Algorithms on strings, trees, and sequences: computer science and computational biology
Algorithms on strings, trees, and sequences: computer science and computational biology
Learning to Classify Text Using Support Vector Machines: Methods, Theory and Algorithms
Learning to Classify Text Using Support Vector Machines: Methods, Theory and Algorithms
Support Vector Machine Active Learning with Application sto Text Classification
ICML '00 Proceedings of the Seventeenth International Conference on Machine Learning
MiTAP for real users, real data, real problems
CHI '03 Extended Abstracts on Human Factors in Computing Systems
A systematic comparison of various statistical alignment models
Computational Linguistics
Applied morphological processing of English
Natural Language Engineering
Mixed-initiative development of language processing systems
ANLC '97 Proceedings of the fifth conference on Applied natural language processing
Deep Read: a reading comprehension system
ACL '99 Proceedings of the 37th annual meeting of the Association for Computational Linguistics on Computational Linguistics
Reading comprehension tests for computer-based understanding evaluation
Natural Language Engineering
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
Methods for using textual entailment in open-domain question answering
ACL-44 Proceedings of the 21st International Conference on Computational Linguistics and the 44th annual meeting of the Association for Computational Linguistics
Recognizing textual entailment using a subsequence kernel method
AAAI'07 Proceedings of the 22nd national conference on Artificial intelligence - Volume 1
Recognizing entailment in intelligent tutoring systems*
Natural Language Engineering
CSLDAMT '10 Proceedings of the NAACL HLT 2010 Workshop on Creating Speech and Language Data with Amazon's Mechanical Turk
Acquiring entailment pairs across languages and domains: a data analysis
IWCS '11 Proceedings of the Ninth International Conference on Computational Semantics
Evaluating semantic evaluations: how RTE measures up
MLCW'05 Proceedings of the First international conference on Machine Learning Challenges: evaluating Predictive Uncertainty Visual Object Classification, and Recognizing Textual Entailment
Recognizing textual entailment via atomic propositions
MLCW'05 Proceedings of the First international conference on Machine Learning Challenges: evaluating Predictive Uncertainty Visual Object Classification, and Recognizing Textual Entailment
SPARTE, a test suite for recognising textual entailment in spanish
CICLing'06 Proceedings of the 7th international conference on Computational Linguistics and Intelligent Text Processing
Natural language inference for arabic using extended tree edit distance with subtrees
Journal of Artificial Intelligence Research
| Hi-index | 0.00 |
We describe our efforts to generate a large (100,000 instance) corpus of textual entailment pairs from the lead paragraph and headline of news articles. We manually inspected a small set of news stories in order to locate the most productive source of entailments, then built an annotation interface for rapid manual evaluation of further exemplars. With this training data we built an SVM-based document classifier, which we used for corpus refinement purposes---we believe that roughly three-quarters of the resulting corpus are genuine entailment pairs. We also discuss the difficulties inherent in manual entailment judgment, and suggest ways to ameliorate some of these.