Incorporation of corpus-specific semantic information into question answering context

In today's environment of information overload, Question Answering (QA) is a critically important research area for the Semantic Web. In order for humans to make effective use of the expansive information sources available to us, we require automated tools to help us make sense of large amounts of data. Within this framework, Question Context plays an important role. We define Question Context to be an semantic structure that can be used to enrich queries so that the user's information need is better represented. This paper describes the theoretical foundations of a novel approach that uses statistical language modeling techniques to create Question Context and to then integrate it into the Information Retrieval stage of QA. We base our approach on two established language modeling methods - the Aspect Model, which is the basis of Probabilistic Latent Semantic Analysis (PLSA) and Relevance-Based Language Models. Our approach proposes an Aspect-Based Relevance Language Model as the Question Context Model, and our methodology incorporates corpus-specific semantic concepts into the QA process. Words from the most heavily relevant aspects are then incorporated into the query. We present some interesting preliminary qualitative results that show the potential usefulness of the Question Context Model to both the first (IR) and second (Intelligent Information Processing) stages of QA.