59 citations · 184 across the 23 of their papers we have counts for
7 papers · 1 filter
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 potent…
The Unexpected Unexpected and the Expected Unexpected: How People's Conception of the Unexpected is Not That Unexpected
Molly S Quinn, Kathleen Campbell, Mark T Keane
The answers people give when asked to 'think of the unexpected' for everyday event scenarios appear to be more expected than unexpected. There are expected unexpected outcomes that…
Plotting Markson's 'Mistress'
Kelleher Conor, Mark T. Keane
The post-modern novel 'Wittgenstein's Mistress' by David Markson (1988) presents the reader with a very challenging non linear narrative, that itself appears to one of the novel's…
Helping News Editors Write Better Headlines: A Recommender to Improve the Keyword Contents & Shareability of News Headlines
Terrence Szymanski, Claudia Orellana-Rodriguez, Mark T. Keane
We present a software tool that employs state-of-the-art natural language processing (NLP) and machine learning techniques to help newspaper editors compose effective headlines for…
Identifying Metaphor Hierarchies in a Corpus Analysis of Finance Articles
Aaron Georw, Mark Keane
Using a corpus of over 17,000 financial news reports (involving over 10M words), we perform an analysis of the argument-distributions of the UP- and DOWN-verbs used to describe mov…
Mining the Web for the Voice of the Herd to Track Stock Market Bubbles
Aaron Gerow, Mark Keane
We show that power-law analyses of financial commentaries from newspaper web-sites can be used to identify stock market bubbles, supplementing traditional volatility analyses. Usin…