Abstract
Acquiring relevant business concepts is a crucial first step for any software project for which the software experts are not domain experts. The wealth of information buried within an organization's written documentation is a precious source of concepts, relationships and attributes which can be used to model the enterprise's domain. The lack of targeted extraction tools can make perusing through this type of resource a lengthy and costly process. We propose a domain model focused extraction process aimed at the rapid discovery of knowledge relevant to the software expert. To avoid undesirable noise from high-level linguistic tools, the process is mainly composed of positive and negative base filters that are less error prone and more robust. The extracted candidates are then reordered using a weight propagation algorithm based on structural hints from source documents. When tested on French text corpora from public organizations, our process performs 2.7 times better than a statistical baseline for relevant concept discovery. A new metric to assess the performance discovery speed of relevant concepts is introduced. The annotation of a gold standard definition of software engineering oriented concepts for knowledge extraction tasks is also presented.
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