Exploring West African Folk Narrative Texts Using Machine Learning

Abstract

This paper examines how machine learning (ML) and natural language processing (NLP) can be used to identify, analyze, and generate West African folk tales. Two corpora of West African and Western European folk tales are compiled and used in three experiments on cross-cultural folk tale analysis. In the text generation experiment, two types of deep learning text generators are built and trained on the West African corpus. We show that although the texts range between semantic and syntactic coherence, each of them contains West African features. The second experiment further examines the distinction between the West African and Western European folk tales by comparing the performance of an LSTM (acc. 0.79) with a BoW classifier (acc. 0.93), indicating that the two corpora can be clearly distinguished in terms of vocabulary. An interactive t-SNE visualization of a hybrid classifier (acc. 0.85) highlights the culture-specific words for both. The third experiment describes an ML analysis of narrative structures. Classifiers trained on parts of folk tales according to the three-act structure are quite capable of distinguishing these parts (acc. 0.78). Common n-grams extracted from these parts not only underline cross-cultural distinctions in narrative structures, but also show the overlap between verbal and written West African narratives.

Rhythm pattern discovery in Niger-Congo story-telling

“…The recordings in both languages were made during fieldwork in Côte d'Ivoire in 2001 and 2002. In each case, the interactive narrative scenario is the same and is characteristic of story-telling scenarios in the Kwa languages (Berry and Spears, 1991;Ninan et al, 2016;Lô et al, 2020).…”

Bearing a Bag-of-Tales: An Open Corpus of Annotated Folktales for Reproducible Research

“…The little one can say about the plethora of methods listed is that, regardless of the corpora, their regionality, and the analytical units whose distributions characterise the body of texts in question, they express similarity between items in terms of distance, with more similar items forming dense groups as the outcome of mass comparison. Cluster analysis (Thuillard et al, 2018), Principal Component Analysis (PCA) (Berezkin, 2015), Labelled Latent Dirichlet Allocation (L-LDA) (Karsdorp & van den Bosch, 2013), Support Vector Machines (SVM) (Nguyen et al, 2012;Meder et al, 2016), or deep learning by Recurrent Neural Networks (RNN) (Lô, de Boer, & van Aart, 2020), however, share the same nature of being static snapshots of collections. Hence there is a contradiction in principle in addressing text evolution, a dynamic phenomenon, through tools tailored to static measurements: the notion asks for vector fields instead of vector spaces (Darányi et al, 2016).…”

“…also report that a large amount of ATU data has recently been made available online by the Multilingual Folk Tale Database (MFTD), 6 which also offers annotation facilities for tales in multilingual versions. We found only a single recent study (Lô, de Boer, & van Aart, 2020) which published a corresponding tale corpus to promote reproducibility, albeit without ATU type labels. 7…”