Algorithm for Semantic Network Generation from Texts of Low Resource Languages Such as Kiswahili
This addresses the challenge of machine processing for low-resource languages, which are important for daily communication but lack adequate data, though it is incremental as it builds on existing semantic network concepts.
The paper tackles the problem of processing low-resource languages like Kiswahili by developing an algorithm to generate semantic networks from raw text, bypassing the need for training data, and achieves up to 78.6% exact match on a Kiswahili question-answering task.
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.