Class-based n-gram models of natural language
Computational Linguistics
Discovery of inference rules for question-answering
Natural Language Engineering
Experiments with open-domain textual Question Answering
COLING '00 Proceedings of the 18th conference on Computational linguistics - Volume 1
A hybrid Japanese parser with hand-crafted grammar and statistics
COLING '00 Proceedings of the 18th conference on Computational linguistics - Volume 1
Inducing a semantically annotated lexicon via EM-based clustering
ACL '99 Proceedings of the 37th annual meeting of the Association for Computational Linguistics on Computational Linguistics
Paraphrasing rules for automatic evaluation of translation into Japanese
PARAPHRASE '03 Proceedings of the second international workshop on Paraphrasing - Volume 16
Using hidden Markov random fields to combine distributional and pattern-based word clustering
COLING '08 Proceedings of the 22nd International Conference on Computational Linguistics - Volume 1
Classifying Japanese polysemous verbs based on fuzzy C-means clustering
TextGraphs-4 Proceedings of the 2009 Workshop on Graph-based Methods for Natural Language Processing
Detection of incorrect case assignments in paraphrase generation
IJCNLP'04 Proceedings of the First international joint conference on Natural Language Processing
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This paper describes an unsupervised learning method for associative relationships between verb phrases, which is important in developing reliable Q&A systems. Consider the situation that a user gives a query "How much petrol was imported by Japan from Saudi Arabia?" to a Q&A system, but the text given to the system includes only the description "X tonnes of petrol was conveyed to Japan from Saudi Arabia." We think that the description is a good clue to find the answer for our query, "X tonnes." But there is no large-scale database that provides the associative relationship between "imported" and "conveyed." Our aim is to develop an unsupervised learning method that can obtain such an associative relationship, which we call scenario consistency. The method we are currently working on uses an expectation-maximization (EM) based word-clustering algorithm, and we have evaluated the effectiveness of this method using Japanese verb phrases.