NDLI: Linear discriminant model for information retrieval

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Linear discriminant model for information retrieval

Content Provider	ACM Digital Library
Author	Qi, Haoliang Gao, Jianfeng Xia, Xinsong Nie, Jian-Yun
Abstract	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-of-the-art language modeling approaches and the classical probabilistic retrieval model; (2) it is more appropriate to train LDM using a measure of AP rather than likelihood if the IR system is graded on AP; and (3) linguistic features (e.g. phrases and dependences) are effective for IR if they are incorporated properly.
Starting Page	290
Ending Page	297
Page Count	8
File Format	PDF
ISBN	1595930345
DOI	10.1145/1076034.1076085
Language	English
Publisher	Association for Computing Machinery (ACM)
Publisher Date	2005-08-15
Publisher Place	New York
Access Restriction	Subscribed
Subject Keyword	Perceptron Language model Discriminative training Hidden markov model Optimization
Content Type	Text
Resource Type	Article

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