Choosing the word most typical in context using a lexical co-occurrence network
ACL '98 Proceedings of the 35th Annual Meeting of the Association for Computational Linguistics and Eighth Conference of the European Chapter of the Association for Computational Linguistics
Proceedings of the 27th annual international ACM SIGIR conference on Research and development in information retrieval
Query expansion using random walk models
Proceedings of the 14th ACM international conference on Information and knowledge management
Improving the estimation of relevance models using large external corpora
SIGIR '06 Proceedings of the 29th annual international ACM SIGIR conference on Research and development in information retrieval
Exploiting underrepresented query aspects for automatic query expansion
Proceedings of the 13th ACM SIGKDD international conference on Knowledge discovery and data mining
Detecting Word Substitutions in Text
IEEE Transactions on Knowledge and Data Engineering
Model tree learning for query term weighting in question answering
ECIR'07 Proceedings of the 29th European conference on IR research
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I have shown that the presence of difficult query aspects that are revealed only implicitly (e.g. exploration, opposition, achievements, cooperation, risks) can be improved by taking advantage of the known presence of other, easier to verify query aspects. The approach proceeds by mining a large external corpus and results in substantial improvements in re-ranking the subset of the top retrieved documents.