The DOI matches its Crossref record; 2 of the 2 references it cites were found in a registry or an index.
The PASCAL Recognising Textual Entailment Challenge
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Citations
Cites
Measuring nominal scale agreement among many raters.
Fleiss, J. L. (1971) · Psychological Bulletin · doi:10.1037/h0031619 · Cited by 8,733
Evaluating Content Selection in Summarization: The Pyramid Method
Nenkova, A., & Passonneau, R. (2004) · Columbia University · doi:10.7916/d80r9xvd · Cited by 527
Research works printed but not linked: 16
- Bacchus, F.: Representing and Reasoning with Probabilistic Knowledge. MIT Press, Cambridge (1990)
- Bar-Haim, R., Szpektor, I., Glickman, O.: Definition and Analysis of Intermediate Entailment Levels. In: ACL 2005 Workshop on Empirical Modeling of Semantic Equivalence and Entailment (2005) · doi:10.3115/1631862.1631872
- Chierchia, G., McConnell-Ginet, S.: Meaning and grammar: An introduction to semantics, 2nd edn. MIT Press, Cambridge (2001)
- Condoravdi, C., Crouch, D., de Paiva, V., Stolle, R., Bobrow, D.G.: Entailment, intensionality and text understanding. In: HLT-NAACL Workshop on Text Meaning (2003) · DOI
- Corley, C., Mihalcea, R.: Measuring the Semantic Similarity of Texts. In: Proceedings of the ACL Workshop on Empirical Modeling of Semantic Equivalence and Entailment, Ann Arbor, June 2005, 1318 pages (2005) · DOI · Paper page
- Dagan, I., Glickman, O.: Probabilistic Textual Entailment: Generic Applied Modeling of Language Variability. In: PASCAL workshop on Learning Methods for Text Understanding and Mining, Grenoble, France, January 26- 29 (2004)
- Glickman, O., Dagan, I., Koppel, M.: A Lexical Alignment Model for Probabilistic Textual Entailment. In: Quiñonero-Candela, J., et al. (eds.) MLCW 2005. LNCS (LNAI), vol. 3944, pp. 287–298. Springer, Heidelberg (2006)
- Halpern, J.Y.: An analysis of first-order logics of probability. Artificial Intelligence 46, 311–350 (1990) · DOI · Paper page
- Keefe, R., Smith, P. (eds.): Vagueness: A Reader. MIT Press, Cambridge (1997)
- Lukasiewicz, J.: Selected Works. In: Borkowski, L. (ed.). North Holland, London (1970)
- Marsi, E., Krahmer, E.: Classification of Semantic Relations by Humans and Machines. In: Proceedings of the ACL Workshop on Empirical Modeling of Semantic Equivalence and Entailment (2005) · doi:10.3115/1631862.1631863
- Monz, C., de Rijke, M.: Light-Weight Entailment Checking for Computational Semantics. In: The third workshop on inference in computational semantics, ICoS-3 (2001)
- Vanderwende, L., Dolan, W.B.: What Syntax can Contribute in the Entailment Task. In: Quiñonero-Candela, J., et al. (eds.) MLCW 2005. LNCS (LNAI), vol. 3944, pp. 205–216. Springer, Heidelberg (2006)
- Szpektor, I., Tanev, H., Dagan, I., Coppola, B.: Scaling Web-based Acquisition of Entailment Relations. In: Empirical Methods in Natural Language Processing (EMNLP) (2004)
- Zadeh, L.: Fuzzy sets. Information and Control 8 (1965) · DOI · Paper page
- Zaenen, A., Karttunen, L., Crouch, R.: Local Textual Inference: Can it be Defined or Circumscribed? In: Proceedings of the ACL Workshop on Empirical Modeling of Semantic Equivalence and Entailment (2005) · DOI
Cited by
BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding
Devlin, J., Chang, M.-W., Lee, K., & Toutanova, K. (2018) · arXiv · doi:10.48550/arxiv.1810.04805 · Cited by 73,173
Language Models are Few-Shot Learners
Brown, T. B., Mann, B., Ryder, N., Subbiah, M., Kaplan, J., Dhariwal, P., Neelakantan, A., Shyam, P., Sastry, G., Askell, A., et al. (2020) · arXiv · doi:10.48550/arxiv.2005.14165 · Cited by 23,372
RoBERTa: A Robustly Optimized BERT Pretraining Approach
Liu, Y., Ott, M., Goyal, N., Du, J., Joshi, M., Chen, D., Levy, O., Lewis, M., Zettlemoyer, L., & Stoyanov, V. (2019) · arXiv · doi:10.48550/arxiv.1907.11692 · Cited by 16,515
Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformer
Raffel, C., Shazeer, N., Roberts, A., Lee, K., Narang, S., Matena, M., Zhou, Y., Li, W., & Liu, P. J. (2019) · arXiv · doi:10.48550/arxiv.1910.10683 · Cited by 10,929
Lewis, M., Liu, Y., Goyal, N., Ghazvininejad, M., Mohamed, A., Levy, O., Stoyanov, V., & Zettlemoyer, L. (2020) · Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics · doi:10.18653/v1/2020.acl-main.703 · Cited by 8,821
Metadata
- Authors
- Dagan, I., Glickman, O., & Magnini, B.
- Venue
- Lecture Notes in Computer Science
- Publisher
- Springer Berlin Heidelberg
- Published
- 2006
- Pages
- 177-190
- ISSN
- 0302-9743 (ISSN-L)
- 1611-3349
- OpenAlex
- W2130158090
- Cited by
- 1,628
- References
- 2
- FWCI
- 57.87
- Type
- conference paper
- Language
- en
- Open access
- closed
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- CoChat. (2026). Citation check: The PASCAL Recognising Textual Entailment Challenge. https://app.cochat.ai/papers/10.1007/11736790_9
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- Dagan, I., Glickman, O., & Magnini, B. (2006). The PASCAL Recognising Textual Entailment Challenge. Lecture Notes in Computer Science. https://doi.org/10.1007/11736790_9
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Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.
