Cognitive Temporal Document Priors

Temporal information retrieval exploits temporal features of document collections and queries. Temporal document priors are used to adjust the score of a document based on its publication time. We consider a class of temporal document priors that is inspired by retention functions considered in cognitive psychology that are used to model the decay of memory. Many such functions used as a temporal document prior have a positive effect on overall retrieval performance. We examine the stability of this effect across news and microblog collections and discover interesting differences between retention functions. We also study the problem of optimizing parameters of the retention functions as temporal document priors; some retention functions display consistent good performance across large regions of the parameter space. A retention function based on a Weibull distribution is the preferred choice for a temporal document prior.

[1]  M. de Rijke,et al.  Incorporating Query Expansion and Quality Indicators in Searching Microblog Posts , 2011, ECIR.

[2]  D. Rubin,et al.  The Precise Time Course of Retention , 1999 .

[3]  Antonio G. Chessa,et al.  A memory model for internet hits after media exposure , 2004 .

[4]  M. Meeter,et al.  Remembering the news: Modeling retention data from a study with 14,000 participants , 2005, Memory & cognition.

[5]  Miles Efron,et al.  Estimation methods for ranking recent information , 2011, SIGIR.

[6]  Christopher D. Manning,et al.  Introduction to Information Retrieval , 2010, J. Assoc. Inf. Sci. Technol..

[7]  M. de Rijke,et al.  Adaptive Temporal Query Modeling , 2012, ECIR.

[8]  C. Izawa,et al.  Conjuring a Work from the Dream Time of Cognitive Psychology@@@On Human Memory: Evolution, Progress, and Reflections on the 30th Anniversary of the Atkinson-Shiffrin Model , 2000 .

[9]  Scott D. Brown,et al.  The power law repealed: The case for an exponential law of practice , 2000, Psychonomic bulletin & review.

[10]  Katrina Fenlon,et al.  Improving retrieval of short texts through document expansion , 2012, SIGIR '12.

[11]  M. de Rijke,et al.  Semantic Document Selection - Historical Research on Collections That Span Multiple Centuries , 2012, TPDL.

[12]  Luis Gravano,et al.  Answering General Time-Sensitive Queries , 2008, IEEE Transactions on Knowledge and Data Engineering.

[13]  Stefan M. Herzog,et al.  Fluency heuristic: a model of how the mind exploits a by-product of information retrieval. , 2008, Journal of experimental psychology. Learning, memory, and cognition.

[14]  Hermann Ebbinghaus (1885) Memory: A Contribution to Experimental Psychology , 2013, Annals of Neurosciences.

[15]  James Pustejovsky,et al.  Temporal Processing with the TARSQI Toolkit , 2008, COLING.

[16]  Mostafa Keikha,et al.  Time-based relevance models , 2011, SIGIR.

[17]  Antonio G. Chessa,et al.  Modelling memory processes and Internet response times: Weibull or power-law? , 2006 .

[18]  Katharine Krause Shobe,et al.  Is Traumatic Memory Special? , 1997 .

[19]  Michael Gertz,et al.  Temporal Information Retrieval: Challenges and Opportunities , 2011, TWAW.

[20]  Stephen Porter,et al.  Is traumatic memory special ? A comparison of traumatic memory characteristics with memory for other emotional life experiences , 2001 .

[21]  Giorgio Gambosi,et al.  FUB, IASI-CNR, UNIVAQ at TREC 2011 Microblog Track , 2011, Text Retrieval Conference.

[22]  Hermann Ebbinghaus,et al.  Memory: a contribution to experimental psychology. , 1987, Annals of neurosciences.

[23]  W. Bruce Croft,et al.  Time-based language models , 2003, CIKM '03.

[24]  Giorgio Gambosi,et al.  On relevance, time and query expansion , 2011, CIKM '11.

[25]  Thomas D. Wickens Measuring the time course of retention. , 1999 .

[26]  John R. Anderson,et al.  The Role of Process in the Rational Analysis of Memory , 1997, Cognitive Psychology.