On the Usage of Semantics, Syntax, and Morphology for Noun Classification in I si Z ulu (2025)

Abstract: There is limited work aimed at solving the core task of noun classification for Nguni languages. The task focuses on identifying the semantic categorisation of each noun and plays a crucial role in the ability to form semantically and morphologically valid sentences. The work by Byamugisha (2022) was the first to tackle the problem for a related, but non-Nguni, language. While there have been efforts to replicate it for a Nguni language, there has been no effort focused on comparing the technique used in the original work vs. contemporary neural methods or a number of traditional machine learning classification techniques that do not rely on human-guided knowledge to the same extent. We reproduce Byamugisha (2022)’s work with different configurations to account for differences in access to datasets and resources, compare the approach with a pre-trained transformer-based model, and traditional machine learning models that relyon less human-guided knowledge. The newly created data-driven models outperform the knowledge-infused models, with the best performing models achieving an F1 score of 0.97.

## introduction solid performance when using modern natural language processing (nlp) approaches, especially ones that are popular with languages like english, is dependent on the availability of large text corpora. unlike english, all niger-congo b 1 (ncb) languages do not have large training datasets; hence, contemporary techniques have 1 some authors use the term bantu languages not been used for tasks such as noun classification. since the languages are characterized by agglutinative morphology, have an intricate noun class system, and possess little datasets and tools that can be repurposed for various tasks @xcite , most problems have been tackled with knowledge-infused approaches. the discrepancy of resource availability also means that there are limited efforts to contrast contemporary datadriven and knowledge-infused techniques to determine whether there is any difference in performance. in this paper, we address this lack of comparison for the task of noun class disambiguation for ncb languages. using isizulu, the focus of this paper and the largest language in south africa by l1 speakers, as a case study the task consists of predicting the noun class (e.g., nc2) when one is given a noun (e.g., abantu 'people'). we limit our investigation to this task since it is a crucial but unsolved problem for all ncb languages, isizulu especially. due to ncb languages' low-resourced state, the only work that tackles the task for a ncb language was done by @xcite focusing on runyankore and related languages from guthrie's zone j @xcite . @xcite 's work deals with the lack of a large dataset of noun and class pairs by introducing a number of modules, each solving some crucial function. some modules use unlabelled or automatically labelled datasets that, when combined are able to predict the noun class of the noun. byamugisha's work is a promising start, since it obtained accuracies in the range 80%-87% for runyankore. the work does not resolve the question of technique comparison; hence, the utility of relying on a multi-modular and knowledgeinfused approach that combines morphology, syntax, and morphology vs. machine learning techniques and a neural model, especially a large language model (llm) adapted via the pretrain-finetune paradigm for classification, is still unclear. all of the aforementioned models have the potential to classify nouns using morphology, syntax, and morphology but differ in the following way: ## ncb noun classification ncb languages are found in more than 54 countries, with an estimated 240 million speakers, and they have a lot of diversity @xcite . nonetheless, they all have a noun class system that categorises each noun to one of 23 classes, as informally summarized in the article dog subj. noun is singl.identif ier un negation -healthy adjective isizulu: inja ayiphilile the formation of the word ayiphilile 'is unhealthy' relies on identifying the noun class (here: nc9) of the subject inja 'dog'. however, there are no models for automatically classifying nouns into their respective classes for isizulu. ## existing models for noun classification the only work that has tackled the task at hand, for ncb languages at least, was conducted by byamugisha and it took inspiration from the existing linguistic theory on the ncb noun class system by modelling the possible avenues for classifying a noun namely the morphological prefix, semantic categorization and syntactical context (byamugisha, 2022). they pursue the task via a multimodular knowledge-infused model, whose function will now be described. the simplest avenue relies on the prefix. byamugisha's model uses the morphological prefix information to classify a noun if it is unique. for instance, the noun abantu 'people' has as prefix aba-and stem -ntu and the prefix aba-is unique to nc2, the noun will be correctly classified. the noun umuntu 'person' has as prefix umu-, but it is ambiguous, because the prefix associated with both nc1 and nc3 is either um-or umu-depending on the number of syllables of the stem. this simple model will only output a prediction if a unique prefix is found otherwise it is considered ambiguous and continues to the next step. when the prefix is insufficient, it draws on the semantic generalizations to determine the noun class. this is done by training a new model to determine similar words, using fast-text foot_0 with a corpus of 1 million sentences, to determine a noun's semantic neighbours. for instance, for the runyankore noun omuntu 'person' from nc1, the model determines that the nearest neighbours are omugyesi 'reaper' (nc1), omutaahi 'companion' (nc1), omukoreesa 'overseer' (nc1), omushomesa 'teacher' (nc1), and omukuru 'elder' (nc1). the semantic information derived from the nearest neighbours allows discerning between ambiguous classes, since information associated with, e.g., omuntu 'person' (nc1) can be used to distinguish it from the noun omukono 'arm', based on the noun class frequencies associated with its neighbours. the noun omukono shares the same prefix omu-, but one retrieves different neighbours, such as omunwa 'mouth' (nc3), omutwe 'head' (nc3), eriino 'tooth' (nc5), and enkokora 'elbow' (nc9), i.e., body parts, vs. omuntu 'person' and certain roles they play. fundamentally, the differentiation between the two is done by analysing the noun classes associated with the neighbouring words and ascertaining that nc1 is the most common class among the neighbours for omuntu 'person', hence, the input noun is inferred to belong to the same class. the determination of the most common class among the neighbours requires filtering out some elements. specifically, when given neighbouring nouns, without any labels, a corpus made up of 1 million sentences is used to train a fasttext classifier, where the corpus' is annotated with partsof-speech, the noun class, and the concord (where possible). the resulting model is used to annotate the input neighbouring words and if these predictions are found to be inconsistent then they are dropped from consideration. the concord annotation is then used for the syntax-based filtering step because it is unique among the classes @xcite . alternative work involving processing ncb nouns exists, but it does not tackle the problem of noun classification; specifically, the efforts on building morphological analysers @xcite , morphological generators @xcite , part-of-speech taggers @xcite , and noun pluralization tools @xcite show attempts to deal with the ambiguity of nouns. other researchers have created a massively multilingual transformer-based encoder-only language model, named serengeti, whose training data includes isizulu @xcite . however, none of these models have been investigated, despite their potential capability, to classify nouns or to compare them with byamugisha (2022)'s approach. ## new dataset for the experiments the aim of the experiments is to ascertain and compare the performance of multiple methods, detailed in sections 5-7. in this section, we describe the dataset that is used to compare the techniques. we created a new isizulu dataset by extracting nouns and their classes from the oxford zulu-english dictionary (de schryver, gilles-maurice, 2015) via optical character recognition and manual cleaning. we created two versions of the dataset where one version is labelled with a single noun class, either singular or plural depending on the modality of the noun, and the second is labelled with the singular and plural classes. for instance, the word umuntu 'person' is labelled with the singular noun class 1 in one dataset and labelled with the singular and plural combined classes 1/2 in another. the dataset version that combines noun classes is only used to train some of the traditional machine learning models and the details are provided in section 6. the number of nouns per class in the dataset is listed in ## knowledge-infused models we created multiple variations of byamugisha (2022)'s multi-modular classifier to support isizulu. this is done by creating multiple versions of each module in the architecture, labelled a-g in figure 1 . we now turn to describe the design decisions and resources used. component a this module identifies the noun class via the noun's prefix. we use component d this module takes each of the predicted neighbouring nouns, produced by modules b and c, and labels them with a noun class and/or a class-specific concord using a classifier. this annotation classifier is trained from scratch. since we do not have access to a contextfree grammar to generate training data à la byamugisha (2022) for the classifier, we investigated the use of different datasets to determine the impact of certain characteristics (e.g., annotation quality); all features are listed in component e and f these modules are responsible for automatically filtering nearestneighbouring nouns using either a part-of-speech classifier or regular expressions. since module d did not annotate the words with a part-of-speech, these modules rely on a newly trained pos classifier for the annotations. the classifier was trained on web-crawled data with simplified pos tags and sourced from (du toit and puttkammer, 2021). the new classifier is able to identify verbs with 96% accuracy when tested against the combined gold standard datasets listed in the second phase of filtering removes words that do not contain a morpheme associated with their predicted noun class. there are two alternative models considered to achieve this. the first version (i.e., subword-level) removes a neighbour if the morpheme associated with predicted label is not contained in the word, by matching all possible versions of it (including phonological conditioned variations) (keet and @xcite . the second model (i.e., word-level) filters neighbours based on their subwords. it fetches the character n-gram range for the word model, computes all substrings for the word that matches that length, labels each subword with a noun class and concord using the previously mentioned classifier and returns true if the neighbour's predicted label is in the set of predictions for its subwords. component g this module is responsible for identifying the noun class from the set of annoword ✓ tated words produced by the previous steps in the pipeline. it does so by computing the frequencies for each noun class found in the dataset and identifies the class with the highest count in the final list of nearest neighbours. the most common class is then used as the final prediction. we compared the various versions of the knowledge-infused model by determining their accuracies on the keet dataset, listed in ## traditional machine learning models to create novel supervised ml models that rely on morphosyntax, and possibly syntax and semantics, for noun classification we considered four supervised machine learning algorithms and models. in addition, we also experimented with various ways of preprocessing and representing the nouns. we describe the choices made regarding these elements in the following subsections. ## results the results of the internal evaluation of the reproduced knowledge-infused technique are presented in figure 2 . the best performing model relies on a expert labelled dataset, with syntax and verb information, for component d (n-gw in the results from comparing all the developed models are provided in ## discussion we now revisit the problem of inferring a noun's noun class by the various techniques and determining whether the use of semantics, syntax, and morphology, in a human-guided setting, yields the best results. the results obtained show that the neural-based models that make use of semantics, syntax, and morphology without human-guidance (the fnn and pre-trained llm) and the traditional machine learning models that rely only on morphology, with less human-guided knowledge, perform better. for nc detection, is better to rely on data-driven models that use human-guided knowledge in a less labour intensive approach where there are fewer modules so that errors are not propagated and have less negative impact on performance. this is evidenced by the observation that all the models that achieve an f1 score above 0.9, as listed in the performance difference between the fnn and llm is small (4%), in particular considering the simplicity of the fnn. this suggests that pretraining offers limited benefit for the current task. when comparing the traditional machine learning 4 . 2023 )'s findings on the utility of compression also apply in the context of a nguni language. since not all the compression-based models outperform the neural models, this might demonstrate that there is utility in using minimal knowledge in traditional machine learning models. this is because the best performing model is an ensemble that exploits the fact that it is easier to identify the plural and singular noun classes of a noun vs. predicting the singular or plural in isolation. then it disambiguates between just the plural vs. singular classes instead of 15, unlike the single class prediction problem. as such, this may indicate that there is value in using insights about a language in less labour intensive ways. ## conclusions in reproducing the work by @xcite with different configurations and techniques, the results showed that the neural and ml models perform best, with an f1 score of 0.97, while the replicated models achieve a score of 0.71 despite their reliance on human-guided knowledge. in future work, we plan to consider also other ncb languages and determine whether the number of 'ambiguous' prefixes among the number of prefixes might influence a technique's performance. we also plan to investigate the use of transformer-based and decoder-focused models.