Séminaires RALI-OLST

Modelling term associations for information retrieval over large-scale text collections

Jimmy Huang (jhuang <at> yorku (point) ca)

York University

Monday 30 November 2015 at 2:30 PM  — Jour et heure inhabituels

Salle 3195, Pavillon André-Aisenstadt


Traditionally, in many probabilistic retrieval models, query terms are assumed to be independent. Although such models can achieve reasonably good performance, associations can exist among terms from human being.s point of view. There are some recent studies that investigate how to model term associations/dependencies by proximity measures. However, the modeling of term associations theoretically under the probabilistic retrieval framework is still largely unexplored. In this talk, I will introduce a new concept named Cross Term, to model term proximity, with the aim of boosting retrieval performance. With Cross Terms, the association of multiple query terms can be modeled in the same way as a simple unigram term. In particular, an occurrence of a query term is assumed to have an impact on its neighboring text. The degree of the query term impact gradually weakens with increasing distance from the place of occurrence. We use shape functions to characterize such impacts. Based on this assumption, we first propose a bigram CRoss TErm Retrieval (CRTER2) model as the basis model, and then recursively propose a generalized n-gram CRoss TErm Retrieval (CRTERn) model for n query terms where n > 2. Specifically, a bigram Cross Term occurs when the corresponding query terms appear close to each other, and its impact can be modeled by the intersection of the respective shape functions of the query terms. For n-gram Cross Term, we develop several distance metrics with different properties and employ them in the proposed models for ranking. We also show how to extend the language model using the newly proposed cross terms. Extensive experiments on a number of large-scale collections demonstrate the effectiveness of our proposed models.



Join with Zoom at this url.
Meeting ID: 916 9097 5818, 𝑷𝒂𝒔𝒔𝒄𝒐𝒅𝒆: 343273.
Phone numbers: https://umontreal.zoom.us/u/abitNZzLg.
One tap mobile: +14388097799,,91690975818#,,,,,,0#,,343273#


To receive weekly talk announcements, please send an e-mail to majordomo@iro.umontreal.ca. Simply write a message containing the single line 'subscribe ralli' (without the quotes).

See all the weekly talks for the year:

1991 1992 1993 1994 1995 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015 2016 2017 2018 2019 2020 2021