Combining semantic information in question answering systems

This paper presents two proposals based on semantic information, semantic roles and WordNet, for the answer extraction module of a general open-domain question answering (QA) system. The main objective of this research is to determine how the system performance is influenced by using this kind of information, and compare it with that of current QA systems based on named entities (NEs). NE-based QA systems achieve good results with NE-based questions. However, with common noun (CN) based questions, like “Where is the stomach? In the abdomen  ”, they fail, and this is the main reason for our study. In this paper our new proposals for answering different types of questions are evaluated and compared with an NE-based approach for both NE-based and CN-based questions. From the results obtained it may be concluded that, with the aid of our proposals, the QA system performs much better with CN-based questions when semantic information is used (semantic information Fβ=1=74.73%Fβ=1=74.73% vs. NEFβ=1=12.19%). Moreover, the more semantic information the system uses, the better the precision and correctness of the answer it achieves.