Discriminative models have been preferred over generative models in many machine learning problems in the recent past owing to some of their attractive theoretical properties. In this paper, we explore the applicability of discriminative classifiers for IR. We have compared the performance of two popular discriminative models, namely the maximum entropy model and support vector machines with that of language modeling, the state-of-the-art generative model for IR. Our experiments on ad-hoc retrieval indicate that although maximum entropy is significantly worse than language models, support vector machines are on par with language models. We argue that the main reason to prefer SVMs over language models is their ability to learn arbitrary features automatically as demonstrated by our experiments on the home-page finding task of TREC-10.
More recently, Nallapati REF compared the performance of the maximum entropy model and support vector machines with that of language modeling in ad hoc retrieval and homepage finding, and argued that SVMs are preferred over language models because of their ability to learn arbitrary features automatically.
Discriminative models for information retrieval
SIGIR '04
Computer Science