NDLI: Bagging to find better expansion words

Content Provider	IEEE Xplore Digital Library
Author	Bingqing Wang Yaqian Zhou Xipeng Qiu Qi Zhang Xuanjing Huang
Copyright Year	2010
Description	Author affiliation: School of Computer Science, Fudan University, 220, Handan Road, Shanghai, China (Bingqing Wang; Yaqian Zhou; Xipeng Qiu; Qi Zhang; Xuanjing Huang)
Abstract	The supervised learning has been applied into the query expansion techniques, which trains a model to predict the “goodness” or “utility” of the expanded term to the retrieval system. There are many features to measure the relatedness between the expanded word and the query, which can be incorporated in the supervised learning to select the expanded terms. The training data set is generated automatically by a tricky method. However, this method can be affected by many aspects. A severe problem is that the distribution of the features is query-dependent, which has not been discussed in previous work. With a different distribution on the features, it is questionable to merge these training instances together and use the whole data set to train one single model. In this paper, we first investigate the statistical distribution of the auto-generated training data and show the problems in the training data set. Based on our analysis, we proposed to use the bagging method to ensemble several regression models in order to get a better supervised model to make prediction on the expanded terms. We conducted the experiments on the TREC benchmark test collections. Our analysis on the training data reveals some interesting phenomena about the query expansion techniques. The experiment results also show that the bagging approach can achieve the state-of-art retrieval performance on the standard TREC data set.
Starting Page	1
Ending Page	8
File Size	1113524
Page Count	8
File Format	PDF
ISBN	9781424468966
e-ISBN	9781424468997
DOI	10.1109/NLPKE.2010.5587826
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2010-08-21
Publisher Place	China
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Regression Learning Query Expansion Variable speed drives Bagging
Content Type	Text
Resource Type	Article

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1	Ministry of Education (GoI), Department of Higher Education	Sanctioning Authority	https://www.education.gov.in/ict-initiatives
2	Indian Institute of Technology Kharagpur	Host Institute of the Project: The host institute of the project is responsible for providing infrastructure support and hosting the project	https://www.iitkgp.ac.in
3	National Digital Library of India Office, Indian Institute of Technology Kharagpur	The administrative and infrastructural headquarters of the project	Dr. B. Sutradhar bsutra@ndl.gov.in
4	Project PI / Joint PI	Principal Investigator and Joint Principal Investigators of the project	Dr. B. Sutradhar bsutra@ndl.gov.in Prof. Saswat Chakrabarti will be added soon
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