A General Information Extraction Framework Based on Formal Languages

For a terminal alphabet $\mathrm{\Sigma }$ and an attribute alphabet $\mathrm{\Gamma }$, a $(\mathrm{\Sigma },\mathrm{\Gamma })$-extractor is a function that maps every string over $\mathrm{\Sigma }$ to a table with a column per attribute and with sets of positions of $w$ as cell entries. This rather general information extraction framework extends the well-known document spanner framework, which has intensively been investigated in the database theory community over the last decade. Moreover, our framework is based on formal language theory in a particularly clean and simple way. In addition to this conceptual contribution, we investigate closure properties, different representation formalisms and the complexity of natural decision problems for extractors.