Applying transformation-based error-driven learning to structured natural language queries

XML information retrieval (XML-IR) systems aim to provide users with highly exhaustive and highly specific results. To interact with XML-IR systems, users must express both their content and structural requirement, in the form of a structured query. Traditionally, these structured queries have been formatted using formal languages such as XPath or NEXI. Unfortunately, formal query languages are very complex and too difficult to be used by experienced, let alone casual users. Therefore, recent research has investigated the idea of specifying users' content and structural needs via natural language queries (NLQs). In previous research we developed NLPX, a natural language interface to an XML-IR system. Here we present additions we have made to NLPX. The additions involve the application of transformation-based error-driven learning (TBL) to structured NLQs, to derive special connotations and group words into an atomic unit of information. TBL has successfully been applied to other areas of natural language processing; however, this paper presents the first time it has been applied to structured NLQs. Here, we investigate the applicability of TBL to NLQs and compare the TBL-based system, with our previous system and a system with a formal language interference. Our results show that TBL is effective for structured NLQs, and that structured NLQs a viable interface tor XML-IR systems