  Assessing risk for voluminous legal documents such as request for proposal;
contracts is tedious and error prone. We have developed "risk-o-meter", a
framework, based on machine learning and natural language processing to review
and assess risks of any legal document. Our framework uses Paragraph Vector, an
unsupervised model to generate vector representation of text. This enables the
framework to learn contextual relations of legal terms and generate sensible
context aware embedding. The framework then feeds the vector space into a
supervised classification algorithm to predict whether a paragraph belongs to a
per-defined risk category or not. The framework thus extracts risk prone
paragraphs. This technique efficiently overcomes the limitations of
keyword-based search. We have achieved an accuracy of 91% for the risk category
having the largest training dataset. This framework will help organizations
optimize effort to identify risk from large document base with minimal human
intervention and thus will help to have risk mitigated sustainable growth. Its
machine learning capability makes it scalable to uncover relevant information
from any type of document apart from legal documents, provided the library is
per-populated and rich.
