Forgetting Exceptions is Harmful in Language Learning
Machine Learning - Special issue on natural language learning
Information fusion for multidocument summarization: paraphrasing and generation
Information fusion for multidocument summarization: paraphrasing and generation
Alignment of shared forests for bilingual corpora
COLING '96 Proceedings of the 16th conference on Computational linguistics - Volume 1
Loosely tree-based alignment for machine translation
ACL '03 Proceedings of the 41st Annual Meeting on Association for Computational Linguistics - Volume 1
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
Textual entailment recognition based on dependency analysis and wordnet
MLCW'05 Proceedings of the First international conference on Machine Learning Challenges: evaluating Predictive Uncertainty Visual Object Classification, and Recognizing Textual Entailment
Learning to recognize features of valid textual entailments
HLT-NAACL '06 Proceedings of the main conference on Human Language Technology Conference of the North American Chapter of the Association of Computational Linguistics
Identifying semantic equivalence for multi-document summarisation
Artificial Intelligence Review
Statement map: assisting information crediblity analysis by visualizing arguments
Proceedings of the 3rd workshop on Information credibility on the web
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
Natural logic for textual inference
RTE '07 Proceedings of the ACL-PASCAL Workshop on Textual Entailment and Paraphrasing
Automatic analysis of semantic similarity in comparable text through syntactic tree matching
COLING '10 Proceedings of the 23rd International Conference on Computational Linguistics
Generating phrasal and sentential paraphrases: A survey of data-driven methods
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
Textual entailment recognition using a linguistically–motivated decision tree classifier
MLCW'05 Proceedings of the First international conference on Machine Learning Challenges: evaluating Predictive Uncertainty Visual Object Classification, and Recognizing Textual Entailment
Revisiting Cross-document Structure Theory for multi-document discourse parsing
Information Processing and Management: an International Journal
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This paper addresses the classification of semantic relations between pairs of sentences extracted from a Dutch parallel corpus at the word, phrase and sentence level. We first investigate the performance of human annotators on the task of manually aligning dependency analyses of the respective sentences and of assigning one of five semantic relations to the aligned phrases (equals, generalizes, specifies, restates and intersects). Results indicate that humans can perform this task well, with an F-score of .98 on alignment and an F-score of .95 on semantic relations (after correction). We then describe and evaluate a combined alignment and classification algorithm, which achieves an F-score on alignment of .85 (using EuroWordNet) and an F-score of .80 on semantic relation classification.