Amharic Question Answering for list questions: A case of Ethiopian tourism

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Date

2013-06

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Addis Ababa University

Abstract

A Question answering (QA) system searches a large text collection and finds a short phrase or sentence that precisely answers a user's question. To solve a QA problem, we might first turn to traditional IR techniques, which have been applied successfully to large scale text search problems. Alternatively, the Natural Language Processing (NLP) and Information Extraction (IE) communities have developed techniques for extracting very precise answers from text. QA research attempts to deal with a wide range of question types including: fact, list, definition, How, Why, hypothetical, semantically constrained, and cross-lingual questions. This research work focuses on list questions in closed domain Amharic questions answering (AQA). It applies the hypothesis, which states that answers to a list questions have same semantic entity class, answers that co-occur within the sentences of the documents are related to the target and the question and sentences containing the answers share similar context. In this research work, list questions are answered using five major modules. The function of these modules are (1) determining the answer type, (2) document retrieval, (3) Extract answer candidates from the document, (4) computing similarity value for each pair of candidate answers based on their co-occurrence within the sentence, (5) selecting final answers. The QA system is evaluated and the experimental results show that the system registered an average of 57.5% F-score. Nevertheless, the performance of the system is greatly affected by the number for documents in the document database and techniques applied. As a result, we managed to develop a prototype for Amharic listing QA system in the area of Ethiopian tourism. KEYWORDS: Amharic Question Answering, List Questions, Answer Type, Candidate Answers, Co-occurance, Question Answering Evaluation.

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Amharic Question Answering, List Questions, Answer Type, Candidate Answers, Co-occurance, Question Answering Evaluation

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