Algorithm for Semantic Network Generation from Texts of Low Resource Languages Such as Kiswahili

TL;DR

This paper presents an algorithm that converts Kiswahili text into semantic networks, enabling tasks like question answering without extensive training data, achieving up to 78.6% accuracy.

Contribution

The paper introduces a novel algorithm for generating semantic networks from low-resource language texts, specifically Kiswahili, bypassing the need for large training datasets.

Findings

Achieved up to 78.6% exact match in Kiswahili QA task

Demonstrated effectiveness of semantic networks for low-resource languages

Provided a practical approach for NLP tasks in Kiswahili

Abstract

Processing low-resource languages, such as Kiswahili, using machine learning is difficult due to lack of adequate training data. However, such low-resource languages are still important for human communication and are already in daily use and users need practical machine processing tasks such as summarization, disambiguation and even question answering (QA). One method of processing such languages, while bypassing the need for training data, is the use semantic networks. Some low resource languages, such as Kiswahili, are of the subject-verb-object (SVO) structure, and similarly semantic networks are a triple of subject-predicate-object, hence SVO parts of speech tags can map into a semantic network triple. An algorithm to process raw natural language text and map it into a semantic network is therefore necessary and desirable in structuring low resource languages texts. This algorithm tested on the Kiswahili QA task with upto 78.6% exact match.