Combining labeled and unlabeled data with co-training
COLT' 98 Proceedings of the eleventh annual conference on Computational learning theory
Learning to paraphrase: an unsupervised approach using multiple-sequence alignment
NAACL '03 Proceedings of the 2003 Conference of the North American Chapter of the Association for Computational Linguistics on Human Language Technology - Volume 1
Co-occurrence Retrieval: A Flexible Framework for Lexical Distributional Similarity
Computational Linguistics
A common theory of information fusion from multiple text sources step one: cross-document structure
SIGDIAL '00 Proceedings of the 1st SIGdial workshop on Discourse and dialogue - Volume 10
Bootstrapping lexical choice via multiple-sequence alignment
EMNLP '02 Proceedings of the ACL-02 conference on Empirical methods in natural language processing - Volume 10
Paraphrase acquisition for information extraction
PARAPHRASE '03 Proceedings of the second international workshop on Paraphrasing - Volume 16
Unsupervised construction of large paraphrase corpora: exploiting massively parallel news sources
COLING '04 Proceedings of the 20th international conference on Computational Linguistics
Learning to sportscast: a test of grounded language acquisition
Proceedings of the 25th international conference on Machine learning
CICLing '07 Proceedings of the 8th International Conference on Computational Linguistics and Intelligent Text Processing
Learning language semantics from ambiguous supervision
AAAI'07 Proceedings of the 22nd national conference on Artificial intelligence - Volume 1
Database-text alignment via structured multilabel classification
IJCAI'07 Proceedings of the 20th international joint conference on Artifical intelligence
Learning semantic correspondences with less supervision
ACL '09 Proceedings of the Joint Conference of the 47th Annual Meeting of the ACL and the 4th International Joint Conference on Natural Language Processing of the AFNLP: Volume 1 - Volume 1
EMNLP '09 Proceedings of the 2009 Conference on Empirical Methods in Natural Language Processing: Volume 1 - Volume 1
A statistical semantic parser that integrates syntax and semantics
CONLL '05 Proceedings of the Ninth Conference on Computational Natural Language Learning
Introduction to the CoNLL-2005 shared task: semantic role labeling
CONLL '05 Proceedings of the Ninth Conference on Computational Natural Language Learning
Loopy belief propagation for approximate inference: an empirical study
UAI'99 Proceedings of the Fifteenth conference on Uncertainty in artificial intelligence
A Bayesian model for unsupervised semantic parsing
HLT '11 Proceedings of the 49th Annual Meeting of the Association for Computational Linguistics: Human Language Technologies - Volume 1
Confidence driven unsupervised semantic parsing
HLT '11 Proceedings of the 49th Annual Meeting of the Association for Computational Linguistics: Human Language Technologies - Volume 1
A semi supervised learning model for mapping sentences to logical form with ambiguous supervision
NLDB'12 Proceedings of the 17th international conference on Applications of Natural Language Processing and Information Systems
A Bayesian approach to unsupervised semantic role induction
EACL '12 Proceedings of the 13th Conference of the European Chapter of the Association for Computational Linguistics
Crosslingual induction of semantic roles
ACL '12 Proceedings of the 50th Annual Meeting of the Association for Computational Linguistics: Long Papers - Volume 1
Aligning predicates across monolingual comparable texts using graph-based clustering
EMNLP-CoNLL '12 Proceedings of the 2012 Joint Conference on Empirical Methods in Natural Language Processing and Computational Natural Language Learning
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We argue that groups of unannotated texts with overlapping and non-contradictory semantics represent a valuable source of information for learning semantic representations. A simple and efficient inference method recursively induces joint semantic representations for each group and discovers correspondence between lexical entries and latent semantic concepts. We consider the generative semantics-text correspondence model (Liang et al., 2009) and demonstrate that exploiting the noncontradiction relation between texts leads to substantial improvements over natural baselines on a problem of analyzing human-written weather forecasts.