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• W11345478 abstract "The goal of this work has been to investigate how well a high-level task like text classification can be carried out on Amharic text. We have also investigated the effect that operations like stemming or part-of-speech tagging can have on text classification performance for such a highly inflectional language like Amharic. Amharic is the official working language of the federal government of the Federal Democratic Republic of Ethiopia and spoken by well over 20 million people as a first or second language. The actual size of the Amharic speaking population has to be based on estimates: Hudson (1999) analyzed the latest national Ethiopian census from 1994 and indicated that more than 40% of the (then) 53 million Ethiopians understood Amharic, with at the time about 17 million first language speakers. The current size of the Ethiopian population is estimated to be some 75 million people (CIA 2006). Amharic is the second most spoken Semitic language in the world (after Arabic) and closely related to Tigrinya. It is today probably the second largest language in Ethiopia (after Oromo, a Cushitic language) and possibly one of the five largest languages on the African continent. Following the Constitution drafted in 1993, Ethiopia is divided into nine fairly independent regions, each with its own nationality language. However, Amharic is the language for country-wide communication and was also for a long period the principal literal language and medium of instruction in primary and secondary schools of the country, while higher education is carried out in English. In spite of the relatively large number of speakers, Amharic is still a language for which very few computational linguistic resources have been developed, and very little has been done in terms of making useful higher level Internet or computer based applications available to those who only speak Amharic. It is generally believed that applications such as information retrieval, text classification, or document filtering could benefit from the existence and availability of basic tools such as stemmers, morphological analyzers or part-of-speech taggers. However, since so few language processing resources for Amharic are available, very little is known about their effect on retrieval or classification performance for this language. It has been argued that stemming can improve text categorization performance, especially for highly inflected languages like Amharic, and it has therefore been the goal of this work to explore this issue further." @default.
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• W11345478 date "2006-01-01" @default.
• W11345478 modified "2023-09-26" @default.
• W11345478 title "Applying machine learning to Amharic text classification" @default.
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