Logo Lanfrica
  • Accueil
  • Atlas
  • Analyses
  • Documentation
  • Sign in

© 2026 Lanfrica. Tous droits réservés. Tous les droits d'auteur des ressources affichées sur le site Web Lanfrica appartiennent aux détenteurs de droits d'auteur d'origine, sauf indication contraire explicite.

Exploring Afrikaans word embeddings with analogies and nearest neighbours

Domaine:

natural language processing

Type de record:

dataset
Créateur:
TanRoald Eiselen
Éditeur:
Dig
Hôte:
This paper presents an exploration of word embeddings for Afrikaans using the analogies and nearest neighbours methodologies. We compare the results on three types of embeddings (fastText, FLAIR and GloVe) on a novel analogy data set for Afrikaans, inspired by the Bigger Analogy Test Set: BATS (Gladkova et al. 2016). Our analysis shows that for Afrikaans, similar to English, the types of embeddings influence the quality of analogies found for different linguistic tasks. Our investigation also demonstrates, however, that these Afrikaans embeddings do not encode as clear a linguistic representation as with English embeddings. The exact reason for this is subject to future work, but the added morphological complexity and the lack of data most likely play a role.

Visit

doi.org

Tasks

embeddings

Languages

Afrikaans

Similaires

CTexT Afrikaans GloVe Word EmbeddingsDetecting Lassa Fever Using Ant Lion Optimization with SMOTE and Edited Nearest NeighboursDebiasing Word Embeddings with Nonlinear GeometryUnsupervised POS Induction with Word EmbeddingsisiZulu Word EmbeddingsMassively Multilingual Word Embeddings

CTexT Afrikaans GloVe Word Embeddings

The CTexT Afrikaans GloVe Word Embeddings is a 300 dimensional Afrikaans embedding model based on th

Detecting Lassa Fever Using Ant Lion Optimization with SMOTE and Edited Nearest Neighbours

Lassa fever remains a significant public health concern in West Africa, with an estimated 100,000 to

Debiasing Word Embeddings with Nonlinear Geometry

Debiasing word embeddings has been largely limited to individual and independent social categories.

Unsupervised POS Induction with Word Embeddings

Unsupervised word embeddings have been shown to be valuable as features in supervised learning probl

isiZulu Word Embeddings

Massively Multilingual Word Embeddings

We introduce new methods for estimating and evaluating embeddings of words in more than fifty langua