Assabie, Yaregal (PhD)Girma, Seble2020-08-242023-11-042020-08-242023-11-043/3/2020http://etd.aau.edu.et/handle/123456789/22100Open Information Extraction is the process of discovering domain-independent relations by providing ways to extract unrestricted relational information from natural language text. It has recently received increased attention and applied extensively to various downstream applications, such as text summarization, question answering, and informational retrieval. Although a lot of Open Information Extraction systems have been developed for various natural language text, no research has been conducted yet for the development of Amharic Open Information Extraction (AOIE). As litrature has shown, the rule-based approach operating on deep parsed sentences yields the most promising results for Open Information Extraction systems. However, to the best of our knowledge, there is no fully implemented deep syntactic parser available for Amharic language. Therefore, in this thesis, we propose the development of a rule-based AOIE system that utilizes shallow parsed sentences. The proposed system has six components: Preprocessing, Morphological Analysis, Phrasal Chunking, Sentence Simplification, Relation Extraction, and Post-processing. In the Preprocessing, each word in the input text is labeled with an appropriate POS tag, and then well-formed and informative sentences are filtered out for further processing based on POS tags of words. The Morphological Analysis component produces morphological information about each word of input sentences. The phrasal chunking component divides the input sentence into non-overlapping phrases based on POS and morphological tags of words. The Sentence Simplification component segments the sentence into a number of self-contained simple sentences that are easier to process. In the Relation Extraction, relation instances are extracted from those simplified sentences and finally the post-processing components prints extracted relations in N-ary format. The proposed method and algorithms were implemented in prototype software and evaluated with a dataset from different domains. In the evaluation, we showed that the system achieved an overall precision of 0.88.enOpen Information ExtractionChunkingSentence SimplificationRelation ExtractionThesis