DOCTORAL ABSTRACT

Incorporating Context within the Language ModelingApproach for ad hoc Information Retrieval
Leif Azzopardi
Using context to improve retrieval performance is a current challenge within the discipline and presents
a major challenge to the research community. In this thesis, the context of documents formed via semantic
associations is utilized within the Language Modeling (LM) approach for ad hoc Information Retrieval.
Whilst, the LM approach provides a natural and intuitive means of encoding such context, it also represents a change to the way probability theory is applied to the ranking of documents in ad hoc Information
Retrieval[5, 6, 2, 4]. This requires several assumptions to be engaged for its application. A consequence ofengaging these assumptions is a key implication that better retrieval performance can be obtained throughdeveloping better representations of the documents[6]. It is posited that the context associated with a document will enable the development of such representations - context based document models. This premiserelies upon the explicit and implicit assumptions of the Language Modeling approach being valid, which
have, up until now, not been fully tested or verified.
In the thesis, we (1) formalize the assumptions of the Language Modeling approach; (2) motivated by
the implications of these assumptions we present our framework for estimating context based document
models; (3) perform a comprehensive analysis of the main assumptions underlying the Language Modelingapproach, not only to validate the approach, but to deepen our understanding of the retrieval model itself;and (4) empirically assess the performance of the context based document models against the standard
document models on various test collections and contexts. Some of the semantic associations used to define
the context included using Probabilistic Latent Semantic Analysis (PLSA)[3]/Latent Dirichlet Allocation(LDA)[1], web links between documents, and interactions with the collection from topic tracking.
LMs and surmises three core assumptions for LM, which are later empirically validated.
An electronic copy of this thesis is available from: http://cis.strath.ac.uk/ leif/

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[3] T. Hofmann. Probabilistic latent semantic analysis. In Proceedings of the 15th Conference of Uncertainty in
Artificial Intelligence, UAI’99, pages 289–296, Stockholm, Sweden, 1999.
[4] D. R. H. Miller, T. Leek, and R. M. Schwartz. A hidden markov model information retrieval. In 22nd Annual International ACM SIGIR conference on Research and development in information retrieval, pages 214–221, California,US, 1999. ACM Press.
[5] J. M. Ponte. A Language Modeling Approach to Information Retrieval. PhD thesis, University of Massachusetts
Amherst, 1998.
[6] J. M. Ponte and W. B. Croft. A language modeling approach to information retrieval. In Proceedings of the Twenty
First ACM-SIGIR, pages 275–281, Melbourne, Australia, 1998. ACM Press.

ACM SIGIR Forum70

Vol.40 No. 1 June 2006