Improving Automatic Query Classification via Semi-Supervised Learning
ICDM '05 Proceedings of the Fifth IEEE International Conference on Data Mining
Building bridges for web query classification
SIGIR '06 Proceedings of the 29th annual international ACM SIGIR conference on Research and development in information retrieval
Manifold Regularization: A Geometric Framework for Learning from Labeled and Unlabeled Examples
The Journal of Machine Learning Research
Robust classification of rare queries using web knowledge
SIGIR '07 Proceedings of the 30th annual international ACM SIGIR conference on Research and development in information retrieval
Random walks on the click graph
SIGIR '07 Proceedings of the 30th annual international ACM SIGIR conference on Research and development in information retrieval
Regularizing query-based retrieval scores
Information Retrieval
Regularized query classification using search click information
Pattern Recognition
Learning query intent from regularized click graphs
Proceedings of the 31st annual international ACM SIGIR conference on Research and development in information retrieval
Improving relevance feedback in language modeling with score regularization
Proceedings of the 31st annual international ACM SIGIR conference on Research and development in information retrieval
Context-aware query classification
Proceedings of the 32nd international ACM SIGIR conference on Research and development in information retrieval
Extracting structured information from user queries with semi-supervised conditional random fields
Proceedings of the 32nd international ACM SIGIR conference on Research and development in information retrieval
Proceedings of the 18th ACM conference on Information and knowledge management
Learning search tasks in queries and web pages via graph regularization
Proceedings of the 34th international ACM SIGIR conference on Research and development in Information Retrieval
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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.