NDLI: A study of methods for negative relevance feedback

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A study of methods for negative relevance feedback

Content Provider	ACM Digital Library
Author	Wang, Xuanhui Zhai, ChengXiang Fang, Hui
Abstract	Negative relevance feedback is a special case of relevance feedback where we do not have any positive example; this often happens when the topic is difficult and the search results are poor. Although in principle any standard relevance feedback technique can be applied to negative relevance feedback, it may not perform well due to the lack of positive examples. In this paper, we conduct a systematic study of methods for negative relevance feedback. We compare a set of representative negative feedback methods, covering vector-space models and language models, as well as several special heuristics for negative feedback. Evaluating negative feedback methods requires a test set with sufficient difficult topics, but there are not many naturally difficult topics in the existing test collections. We use two sampling strategies to adapt a test collection with easy topics to evaluate negative feedback. Experiment results on several TREC collections show that language model based negative feedback methods are generally more effective than those based on vector-space models, and using multiple negative models is an effective heuristic for negative feedback. Our results also show that it is feasible to adapt test collections with easy topics for evaluating negative feedback methods through sampling.
Starting Page	219
Ending Page	226
Page Count	8
File Format	PDF
ISBN	9781605581644
DOI	10.1145/1390334.1390374
Language	English
Publisher	Association for Computing Machinery (ACM)
Publisher Date	2008-07-20
Publisher Place	New York
Access Restriction	Subscribed
Subject Keyword	Negative feedback Difficult topics Vector space models Language models
Content Type	Text
Resource Type	Article

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