Abstract
The discovery of process information in natural language documents is hindered by the innate ambiguity of natural language. This issue is even more challenging when process information is spread throughout several documents, each with the possibility of different formats, writers, and original purposes, that must be read and interpreted individually by a business analyst before their information can be uncovered. This work presents a semi-automated approach to process discovery from multiple documents simultaneously, making use of semantic similarity measures and natural language processing techniques to identify, gather, and extract process information from these documents. An experiment is conducted with this approach to demonstrate its use and results.
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