Exploiting redundancy in question answering
Proceedings of the 24th annual international ACM SIGIR conference on Research and development in information retrieval
On the MSE robustness of batching estimators
Proceedings of the 33nd conference on Winter simulation
ACL '94 Proceedings of the 32nd annual meeting on Association for Computational Linguistics
Using knowledge to facilitate factoid answer pinpointing
COLING '02 Proceedings of the 19th international conference on Computational linguistics - Volume 1
Learning surface text patterns for a Question Answering system
ACL '02 Proceedings of the 40th Annual Meeting on Association for Computational Linguistics
Learning semantic constraints for the automatic discovery of part-whole relations
NAACL '03 Proceedings of the 2003 Conference of the North American Chapter of the Association for Computational Linguistics on Human Language Technology - Volume 1
COGEX: a logic prover for question answering
NAACL '03 Proceedings of the 2003 Conference of the North American Chapter of the Association for Computational Linguistics on Human Language Technology - Volume 1
Automatic derivation of surface text patterns for a maximum entropy based question answering system
NAACL-Short '03 Proceedings of the 2003 Conference of the North American Chapter of the Association for Computational Linguistics on Human Language Technology: companion volume of the Proceedings of HLT-NAACL 2003--short papers - Volume 2
Offline strategies for online question answering: answering questions before they are asked
ACL '03 Proceedings of the 41st Annual Meeting on Association for Computational Linguistics - Volume 1
A noisy-channel approach to question answering
ACL '03 Proceedings of the 41st Annual Meeting on Association for Computational Linguistics - Volume 1
The structure and performance of an open-domain question answering system
ACL '00 Proceedings of the 38th Annual Meeting on Association for Computational Linguistics
A bootstrapping method for learning semantic lexicons using extraction pattern contexts
EMNLP '02 Proceedings of the ACL-02 conference on Empirical methods in natural language processing - Volume 10
Resource analysis for question answering
ACLdemo '04 Proceedings of the ACL 2004 on Interactive poster and demonstration sessions
Genetic algorithms for data-driven web question answering
Evolutionary Computation
Mining web snippets to answer list questions
AIDM '07 Proceedings of the 2nd international workshop on Integrating artificial intelligence and data mining - Volume 84
Model tree learning for query term weighting in question answering
ECIR'07 Proceedings of the 29th European conference on IR research
Intelligent answering location questions from the web using molecular alignment
Journal of Intelligent Information Systems
A survey on question answering technology from an information retrieval perspective
Information Sciences: an International Journal
Molecular sequence alignment for extracting answers for where-typed questions from google snippets
KES'06 Proceedings of the 10th international conference on Knowledge-Based Intelligent Information and Engineering Systems - Volume Part I
Machine learning for query formulation in question answering
Natural Language Engineering
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Data-driven approaches in question answering (QA) are increasingly common. Since availability of training data for such approaches is very limited, we propose an unsupervised algorithm that generates high quality question-answer pairs from local corpora. The algorithm is ontology independent, requiring very small seed data as its starting point. Two alternating views of the data make learning possible: 1) question types are viewed as relations between entities and 2) question types are described by their corresponding question-answer pairs. These two aspects of the data allow us to construct an unsupervised algorithm that acquires high precision question-answer pairs. We show the quality of the acquired data for different question types and perform a task-based evaluation. With each iteration, pairs acquired by the unsupervised algorithm are used as training data to a simple QA system. Performance increases with the number of question-answer pairs acquired confirming the robustness of the unsupervised algorithm. We introduce the notion of semantic drift and show that it is a desirable quality in training data for question answering systems.