Open Domain Factoid Question Answering Systems

Abstract

Question Answering (QA) is a field of Artificial Intelligence (AI) and Information Retrieval (IR) and Natural Language Processing (NLP), and leads to generating systems that answer to questions natural language in open and closed domains, automatically. Question Answering Systems (QASs) have to deal different types of user questions. While answers for some simple questions can be short phrases, answers for some more complex questions can be short texts. A question with a single is known as a factoid question, and a question answering system that deals with factoid questions is called a factoid QAS.In this thesis, we present a factoid QAS that consists of three phases: question processing, document/passage retrieval, and answer processing. In the question processing phase, we consider a new two-level category structure using machine learning techniques to generate search engine from user questions queries.