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| Content Provider | Springer Nature Link |
|---|---|
| Author | Ménard, Pierre André Ratté, Sylvie |
| Copyright Year | 2015 |
| 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. |
| Starting Page | 649 |
| Ending Page | 686 |
| Page Count | 38 |
| File Format | |
| ISSN | 09288910 |
| Journal | Automated Software Engineering |
| Volume Number | 23 |
| Issue Number | 4 |
| e-ISSN | 15737535 |
| Language | English |
| Publisher | Springer US |
| Publisher Date | 2015-08-21 |
| Publisher Place | New York |
| Access Restriction | One Nation One Subscription (ONOS) |
| Subject Keyword | Automated extraction Conceptual model Domain model Relevance evaluation Software project Knowledge modeling Artificial Intelligence (incl. Robotics) Software Engineering/Programming and Operating Systems |
| Content Type | Text |
| Resource Type | Article |
| Subject | Software |
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