Publication Type

Conference Proceeding Article

Version

publishedVersion

Publication Date

8-2012

Abstract

Conventional classification methods tend to focus on features of individual objects, while missing out on potentially valuable pairwise features that capture the relationships between objects. Although recent developments on graph regularization exploit this aspect, existing works generally assume only a single kind of pairwise feature, which is often insufficient. We observe that multiple, heterogeneous pairwise features can often complement each other and are generally more robust in modeling the relationships between objects. Furthermore, as some objects are easier to classify than others, objects with higher initial classification confidence should be weighed more towards classifying related but more ambiguous objects, an observation missing from previous graph regularization techniques. In this paper, we propose a Dirichlet-based regularization framework that supports the combination of heterogeneous pairwise features with confidence-aware prediction using limited labeled training data. Next, we showcase a few applications of our framework in information retrieval, focusing on the problem of query intent classification. Finally, we demonstrate through a series of experiments the advantages of our framework on a large-scale real-world dataset.

Keywords

confidence, applications in information retrieval, pairwise features, graph regularization, query intent classification

Discipline

Databases and Information Systems

Research Areas

Data Science and Engineering

Publication

SIGIR '12: Proceedings of the 35th International ACM SIGIR Conference on Research and Development in Information Retrieval: Portland, Oregon, August 12-16

First Page

951

Last Page

960

ISBN

9781450316583

Identifier

10.1145/2348283.2348410

Publisher

ACM

City or Country

New York

Additional URL

https://doi.org/10.1145/2348283.2348410

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