A common theory of information fusion from multiple text sources step one: cross-document structure
SIGDIAL '00 Proceedings of the 1st SIGdial workshop on Discourse and dialogue - Volume 10
Japanese dependency analysis using cascaded chunking
COLING-02 proceedings of the 6th conference on Natural language learning - Volume 20
Statement map: assisting information crediblity analysis by visualizing arguments
Proceedings of the 3rd workshop on Information credibility on the web
The third PASCAL recognizing textual entailment challenge
RTE '07 Proceedings of the ACL-PASCAL Workshop on Textual Entailment and Paraphrasing
Revisiting Cross-document Structure Theory for multi-document discourse parsing
Information Processing and Management: an International Journal
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In the Recognizing Textual Entailment (RTE) task, sentence pairs are classified into one of three semantic relations: Entailment, Contradiction or Unknown. While we find some sentence pairs hold full entailments or contradictions, there are a number of pairs that partially entail or contradict one another depending on a specific situation. These partial contradiction sentence pairs contain useful information for opinion mining and other such tasks, but it is difficult for Internet users to access this knowledge because current frameworks do not differentiate between full contradictions and partial contradictions. In this paper, under current approaches to semantic relation recognition, we define a new semantic relation known as Confinement in order to recognize this useful information. This information is classified as either Contradiction or Entailment. We provide a series of semantic templates to recognize Confinement relations in Web texts, and then implement a system for recognizing Confinement between sentence pairs. We show that our proposed system can obtains a F-score of 61% for recognizing Confinement in Japanese-language Web texts, and it outperforms a baseline which does not use a manually compiled list of lexico-syntactic patterns to instantiate the semantic templates.