C4.5: programs for machine learning
C4.5: programs for machine learning
Journal of the American Society for Information Science
Data mining: practical machine learning tools and techniques with Java implementations
Data mining: practical machine learning tools and techniques with Java implementations
Technical Note: Naive Bayes for Regression
Machine Learning
High-performance, open-domain question answering from large text collections
High-performance, open-domain question answering from large text collections
Learning to find answers to questions on the Web
ACM Transactions on Internet Technology (TOIT)
Unsupervised question answering data acquisition from local corpora
Proceedings of the thirteenth ACM international conference on Information and knowledge management
Learning surface text patterns for a Question Answering system
ACL '02 Proceedings of the 40th Annual Meeting on Association for Computational Linguistics
An analysis of the AskMSR question-answering system
EMNLP '02 Proceedings of the ACL-02 conference on Empirical methods in natural language processing - Volume 10
Document retrieval in the context of question answering
ECIR'03 Proceedings of the 25th European conference on IR research
Part of Speech Based Term Weighting for Information Retrieval
ECIR '09 Proceedings of the 31th European Conference on IR Research on Advances in Information Retrieval
Aspect presence verification conditional on other aspects
Proceedings of the 33rd international ACM SIGIR conference on Research and development in information retrieval
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Question answering systems rely on retrieval components to identify documents that contain an answer to a user's question. The formulation of queries that are used for retrieving those documents has a strong impact on the effectiveness of the retrieval component. Here, we focus on predicting the importance of terms from the original question. We use model tree machine learning techniques in order to assign weights to query terms according to their usefulness for identifying documents that contain an answer. Incorporating the learned weights into a state-of-the-art retrieval system results in statistically significant improvements.