Industrial Memories: Exploring the Findings of Government Inquiries with Neural Word Embedding and Machine Learning
This work addresses the problem of making lengthy government inquiry documents accessible to the public, though it is incremental as it applies existing NLP methods to a new domain.
The researchers developed a text mining system using neural word embeddings and machine learning to explore findings from government inquiries, specifically applying it to Ireland's industrial schools inquiry to uncover historical insights through an interactive web platform.
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.