TextTiling: segmenting text into multi-paragraph subtopic passages
Computational Linguistics
Word selection for EBMT based on monolingual similarity and translation confidence
HLT-NAACL-PARALLEL '03 Proceedings of the HLT-NAACL 2003 Workshop on Building and using parallel texts: data driven machine translation and beyond - Volume 3
A fully-lexicalized probabilistic model for Japanese syntactic and case structure analysis
HLT-NAACL '06 Proceedings of the main conference on Human Language Technology Conference of the North American Chapter of the Association of Computational Linguistics
Analyzing the explanation structure of procedural texts: dealing with advice and warnings
STEP '08 Proceedings of the 2008 Conference on Semantics in Text Processing
Collecting evaluative expressions for opinion extraction
IJCNLP'04 Proceedings of the First international joint conference on Natural Language Processing
Classification of multiple-sentence questions
IJCNLP'05 Proceedings of the Second international joint conference on Natural Language Processing
Adapting the naive bayes classifier to rank procedural texts
ECIR'06 Proceedings of the 28th European conference on Advances in Information Retrieval
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A variety of know-how such as recipes and solutions for troubles have been stored on the Web. However, it is not so easy to appropriately find certain know-how information. If know-how could be appropriately detected, it would be much easier for us to know how to tackle unforeseen situations such as accidents and disasters. This paper proposes a promising method for acquiring know-how information from the Web. First, we extract passages containing at least one target object and then extract candidates for know-how from them. Then, passages containing the know-how are discriminated from non-know-how information considering each object and its typical usage.