Linear discriminant model for information retrieval

This paper presents a new discriminative model for information retrieval (IR), referred to as linear discriminant model (LDM), which provides a flexible framework to incorporate arbitrary features. LDM is different from most existing models in that it takes into account a variety of linguistic features that are derived from the component models of HMM that is widely used in language modeling approaches to IR. Therefore, LDM is a means of melding discriminative and generative models for IR. We present two algorithms of parameter learning for LDM. One is to optimize the average precision (AP) directly using an iterative procedure. The other is a perceptron-based algorithm that minimizes the number of discordant document-pairs in a rank list. The effectiveness of our approach has been evaluated on the task of ad hoc retrieval using six English and Chinese TREC test sets. Results show that (1) in most test sets, LDM significantly outperforms the state-ofthe-art language modeling approache...

• Type: Conference
• Year: 2005
• Where: SIGIR
• Authors: Jianfeng Gao, Haoliang Qi, Xinsong Xia, Jian-Yun Nie
• Keywords: Language Modeling Approaches, Linguistic Features, Retrieval Models, SIGIR 2005