Handling Ambiguity Problems of Natural Language Interface for Question Answering

The Natural language question (NLQ) processing module is considered a fundamental component in the natural language interface of a Question Answering (QA) system, and its quality impacts the performance of the overall QA system. The most difficult problem in developing a QA system is so hard to find an exact answer to the NLQ. One of the most challenging problems in returning answers is how to resolve lexical semantic ambiguity in the NLQs. Lexical semantic ambiguity may occurs when a user's NLQ contains words that have more than one meaning. As a result, QA system performance can be negatively affected by these ambiguous words. In this research, we aim to resolve this problem by introducing CKCO (Context Knowledge & Concepts Ontology) approach. This approach integrates context knowledge and concepts ontology of a domain, into a shallow natural language processing (SNLP) technique. Concepts knowledge is modeled using ontology, while context knowledge contains a set of words with their senses obtained from WordNet Domain and a group of words within the proposed domain serve as context labels, and it is determined based on neighborhood words in the NLQ. We applied CKCO approach to a university QA domain for new students to examine the impact of WSD in retrieving correct answers. Experimental results show that the CKCO approach together with other components of our QA system yield a result which is 83.4% for precision. We focus on the ambiguity of nouns in the NLQ.