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@@ -146,11 +146,19 @@ This way, the model learns an inner representation of 100 languages that can the
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  This model is limited by its training dataset of entity-annotated news articles from a specific span of time. This may not generalize well for all use cases in different domains.
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- ## Bibtex Reference
 
 
 
 
 
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  ```text
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  @inproceedings{schelbECCEEntitycentricCorpus2022,
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  title = {{ECCE}: {Entity}-centric {Corpus} {Exploration} {Using} {Contextual} {Implicit} {Networks}},
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  url = {https://dl.acm.org/doi/10.1145/3487553.3524237},
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  }
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  ```
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- ## Related Papers
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- * Pan, X., Zhang, B., May, J., Nothman, J., Knight, K., & Ji, H. (2017). Cross-lingual Name Tagging and Linking for 282 Languages. In Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers) (pp. 1946–1958). Association for Computational Linguistics.
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- * Rahimi, A., Li, Y., & Cohn, T. (2019). Massively Multilingual Transfer for NER. In Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics (pp. 151–164). Association for Computational Linguistics.
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- * Liu, Y., Ott, M., Goyal, N., Du, J., Joshi, M., Chen, D., Levy, O., Lewis, M., Zettlemoyer, L., & Stoyanov, V.. (2019). RoBERTa: A Robustly Optimized BERT Pretraining Approach.
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  This model is limited by its training dataset of entity-annotated news articles from a specific span of time. This may not generalize well for all use cases in different domains.
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+ ## Related Papers
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+ * Pan, X., Zhang, B., May, J., Nothman, J., Knight, K., & Ji, H. (2017). Cross-lingual Name Tagging and Linking for 282 Languages. In Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers) (pp. 1946–1958). Association for Computational Linguistics.
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+ * Rahimi, A., Li, Y., & Cohn, T. (2019). Massively Multilingual Transfer for NER. In Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics (pp. 151–164). Association for Computational Linguistics.
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+ * Liu, Y., Ott, M., Goyal, N., Du, J., Joshi, M., Chen, D., Levy, O., Lewis, M., Zettlemoyer, L., & Stoyanov, V.. (2019). RoBERTa: A Robustly Optimized BERT Pretraining Approach.
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+ ## Bibtex Citation
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  ```text
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+ This model was fine-tuned for the following paper. This is how you can cite it if you like:
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  @inproceedings{schelbECCEEntitycentricCorpus2022,
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  title = {{ECCE}: {Entity}-centric {Corpus} {Exploration} {Using} {Contextual} {Implicit} {Networks}},
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  url = {https://dl.acm.org/doi/10.1145/3487553.3524237},
 
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  }
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  ```