Algorithm for Semantic Network Generation from Texts of Low Resource Languages Such as Kiswahili
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Abstract (arXiv)
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.
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References · 7
- 2018Deep contextualized word representationscited by 2,290
- 2020TyDi QA: A Benchmark for Information-Seeking Question Answering in Typologically Diverse Languagescited by 488
- 2018Graph Convolutional Networks for Text Classificationcited by 345
- 2017Constructing Datasets for Multi-hop Reading Comprehension Across Documentscited by 190
- 2018Question Answering by Reasoning Across Documents with Graph Convolutional Networkscited by 56
- 2018Exploring Graph-structured Passage Representation for Multi-hop Reading Comprehension with Graph Neural Networkscited by 23
- 2022KenSwQuAD -- A Question Answering Dataset for Swahili Low Resource Languagecited by 12
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