A grammatical approach to understanding textual tables using two-dimensional SCFGs

Abstract

We present an elegant and extensible model that is capable of providing semantic interpretations for an unusually wide range of textual tables in documents. Unlike the few existing table analysis models, which largely rely on relatively ad hoc heuristics, our linguistically-oriented approach is systematic and grammar based, which allows our model (1) to be concise and yet (2) recognize a wider range of data models than others, and (3) disambiguate to a significantly finer extent the underlying semantic interpretation of the table in terms of data models drawn from relation database theory. To accomplish this, the model introduces Viterbi parsing under two-dimensional stochastic CFGs. The cleaner grammatical approach facilitates not only greater coverage, but also grammar extension and maintenance, as well as a more direct and declarative link to semantic interpretation, for which we also introduce a new, cleaner data model. In disambiguation experiments on recognizing relevant data models of unseen web tables from different domains, a blind evaluation of the model showed 60% precision and 80% recall.