  While open-domain question answering (QA) systems have proven effective for
answering simple questions, they struggle with more complex questions. Our goal
is to answer more complex questions reliably, without incurring a significant
cost in knowledge resource construction to support the QA. One readily
available knowledge resource is a term bank, enumerating the key concepts in a
domain. We have developed an unsupervised learning approach that leverages a
term bank to guide a QA system, by representing the terminological knowledge
with thousands of specialized vector spaces. In experiments with complex
science questions, we show that this approach significantly outperforms several
state-of-the-art QA systems, demonstrating that significant leverage can be
gained from continuous vector representations of domain terminology.
