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Efficiently Summarising Event Sequences with Rich Interleaving Patterns
Discovering the key structure of a database is one of the main goals of ...
01/27/2017 ∙ by Apratim Bhattacharyya, et al. ∙
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Mining Periodic Patterns with a MDL Criterion
The quantity of event logs available is increasing rapidly, be they prod...
07/04/2018 ∙ by Esther Galbrun, et al. ∙
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Extracting Semantic Process Information from the Natural Language in Event Logs
Process mining focuses on the analysis of recorded event data in order t...
03/06/2021 ∙ by Adrian Rebmann, et al. ∙
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Neural language representations predict outcomes of scientific research
Many research fields codify their findings in standard formats, often by...
05/17/2018 ∙ by James P. Bagrow, et al. ∙
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The emotional arcs of stories are dominated by six basic shapes
Advances in computing power, natural language processing, and digitizati...
06/24/2016 ∙ by Andrew J. Reagan, et al. ∙
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On the Winograd Schema Challenge: Levels of Language Understanding and the Phenomenon of the Missing Text
The Winograd Schema (WS) challenge has been proposed as an alternative t...
09/30/2018 ∙ by Walid S. Saba, et al. ∙
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Mining and discovering biographical information in Difangzhi with a language-model-based approach
We present results of expanding the contents of the China Biographical D...
04/08/2015 ∙ by Peter K. Bol, et al. ∙
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LonelyText: A Short Messaging Based Classification of Loneliness
01/22/2021 ∙ by Mawulolo K. Ameko, et al. ∙
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Loneliness does not only have emotional implications on a person but also on his/her well-being. The study of loneliness has been challenging and largely inconclusive in findings because of the several factors that might correlate to the phenomenon. We present one approach to predicting this event by discovering patterns of language associated with loneliness. Our results show insights and promising directions for mining text from instant messaging to predict loneliness.
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