The DOI matches its Crossref record; 24 of the 24 references it cites were found in a registry or an index.
PPDB 2.0: Better paraphrase ranking, fine-grained entailment relations, word embeddings, and style classification
Open accessAbstract
Ellie Pavlick, Pushpendre Rastogi, Juri Ganitkevitch, Benjamin Van Durme, Chris Callison-Burch. Proceedings of the 53rd Annual Meeting of the Association for Computational Linguistics and the 7th International Joint Conference on Natural Language Processing (Volume 2: Short Papers). 2015.
Overview
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Integrity signals
Registry
Cites
Statistics
Verification
Claims the paper makes
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Citations
Cites
Glove: Global Vectors for Word Representation
Pennington, J., Socher, R., & Manning, C. (2014) · Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP) · doi:10.3115/v1/d14-1162 · Cited by 34,071
Efficient Estimation of Word Representations in Vector Space
Mikolov, T., Chen, K., Corrado, G., & Dean, J. (2013) · arXiv · doi:10.48550/arxiv.1301.3781 · Cited by 26,568
Retrofitting Word Vectors to Semantic Lexicons
Faruqui, M., Dodge, J., Jauhar, S. K., Dyer, C., Hovy, E., & Smith, N. A. (2015) · Proceedings of the 2015 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies · doi:10.3115/v1/n15-1184 · Cited by 721
Learning Syntactic Patterns for Automatic Hypernym Discovery
Snow, R., Jurafsky, D. S., & Ng, A. Y. (2004) · neural information processing systems · Cited by 658
Paraphrasing with bilingual parallel corpora
Bannard, C., & Callison-Burch, C. (2005) · Proceedings of the 43rd Annual Meeting on Association for Computational Linguistics - ACL '05 · doi:10.3115/1219840.1219914 · Cited by 533
Self-citationProblems in Current Text Simplification Research: New Data Can Help
Xu, W., Callison-Burch, C., & Napoles, C. (2015) · Transactions of the Association for Computational Linguistics · doi:10.1162/tacl_a_00139 · Cited by 369
Self-citationUMBC_EBIQUITY-CORE: Semantic Textual Similarity Systems
Han, L., Kashyap, A. L., Finin, T., Mayfield, J., & Weese, J. (2013) · doi:10.13016/m28w38626 · Cited by 311
Improving Lexical Embeddings with Semantic Knowledge
Yu, M., & Dredze, M. (2014) · Proceedings of the 52nd Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers) · doi:10.3115/v1/p14-2089 · Cited by 276
Multi-View Learning of Word Embeddings via CCA
Dhillon, P. S., Foster, D. P., & Ungar, L. (2011) · ScholarlyCommons (University of Pennsylvania) · Cited by 241
Manning, C. D., & MacCartney, B. (2009) · Cited by 209
Cited by
A large annotated corpus for learning natural language inference
Bowman, S. R., Angeli, G., Potts, C., & Manning, C. D. (2015) · Proceedings of the 2015 Conference on Empirical Methods in Natural Language Processing · doi:10.18653/v1/d15-1075 · Cited by 3,377
Emergent: a novel data-set for stance classification
Ferreira, W., & Vlachos, A. (2016) · Proceedings of the 2016 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies · doi:10.18653/v1/n16-1138 · Cited by 360
RESIDE: Improving Distantly-Supervised Neural Relation Extraction using Side Information
Vashishth, S., Joshi, R., Prayaga, S. S., Bhattacharyya, C., & Talukdar, P. (2018) · Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing · doi:10.18653/v1/d18-1157 · Cited by 258
Academic Plagiarism Detection: A Systematic Literature Review
Foltýnek, T., Meuschke, N., & Gipp, B. (2020) · ACM Computing Surveys · doi:10.1145/3345317 · Cited by 238
Assessing Phrasal Representation and Composition in Transformers
Yu, L., & Ettinger, A. (2020) · Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP) · doi:10.18653/v1/2020.emnlp-main.397 · Cited by 58
Metadata
- Authors
- Pavlick, E., Rastogi, P., Ganitkevitch, J., Van Durme, B., & Callison-Burch, C.
- Venue
- Proceedings of the 53rd Annual Meeting of the Association for Computational Linguistics and the 7th International Joint Conference on Natural Language Processing (Volume 2: Short Papers)
- Published
- 2015
- Pages
- 425-430
- OpenAlex
- W2251882135
- Cited by
- 256
- References
- 24
- FWCI
- 15.34
- Type
- conference paper
- Language
- en
- Open access
- gold
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review
How to cite
Cite CoChat for the checks on this page, and the paper for its own findings.
- This page
- CoChat. (2026). Citation check: PPDB 2.0: Better paraphrase ranking, fine-grained entailment relations, word embeddings, and style classification. https://app.cochat.ai/papers/10.3115/v1/p15-2070
- The paper
- Pavlick, E., Rastogi, P., Ganitkevitch, J., Van Durme, B., & Callison-Burch, C. (2015). PPDB 2.0: Better paraphrase ranking, fine-grained entailment relations, word embeddings, and style classification. Proceedings of the 53rd Annual Meeting of the Association for Computational Linguistics and the 7th International Joint Conference on Natural Language Processing (Volume 2: Short Papers). https://doi.org/10.3115/v1/p15-2070
Data sources
Bibliographic data: Semantic Scholar, OpenAlex, Crossref.
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.
