Discovery of inference rules for question-answering
Natural Language Engineering
Entailment, intensionality and text understanding
HLT-NAACL-TEXTMEANING '03 Proceedings of the HLT-NAACL 2003 workshop on Text meaning - Volume 9
SIGHAN '03 Proceedings of the second SIGHAN workshop on Chinese language processing - Volume 17
Negation, contrast and contradiction in text processing
AAAI'06 Proceedings of the 21st national conference on Artificial intelligence - Volume 1
It's a contradiction---no, it's not: a case study using functional relations
EMNLP '08 Proceedings of the Conference on Empirical Methods in Natural Language Processing
Unsupervised models for coreference resolution
EMNLP '08 Proceedings of the Conference on Empirical Methods in Natural Language Processing
Identifying synonyms among distributionally similar words
IJCAI'03 Proceedings of the 18th international joint conference on Artificial intelligence
The third PASCAL recognizing textual entailment challenge
RTE '07 Proceedings of the ACL-PASCAL Workshop on Textual Entailment and Paraphrasing
Contradiction-focused qualitative evaluation of textual entailment
NeSp-NLP '10 Proceedings of the Workshop on Negation and Speculation in Natural Language Processing
The PASCAL recognising textual entailment challenge
MLCW'05 Proceedings of the First international conference on Machine Learning Challenges: evaluating Predictive Uncertainty Visual Object Classification, and Recognizing Textual Entailment
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Detecting contradictive statements is a foundational and challenging task for text understanding applications such as textual entailment. In this article, we aim to address the problem of the shortage of specific background knowledge in contradiction detection. A novel contradiction detecting approach based on the distribution of the query composed of critical mismatch combinations on the Internet is proposed to tackle the problem. By measuring the availability of mismatch conjunction phrases (MCPs), the background knowledge about two target statements can be implicitly obtained for identifying contradictions. Experiments on three different configurations show that the MCP-based approach achieves remarkable improvement on contradiction detection and can significantly improve the performance of textual entailment recognition.