alemiaschi/clic-it_corpus huggingface dataset:
Nominal Class Assignment in Swahili
Authors: Giada Palmieri, Konstantinos Kogkalidis (Università di Bologna, Aalto University, 2024)
Topic: Distributional Semantics

Abstract
We discuss the open question of the relation between semantics and nominal class assignment in Swahili. We approach the problem from a computational perspective, aiming first to quantify the extent of this relation, and then to explicate its nature, taking extra care to suppress morphosyntactic confounds. Our results are the first of their kind, providing a quantitative evaluation of the semantic cohesion of each nominal class, as well as a nuanced taxonomic description of its semantic content.

Introduction
Swahili has a grand total of 18 nominal classes (\ie{} `genders'). There is no consensus on the extent to which the assignment of a noun to a given class is determined by its semantic content. We explore this question from a computational angle. Our experiments suggest semantic cohesion among nominal classes, and provide a summary of the taxonomic concepts associated to each class.

Conclusion
We explored the relation between semantics and nominal class assignment in Swahili. We approached the question from two complementary computational angles. Verifying first the presence of a relation using supervised learning, we then sought to explicate its nature using unsupervised topic modeling. Starting from a blank slate and without any prior interpretative bias, our methodology rediscovered go-to theories of Swahili nominal classification, while also offering room for further insights and explorations. Our work is among the first to tackle Bantu nominal assignment computationally, and the first to focus exclusively on semantics. Our methodology is typologically unbiased and computationally accessible, allowing for an easy extension to other languages, under the sole requirement of a dictionary. We make our scripts and generated artifacts publicly available at . We leave several directions open to future work. We have experimented with a single dataset, a single model and a single lexical database; varying either of these coordinates and aggregating the results should help debias our findings. We have only looked for semantic generalizations across hyperonymic taxonomies -- looking at other kinds of lexical relations might yield different semantic observations. Our chosen metric of relevance is by construction limited to first-order pairwise interactions, failing to account for exceptional cases or conditional associations. Finally, we had to resort to computational acrobatics through English in order to access necessary tools and resources. This is yet another reminder of the disparities in the pace of `progress' of language technology, and a call for the computational inclusion of typologically diverse languages.