Effectively using syntax for recognizing false entailment
HLT-NAACL '06 Proceedings of the main conference on Human Language Technology Conference of the North American Chapter of the Association of Computational Linguistics
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
Improving Question Answering Tasks by Textual Entailment Recognition
NLDB '08 Proceedings of the 13th international conference on Natural Language and Information Systems: Applications of Natural Language to Information Systems
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Information Processing and Management: an International Journal
A Gradual Combination of Features for Building Automatic Summarisation Systems
TSD '09 Proceedings of the 12th International Conference on Text, Speech and Dialogue
Studying the influence of semantic constraints in AVE
CLEF'08 Proceedings of the 9th Cross-language evaluation forum conference on Evaluating systems for multilingual and multimodal information access
Experiments on summary-based opinion classification
CAAGET '10 Proceedings of the NAACL HLT 2010 Workshop on Computational Approaches to Analysis and Generation of Emotion in Text
Improving automatic image captioning using text summarization techniques
TSD'10 Proceedings of the 13th international conference on Text, speech and dialogue
Improving reading comprehension using knowledge model
ISICA'10 Proceedings of the 5th international conference on Advances in computation and intelligence
COMPENDIUM: a text summarization system for generating abstracts of research papers
NLDB'11 Proceedings of the 16th international conference on Natural language processing and information systems
Web 2.0, Language Resources and standards to automatically build a multilingual Named Entity Lexicon
Language Resources and Evaluation
Editorial: COMPENDIUM: A text summarization system for generating abstracts of research papers
Data & Knowledge Engineering
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The textual entailment recognition system that we discuss in this paper represents a perspective-based approach composed of two modules that analyze text-hypothesis pairs from a strictly lexical and syntactic perspectives, respectively. We attempt to prove that the textual entailment recognition task can be overcome by performing individual analysis that acknowledges us of the maximum amount of information that each single perspective can provide. We compare this approach with the system we presented in the previous edition of PASCAL Recognising Textual Entailment Challenge, obtaining an accuracy rate 17.98% higher.