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In this paper we present JUDIE (Joint Unsupervised Structure Discovery and Information Extraction), a new method for automatically extracting semi-structured data records in the form of continuous text (e.g., bibliographic citations, postal addresses, classified ads, etc.) and having no explicit delimiters between them. While in state-of-the-art Information Extraction methods the structure of the data records is manually supplied the by user as a training step, JUDIE is capable of detecting the structure of each individual record being extracted without any user assistance. This is accomplished by a novel Structure Discovery algorithm that, given a sequence of labels representing attributes assigned to potential values, groups these labels into individual records by looking for frequent patterns of label repetitions among the given sequence. We also show how to integrate this algorithm in the information extraction process by means of successive refinement steps that alternate information extraction and structure discovery. Through an extensively experimental evaluation with different datasets in distinct domains, we compare JUDIE with state-of-the-art information extraction methods and conclude that, even without any user intervention, it is able to achieve high quality results on the tasks of discovering the structure of the records and extracting information from them.

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