Retrieving Potential Causes from a Query Event: SIGIR 2020 Pre-recorded Presentation - CrossMinds.ai
Retrieving Potential Causes from a Query Event: SIGIR 2020 Pre-recorded Presentation
Aug 04, 202014 views
Debasis Ganguly
Authors:
Suchana Datta, University College Dublin
 suchana.datta@ucdconnect.ie

Debasis Ganguly, 
IBM Research Europe, Dublin, Ireland
 debasis.ganguly1@ie.ibm.com

Dwaipayan Roy
 GESIS, Cologne
 dwaipayan.roy@gesis.org

Francesca Bonin 
IBM Research Europe, Dublin, Ireland
 fbonin@ie.ibm.com

Charles Jochim 
IBM Research Europe, Dublin, Ireland 
charlesj@ie.ibm.com

Mandar Mitra
, Indian Statistical Institute, Kolkata
 mandar@isical.ac.in

Abstract: Different to traditional IR, which retrieves a set of topically relevant
documents given a user query, we investigate causal retrieval, which
involves retrieving a set of documents that describe a set of potential
causes leading to an effect specified in the query. We argue that the
nature of causal relevance should be different to that of traditional
topical relevance. This is because although the causally relevant
documents would have partial term overlap with the ones that are
topically relevant for a query, yet it is expected that a majority
of these documents would use a different set of terms to describe
a number of causes possibly leading to their effects. To address
this, we propose a feedback model to estimate a distribution of
terms which are relatively infrequent but associated with high
weights in the topically relevant distribution, leading to potential
causal relevance. Our experiments demonstrate that such a feedback
model turns out to be substantially more effective than traditional
IR models and a number of other causality heuristic baselines.
SIGIR 2020
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