Automatic acquisition of hyponyms from large text corpora
COLING '92 Proceedings of the 14th conference on Computational linguistics - Volume 2
Finding parts in very large corpora
ACL '99 Proceedings of the 37th annual meeting of the Association for Computational Linguistics on Computational Linguistics
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ACL '02 Proceedings of the 40th Annual Meeting on Association for Computational Linguistics
Kernel methods for relation extraction
EMNLP '02 Proceedings of the ACL-02 conference on Empirical methods in natural language processing - Volume 10
Automatic Discovery of Part-Whole Relations
Computational Linguistics
Espresso: leveraging generic patterns for automatically harvesting semantic relations
ACL-44 Proceedings of the 21st International Conference on Computational Linguistics and the 44th annual meeting of the Association for Computational Linguistics
HLT-NAACL '06 Proceedings of the main conference on Human Language Technology Conference of the North American Chapter of the Association of Computational Linguistics
Automatic construction of polarity-tagged corpus from HTML documents
COLING-ACL '06 Proceedings of the COLING/ACL on Main conference poster sessions
Extracting Advantage Phrases That Hint at a New Technology's Potentials
PAKM '08 Proceedings of the 7th International Conference on Practical Aspects of Knowledge Management
A web service for automatic word class acquisition
Proceedings of the 3rd International Universal Communication Symposium
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ACLDemos '10 Proceedings of the ACL 2010 System Demonstrations
Extracting concerns and reports on crimes in blogs
AMT'10 Proceedings of the 6th international conference on Active media technology
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This paper presents a method for mining potential troubles or obstacles related to the use of a given object. Some example instances of this relation are (medicine, side effect) and (amusement park, height restriction). Our acquisition method consists of three steps. First, we use an un-supervised method to collect training samples from Web documents. Second, a set of expressions generally referring to troubles is acquired by a supervised learning method. Finally, the acquired troubles are associated with objects so that each of the resulting pairs consists of an object and a trouble or obstacle in using that object. To show the effectiveness of our method we conducted experiments using a large collection of Japanese Web documents for acquisition. Experimental results show an 85.5% precision for the top 10,000 acquired troubles, and a 74% precision for the top 10% of over 60,000 acquired object-trouble pairs.