DIRT @SBT@discovery of inference rules from text
Proceedings of the seventh ACM SIGKDD international conference on Knowledge discovery and data mining
Japanese dependency analysis using cascaded chunking
COLING-02 proceedings of the 6th conference on Natural language learning - Volume 20
Recognising textual entailment with logical inference
HLT '05 Proceedings of the conference on Human Language Technology and Empirical Methods in Natural Language Processing
YAGO: A Large Ontology from Wikipedia and WordNet
Web Semantics: Science, Services and Agents on the World Wide Web
ISUC '08 Proceedings of the 2008 Second International Symposium on Universal Communication
Modeling semantic containment and exclusion in natural language inference
COLING '08 Proceedings of the 22nd International Conference on Computational Linguistics - Volume 1
A phrase-based alignment model for natural language inference
EMNLP '08 Proceedings of the Conference on Empirical Methods in Natural Language Processing
The third PASCAL recognizing textual entailment challenge
RTE '07 Proceedings of the ACL-PASCAL Workshop on Textual Entailment and Paraphrasing
RTE '07 Proceedings of the ACL-PASCAL Workshop on Textual Entailment and Paraphrasing
On the role of lexical and world knowledge in RTE3
RTE '07 Proceedings of the ACL-PASCAL Workshop on Textual Entailment and Paraphrasing
Enhancing the Japanese WordNet
ALR7 Proceedings of the 7th Workshop on Asian Language Resources
Large-scale verb entailment acquisition from the web
EMNLP '09 Proceedings of the 2009 Conference on Empirical Methods in Natural Language Processing: Volume 3 - Volume 3
Natural language as the basis for meaning representation and inference
CICLing'08 Proceedings of the 9th international conference on Computational linguistics and intelligent text processing
"Ask not what textual entailment can do for you..."
ACL '10 Proceedings of the 48th Annual Meeting of the Association for Computational Linguistics
Assessing the role of discourse references in entailment inference
ACL '10 Proceedings of the 48th Annual Meeting of the Association for Computational Linguistics
A structured model for joint learning of argument roles and predicate senses
ACLShort '10 Proceedings of the ACL 2010 Conference Short Papers
Simple and efficient algorithm for approximate dictionary matching
COLING '10 Proceedings of the 23rd International Conference on Computational Linguistics
Integrating logical representations with probabilistic information using Markov logic
IWCS '11 Proceedings of the Ninth International Conference on Computational Semantics
Abductive reasoning with a large knowledge base for discourse processing
IWCS '11 Proceedings of the Ninth International Conference on Computational Semantics
Types of common-sense knowledge needed for recognizing textual entailment
HLT '11 Proceedings of the 49th Annual Meeting of the Association for Computational Linguistics: Human Language Technologies: short papers - Volume 2
Recognising textual entailment with robust logical inference
MLCW'05 Proceedings of the First international conference on Machine Learning Challenges: evaluating Predictive Uncertainty Visual Object Classification, and Recognizing Textual Entailment
Collecting evaluative expressions for opinion extraction
IJCNLP'04 Proceedings of the First international joint conference on Natural Language Processing
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Since the problem of textual entailment recognition requires capturing semantic relations between diverse expressions of language, linguistic and world knowledge play an important role. In this article, we explore the effectiveness of different types of currently available resources including synonyms, antonyms, hypernym-hyponym relations, and lexical entailment relations for the task of textual entailment recognition. In order to do so, we develop an entailment relation recognition system which utilizes diverse linguistic analyses and resources to align the linguistic units in a pair of texts and identifies entailment relations based on these alignments. We use the Japanese subset of the NTCIR-9 RITE-1 dataset for evaluation and error analysis, conducting ablation testing and evaluation on hand-crafted alignment gold standard data to evaluate the contribution of individual resources. Error analysis shows that existing knowledge sources are effective for RTE, but that their coverage is limited, especially for domain-specific and other low-frequency expressions. To increase alignment coverage on such expressions, we propose a method of alignment inference that uses syntactic and semantic dependency information to identify likely alignments without relying on external resources. Evaluation adding alignment inference to a system using all available knowledge sources shows improvements in both precision and recall of entailment relation recognition.