Query expansion using local and global document analysis
SIGIR '96 Proceedings of the 19th annual international ACM SIGIR conference on Research and development in information retrieval
Phrasal translation and query expansion techniques for cross-language information retrieval
Proceedings of the 20th annual international ACM SIGIR conference on Research and development in information retrieval
Experiments in Japanese text retrieval and routing using the NEAT system
Proceedings of the 21st annual international ACM SIGIR conference on Research and development in information retrieval
MT-based Japanese-Enlish cross-language IR experiments using the TREC test collections
IRAL '00 Proceedings of the fifth international workshop on on Information retrieval with Asian languages
Flexible pseudo-relevance feedback using optimization tables
Proceedings of the 24th annual international ACM SIGIR conference on Research and development in information retrieval
Flexible pseudo-relevance feedback via selective sampling
ACM Transactions on Asian Language Information Processing (TALIP)
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Local feedback for ad hoc retrieval typically hurts performance for about one-third of the search requests while improving the average performance. Our objective is to make it more reliable by estimating the optimal number of assumed-relevant documents and the optimal number of expansion terms for each request. We examine some simple optimization methods based on: the number of case particles in the request; the number of initial search terms; the highest document score in the initial ranked output; and the document score curves. Unfortunately, our first results using the BMIR-J2 and IREX Japanese test collections are negative. We are currently exploring some modified strategies for solving the optimization problems.