Parallel distributed processing: explorations in the microstructure of cognition, vol. 1: foundations
Parallel distributed processing: explorations in the microstructure of cognition, vol. 1
The nature of statistical learning theory
The nature of statistical learning theory
Making large-scale support vector machine learning practical
Advances in kernel methods
Distributed representations and nested compositional structure
Distributed representations and nested compositional structure
ACL '02 Proceedings of the 40th Annual Meeting on Association for Computational Linguistics
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 machine learning approach to textual entailment recognition
Natural Language Engineering
Measuring the semantic similarity of texts
EMSEE '05 Proceedings of the ACL Workshop on Empirical Modeling of Semantic Equivalence and Entailment
The third PASCAL recognizing textual entailment challenge
RTE '07 Proceedings of the ACL-PASCAL Workshop on Textual Entailment and Paraphrasing
Recognizing textual entailment using sentence similarity based on dependency tree skeletons
RTE '07 Proceedings of the ACL-PASCAL Workshop on Textual Entailment and Paraphrasing
Syntactic/semantic structures for textual entailment recognition
HLT '10 Human Language Technologies: The 2010 Annual Conference of the North American Chapter of the Association for Computational Linguistics
A regression model of adjective-noun compositionality in distributional semantics
GEMS '10 Proceedings of the 2010 Workshop on GEometrical Models of Natural Language Semantics
EMNLP '10 Proceedings of the 2010 Conference on Empirical Methods in Natural Language Processing
Estimating linear models for compositional distributional semantics
COLING '10 Proceedings of the 23rd International Conference on 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
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Stemming from distributed representation theories, we investigate the interaction between distributed structure and distributional meaning. We propose a pure distributed tree (DT) and distributional distributed tree (DDT). DTs and DDTs are exploited for defining distributed tree kernels (DTKs) and distributional distributed tree kernels (DDTKs). We compare DTKs and DDTKs in two tasks: approximating tree kernels TK (Collins and Duffy, 2002); performing textual entailment recognition (RTE). Results show that DTKs correlate with TKs and perform in RTE better than DDTKs. Then, including distributional vectors in distributed structures is a very difficult task.