DIRT @SBT@discovery of inference rules from text
Proceedings of the seventh ACM SIGKDD international conference on Knowledge discovery and data mining
A Unified Theory of Inference for Text Understanding
A Unified Theory of Inference for Text Understanding
A maximum-entropy-inspired parser
NAACL 2000 Proceedings of the 1st North American chapter of the Association for Computational Linguistics conference
Verbs semantics and lexical selection
ACL '94 Proceedings of the 32nd annual meeting on Association for Computational Linguistics
ACL '02 Proceedings of the 40th Annual Meeting on Association for Computational Linguistics
Semantic Kernels for Text Classification Based on Topological Measures of Feature Similarity
ICDM '06 Proceedings of the Sixth International Conference on Data Mining
Automatic learning of textual entailments with cross-pair similarities
ACL-44 Proceedings of the 21st International Conference on Computational Linguistics and the 44th annual meeting of the Association for Computational Linguistics
A semantic approach to recognizing textual entailment
HLT '05 Proceedings of the conference on Human Language Technology and Empirical Methods in Natural Language Processing
Robust textual inference via graph matching
HLT '05 Proceedings of the conference on Human Language Technology and Empirical Methods in Natural Language Processing
Recognising textual entailment with logical inference
HLT '05 Proceedings of the conference on Human Language Technology and Empirical Methods in Natural Language Processing
Structure and semantics for expressive text kernels
Proceedings of the sixteenth ACM conference on Conference on information and knowledge management
Corpus-based and knowledge-based measures of text semantic similarity
AAAI'06 Proceedings of the 21st national conference on Artificial intelligence - Volume 1
Learning entailment rules for unary templates
COLING '08 Proceedings of the 22nd International Conference on Computational Linguistics - Volume 1
Inference rules and their application to recognizing textual entailment
EACL '09 Proceedings of the 12th Conference of the European Chapter of the Association for Computational Linguistics
WordNet::Similarity: measuring the relatedness of concepts
HLT-NAACL--Demonstrations '04 Demonstration Papers at HLT-NAACL 2004
Semantic inference at the lexical-syntactic level
AAAI'07 Proceedings of the 22nd national conference on Artificial intelligence - Volume 1
A study on similarity and relatedness using distributional and WordNet-based approaches
NAACL '09 Proceedings of Human Language Technologies: The 2009 Annual Conference of the North American Chapter of the Association for Computational Linguistics
A machine learning approach to textual entailment recognition
Natural Language Engineering
Word sense disambiguation with distribution estimation
IJCAI'05 Proceedings of the 19th 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
A semantic approach to textual entailment: system evaluation and task analysis
RTE '07 Proceedings of the ACL-PASCAL Workshop on Textual Entailment and Paraphrasing
Semantic and logical inference model for textual entailment
RTE '07 Proceedings of the ACL-PASCAL Workshop on Textual Entailment and Paraphrasing
A compact forest for scalable inference over entailment and paraphrase rules
EMNLP '09 Proceedings of the 2009 Conference on Empirical Methods in Natural Language Processing: Volume 3 - Volume 3
Effective use of WordNet semantics via kernel-based learning
CONLL '05 Proceedings of the Ninth Conference on Computational Natural Language Learning
Combined syntactic and semantic Kernels for text classification
ECIR'07 Proceedings of the 29th European conference on IR research
Kernel engineering for fast and easy design of natural language applications
COLING '10 Proceedings of the 23rd International Conference on Computational Linguistics: Kernel Engineering for Fast and Easy Design of Natural Language Applications
Using bilingual parallel corpora for cross-lingual textual entailment
HLT '11 Proceedings of the 49th Annual Meeting of the Association for Computational Linguistics: Human Language Technologies - Volume 1
Abductive reasoning with a large knowledge base for discourse processing
IWCS '11 Proceedings of the Ninth International Conference on Computational Semantics
Distributed structures and distributional meaning
DiSCo '11 Proceedings of the Workshop on Distributional Semantics and Compositionality
Structured lexical similarity via convolution kernels on dependency trees
EMNLP '11 Proceedings of the Conference on Empirical Methods in Natural Language Processing
Structural relationships for large-scale learning of answer re-ranking
SIGIR '12 Proceedings of the 35th international ACM SIGIR conference on Research and development in information retrieval
FBK: machine translation evaluation and word similarity metrics for semantic textual similarity
SemEval '12 Proceedings of the First Joint Conference on Lexical and Computational Semantics - Volume 1: Proceedings of the main conference and the shared task, and Volume 2: Proceedings of the Sixth International Workshop on Semantic Evaluation
Verb classification using distributional similarity in syntactic and semantic structures
ACL '12 Proceedings of the 50th Annual Meeting of the Association for Computational Linguistics: Long Papers - Volume 1
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In this paper, we describe an approach based on off-the-shelf parsers and semantic resources for the Recognizing Textual Entailment (RTE) challenge that can be generally applied to any domain. Syntax is exploited by means of tree kernels whereas lexical semantics is derived from heterogeneous resources, e.g. WordNet or distributional semantics through Wikipedia. The joint syntactic/semantic model is realized by means of tree kernels, which can exploit lexical related-ness to match syntactically similar structures, i.e. whose lexical compounds are related. The comparative experiments across different RTE challenges and traditional systems show that our approach consistently and meaningfully achieves high accuracy, without requiring any adaptation or tuning.