Recognizing 3-D objects by forward checking constrained tree search
Pattern Recognition Letters
WordNet: a lexical database for English
Communications of the ACM
A graph distance metric based on the maximal common subgraph
Pattern Recognition Letters
Making large-scale support vector machine learning practical
Advances in kernel methods
Meaning and grammar (2nd ed.): an introduction to semantics
Meaning and grammar (2nd ed.): an introduction to semantics
DIRT @SBT@discovery of inference rules from text
Proceedings of the seventh ACM SIGKDD international conference on Knowledge discovery and data mining
Learning to Classify Text Using Support Vector Machines: Methods, Theory and Algorithms
Learning to Classify Text Using Support Vector Machines: Methods, Theory and Algorithms
An Information-Theoretic Definition of Similarity
ICML '98 Proceedings of the Fifteenth International Conference on Machine Learning
Structural Similarity and Adaptation
EWCBR '96 Proceedings of the Third European Workshop on Advances in Case-Based Reasoning
The Case for Graph-Structured Representations
ICCBR '97 Proceedings of the Second International Conference on Case-Based Reasoning Research and Development
The Case for Graph-Structured Representations
ICCBR '97 Proceedings of the Second International Conference on Case-Based Reasoning Research and Development
Parsing engineering and empirical robustness
Natural Language Engineering
ACL '02 Proceedings of the 40th Annual Meeting on Association for Computational Linguistics
A study on convolution kernels for shallow semantic parsing
ACL '04 Proceedings of the 42nd Annual Meeting on Association for Computational Linguistics
Using information content to evaluate semantic similarity in a taxonomy
IJCAI'95 Proceedings of the 14th international joint conference on Artificial intelligence - Volume 1
Measuring the semantic similarity of texts
EMSEE '05 Proceedings of the ACL Workshop on Empirical Modeling of Semantic Equivalence and Entailment
A probabilistic setting and lexical cooccurrence model for textual entailment
EMSEE '05 Proceedings of the ACL Workshop on Empirical Modeling of Semantic Equivalence and Entailment
A linguistic inspection of textual entailment
AI*IA'05 Proceedings of the 9th conference on Advances in Artificial Intelligence
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
What syntax can contribute in the entailment task
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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Textual Entailment recognition is a very difficult task as it is one of the fundamental problems in any semantic theory of natural language. As in many other NLP tasks, Machine Learning may offer important tools to better understand the problem. In this paper, we will investigate the usefulness of Machine Learning algorithms to address an apparently simple and well defined classification problem: the recognition of Textual Entailment. Due to its specificity, we propose an original feature space, the distance feature space, where we model the distance between the elements of the candidate entailment pairs. The method has been tested on the data of the Recognizing Textual Entailment (RTE) Challenge.