Computer Science

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Browsing Computer Science by Author "Abera, Bekele"

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    Event Modeling from Amharic News Articles
    (Addis Ababa University, 2/4/2018) Abera, Bekele; Getahun, Fekade (PhD)
    With the increase of online information overload available to us, everyday tasks such as extracting, searching, understanding and relating relevant data have become intractable. A large percentage of these information are discussing past, current, and future real world events. Events are dynamic data structures that play a key role in understanding phenomena happing in real world, which are basically driven by the four ‘W’s’ (what, who, when, and where). This natural progression of questions is a classic example of what one might ask about an event. This results in natural way to explain complicated relations between people, places, actions and objects. Event centered modeling captures the dynamic aspects of an event along with semantic representation of event facts. In this research work, we have proposed and developed event extraction and representation model from Amharic news article. Event modeling involves key event identification, event elements extraction, and event semantic elements representation. Event triggers tells the action taking place in news article. To identify the mention of event in news article we used manually collected event trigger words and phrases from various news domains. For event elements extraction, we used named entity recognizer and other local features like potential trigger, event extent, path from the extent to head word of the trigger. Machine learning Maximum entropy classifier is trained using event related news article collected from Fana Broadcast Corporate news archive. For event representation, we designed ontology based event representation model that provides deeper semantic through event information representation. A prototype showing an event extraction and representation is developed using different programming environment. Evaluation of trigger identification and event elements extraction is carried out by comparing manually tagged news article with the automatically extracted event information by the system. The evaluation result shows that the trigger identifier module obtain precision (67.1%) of event correctly which contributes to the better event elements extraction. The event elements extractor component shows greater obtaining precision (69.1%) while event classification module classify about (72%) of event correctly. The representative ability of our event representation model is evaluated with respect to requirements and event dimensions we covered in this work.