In some IR applications, it is desirable to adopt a high precision search strategy to return a small set of documents that are highly focused and relevant to the user's information need. With these applications in mind, we investigate semantic search using the XML Fragments query language on text corpora automatically pre-processed to encode semantic information useful for retrieval. We identify three XML Fragment operations that can be applied to a query to conceptualize, restrict, or relate terms in the query. We demonstrate how these operations can be used to address four different query-time semantic needs: to specify target information type, to disambiguate keywords, to specify search term context, or to relate select terms in the query. We demonstrate the effectiveness of our semantic search technology through a series of experiments using the two applications in which we embed this technology and show that it yields significant improvement in precision in the search results.

QA-by-Dossier-with-Constraints is a new approach to Question Answering whereby candidate answers' confidences are adjusted by asking auxiliary questions whose answers constrain the original answers. These constraints emerge naturally from the domain of interest, and enable application of real-world knowledge to QA. We show that our approach significantly improves system performance (75% relative improvement in F-measure on select question types) and can create a "dossier" of information about the subject matter in the original question.

The ability to evaluate intermediate results in a Question Answering (QA) system, which we call introspection, is necessary in architectures based on planning or on processing loops. In particular, it is needed to determine if an earlier phase must be retried, or if the response "No Answer" must be offered. We look at an introspection task of performing a cursory evaluation of the search engine output in a QA system. We define this task as a concept-learning problem and evaluate two classifiers that use features based on score progression in the ranked list returned by the search engine and candidate answer types. Our experiments showed promising results, achieving 25% relative improvement over a majority class baseline on unseen data.