Question-answering by predictive annotation
SIGIR '00 Proceedings of the 23rd annual international ACM SIGIR conference on Research and development in information retrieval
Rank aggregation methods for the Web
Proceedings of the 10th international conference on World Wide Web
Proceedings of the 24th annual international ACM SIGIR conference on Research and development in information retrieval
Cumulated gain-based evaluation of IR techniques
ACM Transactions on Information Systems (TOIS)
Optimizing search engines using clickthrough data
Proceedings of the eighth ACM SIGKDD international conference on Knowledge discovery and data mining
An efficient boosting algorithm for combining preferences
The Journal of Machine Learning Research
Discriminative models for information retrieval
Proceedings of the 27th annual international ACM SIGIR conference on Research and development in information retrieval
SVM answer selection for open-domain question answering
COLING '02 Proceedings of the 19th international conference on Computational linguistics - Volume 1
Learning to rank using gradient descent
ICML '05 Proceedings of the 22nd international conference on Machine learning
High accuracy retrieval with multiple nested ranker
SIGIR '06 Proceedings of the 29th annual international ACM SIGIR conference on Research and development in information retrieval
Linear feature-based models for information retrieval
Information Retrieval
Learning to rank: from pairwise approach to listwise approach
Proceedings of the 24th international conference on Machine learning
A support vector method for optimizing average precision
SIGIR '07 Proceedings of the 30th annual international ACM SIGIR conference on Research and development in information retrieval
FRank: a ranking method with fidelity loss
SIGIR '07 Proceedings of the 30th annual international ACM SIGIR conference on Research and development in information retrieval
AdaRank: a boosting algorithm for information retrieval
SIGIR '07 Proceedings of the 30th annual international ACM SIGIR conference on Research and development in information retrieval
The WEKA data mining software: an update
ACM SIGKDD Explorations Newsletter
Rank learning for factoid question answering with linguistic and semantic constraints
CIKM '10 Proceedings of the 19th ACM international conference on Information and knowledge management
Learning to rank for why-question answering
Information Retrieval
Learning to rank answers to non-factoid questions from web collections
Computational Linguistics
A framework for merging and ranking of answers in DeepQA
IBM Journal of Research and Development
A phased ranking model for question answering
Proceedings of the 22nd ACM international conference on Conference on information & knowledge management
Building structures from classifiers for passage reranking
Proceedings of the 22nd ACM international conference on Conference on information & knowledge management
| Hi-index | 0.00 |
This paper aims to solve the problem of improving the ranking of answer candidates for factoid based questions in a state-of-the-art Question Answering system. We first provide an extensive comparison of 5 ranking algorithms on two datasets -- from the Jeopardy quiz show and a medical domain. We then show the effectiveness of a cascading approach, where the ranking produced by one ranker is used as input to the next stage. The cascading approach shows sizeable gains on both datasets. We finally evaluate several rank aggregation techniques to combine these algorithms, and find that Supervised Kemeny aggregation is a robust technique that always beats the baseline ranking approach used by Watson for the Jeopardy competition. We further corroborate our results on TREC Question Answering datasets.