Browsing by Author "Getachew Medhanit"

Now showing 1 - 1 of 1
  • No Thumbnail Available
    Item
    Amharic Question Answering for Factoid and List Questions Using Machine-Learning Approaches
    (Addis Ababa University, 2019-02-04) Getachew Medhanit; Abebe Ermias (Ato)
    Question answering is a system that allows users to ask questions about some topic in natural language and give exact answers by retrieving answers from collection of documents. Its main aim is to assist human to get exact answers to questions they ask. In addition, it avoids going through many documents to find a single answer to their questions. There are two types of questions in QA namely factoid and non-factoid questions. The first one comprises of what, where, when, who questions and the second one deals with list, definition, acronym, how questions. The focuses of this study are factoid and list questions. There are some researches conducted previously on question answering. Most of the researches used only SVM algorithm for question classification and any of them did not make use of named entity recognizer for answer extraction. In this study an attempt is made to design a list and factoid question answering using machine learning approach and an answer extraction that makes use of NER. This research is a closed domain QA for Amharic that focuses on Ethiopian history. It has three components. Question classification for identifying the types of questions which is done using two algorithms; HMM and SVM, passage retrieval that is performed by selecting the relevant sentences using sentence-level retrieval and answer extraction component selects answers from the top ranked sentences using a NER which is developed for this research. Factoid questions are answered by using key words matching and extraction using the NER from the question and the list questions are answered by using co-occurrence of answer types and candidate answers in a text. The study achieved an F-measure of 73% using the SVM classifier for question classification and an F-measure of 65% was achieved using the HMM classifier for question classification. From the result we achieved, we realized that question classification using SVM has a better answer extraction performance than the HMM. In addition, the use of NER tool helped answer extraction in getting exact answers.