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Extracting Structured Data from Natural Language Documents with Island Parsing
Alberto Bacchelli, Anthony Cleve, Michele Lanza, and Andrea Mocci
(University of Lugano, Switzerland; University of Namur, Belgium; Politecnico di Milano, Italy)
The design and evolution of a software system leave traces in various kinds of artifacts. In software, produced by humans for humans, many artifacts are written in natural language by people involved in the project. Such entities contain structured information which constitute a valuable source of knowledge for analyzing and comprehending a system's design and evolution. However, the ambiguous and informal nature of narrative is a serious challenge in gathering such information, which is scattered throughout natural language text. We present an approach-based on island parsing-to recognize and enable the parsing of structured information that occur in natural language artifacts. We evaluate our approach by applying it to mailing lists pertaining to three software systems. We show that this approach allows us to extract structured data from emails with high precision and recall.