estimated third of a billion people. Despite this populous and the amount of high quality linguistic research done over the years, Bantu languages are still computationally under-resourced. The biggest limitation to the development of computational methods for processing Bantu language text is their complex grammatical structure, chiefly in the system of noun classes. We investigated the use of a combined syntactic and semantic method to disambiguate among singular nouns with the same class prefix but belonging to different noun classes. This combination uses the semantic generalizations of the types of nouns in each class to overcome the limitations of relying only on the prefixes they take. We used the nearest neighbors of a query word as semantic generalizations, and developed a tool to determine the noun class based on resources in Runyankore, a Bantu language indigenous to Uganda. We also investigated whether, with the same Runyankore resources, our method had utility in other Bantu languages, Luganda, indigenous to Uganda, and Kinyarwanda, indigenous to Rwanda. For all three languages, the combined approach resulted in an improvement in accuracy, as compared to using only the syntactic or the semantic approach.

## introduction over the last three decades, there has been an increase in the development of computational resources for bantu languages. much of this work applies the knowledge gained on bantu language linguistics to digitize textual resources and develop software tools for text processing for a single language or for a group of languages. among the textual resources created are the sawa english-kiswahili parallel corpus for machine learning @xcite and the labelled and unlabelled runyankore datasets @xcite . the text processing resources include morphological tools, such as morphological analyzers for isizulu @xcite , isixhosa, seswati, and isindebele @xcite ; text generation tools, such as a morphological generator for isizulu @xcite and surface realizers for isizulu @xcite and runyankore @xcite ; part-of-speech taggers for kiswahili, ciluba, northern sotho, and isizulu @xcite ; and noun pluralization tools for isizulu and runyankore @xcite , chishona, isixhosa, kikuyu, kinyarwanda, and luganda @xcite . despite these efforts, bantu languages are still among the most computationally under-resourced languages in the world. this is due to their complex grammatical structure, mainly the noun class system, verb morphology, and agglutinative morphology. in this paper, we focus on the noun class system only, the hallmark of bantu nominal morphology @xcite . this system places every noun in a language into a class, based on the semantics of a noun first (such as whether the noun is of a human or non-human entity), then the morphology of a noun next (which is based on the prefix of a noun) @xcite . the importance of the noun class to computational text processing goes beyond classifying nouns, and also determines the formulation of other parts of speech, such as adjectives, verbs, possessives, determinants, grammatical number, etc. because a noun class is central to an extensive system of concordial agreement that determines morphological composition @xcite . given that there exists in some noun class systems class prefixes that are not unique among different noun classes, a problem of ambiguity exists when determining a noun class using a class prefix only, as is the case with morphological approaches. we refer to the process of determining the correct noun class under these circumstances as noun class disambiguation, which, to the best of our knowledge, has not yet been solved. @xcite state that using a noun's semantics and extending beyond morphology to syntax by considering concords, can overcome the limitations of morphological approaches, but it requires large resources that capture the context in which a noun is used. we therefore investigated whether it is possible to extend morphological approaches to syntactic approaches by including the syntax of an entire sentence, and further combine this with the semantics of a noun in order to undertake noun class disambiguation. we used the following questions: we investigated the applicability of a combined syntactic and semantic approach as a means of noun class disambiguation among singular nouns, using word vectors pre-trained using fasttext @xcite on two one million sentence datasets in runyankore, one dataset unlabelled and the other labelled with morphological information including the noun class @xcite . the syntactic method relies both on the morphology of a noun based on its prefix and on the syntax of other grammatical units in a sentence which are determined by a noun class. the semantic method uses the noun class labels of the nearest neighbors of a query word. only singular nouns were used in this investigation because there is ground-truth data from the singular wordlists used in the noun pluralization tools with which to evaluate the results, and these same tools apply the knowledge on the singular/plural pairings to solve the problem of plurals computationally. we started with two datasets in runyankore, the same language as the word vectors used, and obtained accuracies of 80.54% and 85.23% on two test sets. we then investigated whether, using a combined approach with the same runyankore word vectors, a correct noun class determination can be made for another bantu language. using luganda and kinyarwanda, we obtained accuracies of 73.97% and 63.64%, respectively. to the best of our knowledge, this is the first computational attempt to use both the syntactic and semantic characteristics of a noun to disambiguate among noun classes with the same class prefix. the rest of this paper is arranged as follows: section 2 provides information on bantu languages, with a focus on the noun class system; section 3 details the materials, methods, and results from using a combined syntactic and semantic approach to determine a noun class; section 4 discusses the implications of this work; and we conclude in section 5. ## bantu language noun class system bantu languages are a group of languages indigenous to africa @xcite . they extend from the south, below nigeria, to most of central, east, and southern africa, as shown in figure 1 @xcite . there are bantu-speaking communities in 27 of the continent's 54 countries, with about 240 million speakers @xcite . the exact number of languages classified as bantu ranges from 300 to 680, based on different criteria by different authors @xcite . figure 1 : the spread of bantu languages across africa @xcite bantu languages assign all nouns to a class re-ferred to as a noun class (nc); and there are over 20 noun classes, though some ncs have fallen into disuse in most languages @xcite @xcite . the semantic generalizations of the types of nouns in each class are shown in noun class description of associated nouns 1 and 2 people and kinship 3 and 4 plants, nature, and some parts of the body 5 and 6 fruits, liquids, some parts of the body, and paired things 7 and 8 inanimate objects 9 and 10 tools and animals 11 long thin stringy objects, languages, and inanimate objects 12 and 13 diminutives 14 abstract concepts 15 infinitives and parts of the body 16, 17, and 18 locative classes 19 diminutives 20, 21, and 22 augmentatives 23 locative class the simple noun comprises a prefix and a stem @xcite ; for example, omuntu 'person' in runyankore, which can be analyzed as the prefix o-muand stem -ntu. therefore, in addition to the semantic categorization of nouns shown in noun classes do not only classify nouns, but are at the heart of an extensive system of concordial agreement @xcite , where each class determines the agreement with: concord patterns; nominal prefix in nouns, locatives, and adjectives; numeral prefix; pronominal prefix for substitutives, connectives, possessives, demonstratives, and determinatives; initial prefix in absolutive verb forms; and the verbal infix @xcite @xcite @xcite . given how central the noun class system is to bantu language computational linguistics, it is important to identify a means of determining the noun class of a noun. ## noun class disambiguation using syntax and semantics in this research, we aimed to find out to what extent determining the semantics of a noun is beneficial to disambiguating among noun classes, in order to determine its noun class correctly. in byamugisha (2020), it was shown that from word vectors trained on one million runyankore sentences in an unsupervised manner, the nearest neighbors (co-occurrence vectors) obtained for a query word reflect the semantic generalizations of noun classes shown in our approach to disambiguating among noun classes of a noun also extends beyond the morphology of a noun and considers the presence of the concords in a sentence that are indicative of a noun class. we combine this method with a noun's semantics as defined by its nearest neighbors. the details on each of these two methods are explained in the following sections. ## discussion we developed an approach to disambiguate among noun classes in order to determine a noun's class that combines a noun's syntax and semantics in order to achieve the best results. our approach is in line with literature on the bantu noun class system that states that the assignment of nouns to a class is based on semantic generalizations of the types of nouns in each class, as well as morphologically, according to their class prefixes, and syntactically, according to the concords they take @xcite . while our work is focused on bantu languages, it is important to note that noun class systems are a strong areal feature in africa, with an estimated two-thirds of the languages on the continent having noun classes @xcite . the theory on which our approach is based might be applicable to another family of languages in africa. that a combined syntactic and semantic approach is necessary during text processing for bantu languages is not novel, as this is the basis of the tools on noun pluralization for runyankore and isizulu @xcite and chishona, isixhosa, kikuyu, kinyarwanda, and luganda @xcite . what is emphasized by our results, however, is that, for the semantics, a little goes a long way. only 385 singular nouns were used in the generation of the runyankore datasets @xcite on which the word vectors were trained; yet, as seen in the results in the main reason for needing a semantic approach is to overcome noun class ambiguity, a situation where nouns belonging to different noun classes have the same class prefix. therefore, though useful, the reuse of the resources in one language to determine the semantics of another language is limited if the source language does not account for the nouns affected in the target language. for example, in addition to classes 1 and 3 having the same prefix in luganda and kinyarwanda, classes 5, 9, and 23 in luganda also have the same prefix, e-, and classes 5 and 9 in kinyarwanda also have the same prefix, i-; yet their semantic distinctions are not all captured in the runyankore resources. having language-specific resources is the desired outcome, though the ability to reuse resources provides a benefit. the increase in accuracy on test sets in languages that are different from the resources on which the semantics are determined is also a notable finding. though the selection of luganda and kinyarwanda was purposively based on the availability of resources, their results cannot be removed entirely from their grouping in guthrie zones as compared to runyankore. guthrie zones are a referential classification of bantu languages which groups together languages with similar linguistic features (such as phonetic, semantic, and syntactic) that are geographically colocated, without presupposing their historical relatedness @xcite . guthrie zones categorize bantu languages into 16 geographic zones, which are labeled using the letters a, b, c, d, e, f, g, h, j, k, l, m, n, p, r, and s; and these are further subdivided into decades (zone j.10, where runyankore belongs, contains individual languages labeled from j.11 to j.19, while j.20, j.30, etc. represent different zones) @xcite @xcite . figure 3 shows how this classification covers the bantu languages throughout the african continent. figure 3 : the spread and classification of bantu languages into guthrie zones @xcite runyankore and luganda are in zone j.10, while kinyarwanda is in zone j.60 @xcite . when considered from the perspective of guthrie zone classification, the results in ## conclusion in this paper, we have presented the use of a combined syntactic and semantic approach to address noun class ambiguity when determining a singular noun's class. the semantic approach, which is based on the type of the noun, is necessary to perform noun class disambiguation, which addresses the main limitation of the morphological approach, that is, the presence of nouns with the same prefix but belonging to different noun classes. we used a noun's nearest neighbors in word vectors as representations of a noun's semantic generalizations, and a noun's concords, together with its morphology, as representations of a noun's syntactic association. we developed a tool based on our combined approach using pre-trained word vectors in runyankore, and evaluated the accuracy of the tool with two runyankore datasets, achieving up to 87.18% accuracy. we also evaluated the reusability of our approach to other bantu languages, luganda and kinyarwanda, whilst relying on runyankore resources for the semantic approach. we achieved accuracies of 73.97% in luganda and 63.64% in kinyarwanda. additionally, for all three languages, the combined approach performed better than individual morphological and semantic approaches.