Industrial Memories: Exploring the Findings of Government Inquiries with
  Neural Word Embedding and Machine Learning
Susan Leavy Emilie Pine Mark T Keane
We present a text mining system to support the exploration of large volumes
of text detailing the findings of government inquiries. Despite their
historical significance and potential societal impact, key findings of
inquiries are often hidden within lengthy documents and remain inaccessible to
the general public. We transform the findings of the Irish government's inquiry
into industrial schools and through the use of word embedding, text
classification and visualisation, present an interactive web-based platform
that enables the exploration of the text to uncover new historical insights.