Automatic Amharic text News Classification: A Neural Networks Approach
| dc.contributor.advisor | Zhou, Lina(PhD) | |
| dc.contributor.advisor | Yilma, Aklilu (PhD) | |
| dc.contributor.author | Alemayehu, Wube | |
| dc.date.accessioned | 2020-06-25T08:10:02Z | |
| dc.date.accessioned | 2023-11-18T12:46:39Z | |
| dc.date.available | 2020-06-25T08:10:02Z | |
| dc.date.available | 2023-11-18T12:46:39Z | |
| dc.date.issued | 2005-01 | |
| dc.description.abstract | Parsing is important in Natural Language Processing. A parser allows a collection of Sentences to be analyzed in terms of its well- formlessness according to a per-defined grammar. A parser can be applied in Machine translation, spelling correction, grammar checking, summary, speech processing, question-answering systems. This paper presents a rule based parser for Amharic sentence disambiguation. The parser analyzes structurally ambiguous sentences with a rule base method. Using an active chart, the parser first generates all possible parses of a sentence according to given grammar rules. It employees depth-first search strategy in invoking rules and bottom up parsing strategy in constructing parses. In order to resolve structurally ambiguous sentences, the parser incorporates a dictionary that containing the headwords of noun phrases and verb phrases and their categories that is used to uniquely identify the groups of headwords. Structural rules are also provided for each category of the headwords in the dictionary. The performance of the parser is measured with accuracy, which is evaluated by comparing the automatically parsed sentences against the hand-parsed sentences in the test set. The result shows that 86% of the sentences are parsed correctly . The result achieved is very encouraging. Given the small sample size available for rules induction, the parser can be improved by evaluating it on a large collection of Amharic sentences in future . | en_US |
| dc.identifier.uri | http://etd.aau.edu.et/handle/12345678/21844 | |
| dc.language.iso | en | en_US |
| dc.publisher | Addis Ababa University | en_US |
| dc.subject | Information Science | en_US |
| dc.title | Automatic Amharic text News Classification: A Neural Networks Approach | en_US |
| dc.type | Thesis | en_US |