Incremental relevance feedback for information filtering
SIGIR '96 Proceedings of the 19th annual international ACM SIGIR conference on Research and development in information retrieval
Modern Information Retrieval
Quantitative evaluation of passage retrieval algorithms for question answering
Proceedings of the 26th annual international ACM SIGIR conference on Research and development in informaion retrieval
Building a reusable test collection for question answering
Journal of the American Society for Information Science and Technology - Research Articles
Discretization based learning approach to information retrieval
HLT '05 Proceedings of the conference on Human Language Technology and Empirical Methods in Natural Language Processing
Terrier information retrieval platform
ECIR'05 Proceedings of the 27th European conference on Advances in Information Retrieval Research
Evolutionary optimization for ranking how-to questions based on user-generated contents
Expert Systems with Applications: An International Journal
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Automated answering of natural language questions is an interesting and useful problem to solve. Question answering (QA) systems often perform information retrieval at an initial stage. Information retrieval (IR) performance, provided by engines such as Lucene, places a bound on overall system performance. For example, no answer bearing documents are retrieved at low ranks for almost 40% of questions. In this paper, answer texts from previous QA evaluations held as part of the Text REtrieval Conferences (TREC) are paired with queries and analysed in an attempt to identify performance-enhancing words. These words are then used to evaluate the performance of a query expansion method. Data driven extension words were found to help in over 70% of difficult questions. These words can be used to improve and evaluate query expansion methods. Simple blind relevance feedback (RF) was correctly predicted as unlikely to help overall performance, and an possible explanation is provided for its low value in IR for QA.