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On the Place of Text Data in Lifelogs, and Text Analysis via Semantic Facets

Gregory Grefenstette 1 Lawrence Muchemi 1
1 TAO - Machine Learning and Optimisation
CNRS - Centre National de la Recherche Scientifique : UMR8623, Inria Saclay - Ile de France, UP11 - Université Paris-Sud - Paris 11, LRI - Laboratoire de Recherche en Informatique
Abstract : Current research in lifelog data has not paid enough attention to analysis of cognitive activities in comparison to physical activities. We argue that as we look into the future, wearable devices are going to be cheaper and more prevalent and textual data will play a more significant role. Data captured by lifelogging devices will increasingly include speech and text, potentially useful in analysis of intellectual activities. Analyzing what a person hears, reads, and sees, we should be able to measure the extent of cognitive activity devoted to a certain topic or subject by a learner. Test-based lifelog records can benefit from semantic analysis tools developed for natural language processing. We show how semantic analysis of such text data can be achieved through the use of taxonomic subject facets and how these facets might be useful in quantifying cognitive activity devoted to various topics in a person's day. We are currently developing a method to automatically create taxonomic topic vocabularies that can be applied to this detection of intellectual activity.
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Submitted on : Wednesday, June 8, 2016 - 9:56:18 AM
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  • HAL Id : hal-01328473, version 1
  • ARXIV : 1606.02440

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Gregory Grefenstette, Lawrence Muchemi. On the Place of Text Data in Lifelogs, and Text Analysis via Semantic Facets. iConference 2016 SIE on Lifelogging, Mar 2016, Philadelphia, United States. ⟨hal-01328473⟩

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