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Advances of Science and Technology. 8th EAI International Conference, ICAST 2020, Bahir Dar, Ethiopia, October 2-4, 2020, Proceedings, Part I

Research Article

Amharic Open Information Extraction with Syntactic Sentence Simplification

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  • @INPROCEEDINGS{10.1007/978-3-030-80621-7_33,
        author={Seble Girma and Yaregal Assabie},
        title={Amharic Open Information Extraction with Syntactic Sentence Simplification},
        proceedings={Advances of Science and Technology. 8th EAI International Conference, ICAST 2020, Bahir Dar, Ethiopia, October 2-4, 2020, Proceedings, Part I},
        proceedings_a={ICAST},
        year={2021},
        month={7},
        keywords={Open Information Extraction Chunking Sentence Simplification Relation Extraction},
        doi={10.1007/978-3-030-80621-7_33}
    }
    
  • Seble Girma
    Yaregal Assabie
    Year: 2021
    Amharic Open Information Extraction with Syntactic Sentence Simplification
    ICAST
    Springer
    DOI: 10.1007/978-3-030-80621-7_33
Seble Girma1, Yaregal Assabie1
  • 1: Department of Computer Science

Abstract

Open Information Extraction (OIE) is the process of discovering domain-independent relations from natural language text. It has recently received increased attention and been applied extensively to various downstream applications, such as text summarization, question answering, and informational retrieval. In this paper, we propose a method of OIE for Amharic language. To improve the performance of relation extraction, the proposed OIE method implements a sentence simplification technique that breaks down complex and compound sentences into simple sentences. Linguistic rules are utilized to extract domain-independent and unanticipated relation instances with their arguments from simple sentences. The proposed method and algorithms are implemented and evaluated with a dataset from different domains. Test results show that the system achieved an overall precision of 0.88.

Keywords
Open Information Extraction Chunking Sentence Simplification Relation Extraction
Published
2021-07-15
Appears in
SpringerLink
http://dx.doi.org/10.1007/978-3-030-80621-7_33
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