Mathematical formulas introduce complications to the standard approaches used in information retrieval. By studying how traditional (sparse) search systems perform in matching queries to documents, we hope to gain insights into which features in the formulas and in the accompanying natural language text signal likely relevance.

Most optimization techniques deployed in information extraction systems assume that source documents are static. Instead, extracted relations can be considered to be materialized views defined by a language built on regular expressions. Using this perspective, we can provide an efficient verifier (using static analysis) that can be used to avoid the high cost of re-extracting information after an update. In particular, we propose an efficient mechanism to identify updates for which we can autonomously compute an extracted relation. We present experimental results that support the feasibility and practicality of this mechanism in real world extraction systems.

Information extraction programs (extractors) can be applied to documents to isolate structured versions of some content, that is, to create tabular records corresponding to facts found in the documents. If the data in an extracted table needs to be updated for any reason (for example, as a result of data cleaning), the source document will no longer be synchronized with the data. But documents are the principal medium for sharing information among humans. We therefore wish to ensure that changes to extracted tables are reflected correctly in their source documents.