Abstract-Natural language processing (NLP) is a subfield of computer science, with strong connections to artificial intelligence. One area of NLP is concerned with creating proofing systems, such as grammar checker. Grammar checker determines the syntactical correctness of a sentence which is mostly used in word processors and compilers. For languages, such as Afan Oromo, advanced tools have been lacking and are still in the early stages. In this paper a rule based grammar checker is presented. The rule base is entirely developed and dependent on the morphology of the language . The checker is evaluated and shown a promising result.
This paper presents designing a rule based Afan Oromo Disambiguator. The ultimate aim of this work is to develop a model that identifies the senses of the words. Hence; a word may have multiple senses, the problem is to find out which particular sense is appropriate in a given context. To this end, a rule based approach was used which is designed manually a set of rules. Some ambiguous words were collected from the Oromo society and these words are the most frequently used in the society. Due to under the resource of the language, the work was used 15 natural ambiguous words for the sake of the test. The results of the work were shown that in Afan Oromo language, an ambiguous word have 2 to the n senses (where n unlimited senses; as the number of contexts increased).
Malaria is a serious and fatal disease caused by a parasite that can infect a certain type of mosquito which feeds on human blood. It is a public health problem in Ethiopia and a major cause of illness and death. More than 75% of the total land of Ethiopia is malarious affecting more than 68% of the population, making malaria the leading public health problem in Ethiopia. In an effort to address such problems, it is important to develop knowledge-based system (KBS) that can provide advice for health professionals and patients to facilitate diagnosis and treatment of malaria patients. Experimental research design was used to developed prototype system. Purposive sampling technique was used to select domain experts for knowledge acquisition. The domain experts are selected from Jimma special hospital, Adama hospital and Agaro health centre. The knowledge was acquired using both structured and unstructured interviews from domain experts and represented by production rule, (if- then method). The user's acceptance of the prototype system by visual interaction method that by showing the prototype system to the domain experts was conducted result is 83.21%. In addition, performance of the prototype system was evaluated using case testing method and produce result of 82.3%. It is promising to save the life of people in rural area where there is scarcity of health professionals and apparatus. In addition, it is possible to reduce time and cost of diagnosis and treatment in health centre by implementing intelligent systems. Developing in local languages, good interface programming language and in other techniques are the future works of the study.
After summarization of the principles underlying the creation of an index to support word finding, we present some results concerning its automatic creation. As this is a very complex problem, we confined ourselves to a subset of relations, meronymy, i.e. part-of relations.
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