Industrial Memories: Exploring the Findings of Government Inquiries with Neural Word Embedding and Machine Learning

Abstract

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 visualization, present an interactive web-based platform that enables the exploration of the text in new ways to uncover new historical insights.

Curatr: A Platform for Semantic Analysis and Curation of Historical Literary Texts

“…By transforming textual data in this way, we can use the new representation to capture the semantic similarity between pairs or groups of words. Word embedding methods have been used in digital humanities research to generate semantic lexicons for a range of purposes including detecting language change over time [14], extracting social networks from literary texts [36], sentiment analysis [32], and semantic annotation [21]. An interactive strategy whereby a user incrementally creates a lexicon based on recommendations for similar words as recommended by a word embedding model has been demonstrated in a number of works [10,27].…”