  Procedures are an important knowledge component of documents that can be
leveraged by cognitive assistants for automation, question-answering or driving
a conversation. It is a challenging problem to parse big dense documents like
product manuals, user guides to automatically understand which parts are
talking about procedures and subsequently extract them. Most of the existing
research has focused on extracting flows in given procedures or understanding
the procedures in order to answer conceptual questions. Identifying and
extracting multiple procedures automatically from documents of diverse formats
remains a relatively less addressed problem. In this work, we cover some of
this ground by -- 1) Providing insights on how structural and linguistic
properties of documents can be grouped to define types of procedures, 2)
Analyzing documents to extract the relevant linguistic and structural
properties, and 3) Formulating procedure identification as a classification
problem that leverages the features of the document derived from the above
analysis. We first implemented and deployed unsupervised techniques which were
used in different use cases. Based on the evaluation in different use cases, we
figured out the weaknesses of the unsupervised approach. We then designed an
improved version which was supervised. We demonstrate that our technique is
effective in identifying procedures from big and complex documents alike by
achieving accuracy of 89%.
