Detecting Polarized Topics Using Partisanship-aware Contextualized Topic Embeddings. He, Z., Mokhberian, N., Câmara, A., Abeliuk, A., & Lerman, K. In Moens, M., Huang, X., Specia, L., & Yih, S. W., editors, Findings of the Association for Computational Linguistics: EMNLP 2021, Virtual Event / Punta Cana, Dominican Republic, 16-20 November, 2021, pages 2102–2118, 2021. Association for Computational Linguistics.
Detecting Polarized Topics Using Partisanship-aware Contextualized Topic Embeddings [link]Paper  doi  bibtex   
@inproceedings{DBLP:conf/emnlp/HeMCAL21,
  author       = {Zihao He and
                  Negar Mokhberian and
                  Ant{\'{o}}nio C{\^{a}}mara and
                  Andr{\'{e}}s Abeliuk and
                  Kristina Lerman},
  editor       = {Marie{-}Francine Moens and
                  Xuanjing Huang and
                  Lucia Specia and
                  Scott Wen{-}tau Yih},
  title        = {Detecting Polarized Topics Using Partisanship-aware Contextualized
                  Topic Embeddings},
  booktitle    = {Findings of the Association for Computational Linguistics: {EMNLP}
                  2021, Virtual Event / Punta Cana, Dominican Republic, 16-20 November,
                  2021},
  pages        = {2102--2118},
  publisher    = {Association for Computational Linguistics},
  year         = {2021},
  url          = {https://doi.org/10.18653/v1/2021.findings-emnlp.181},
  doi          = {10.18653/V1/2021.FINDINGS-EMNLP.181},
  timestamp    = {Fri, 16 Feb 2024 08:27:36 +0100},
  biburl       = {https://dblp.org/rec/conf/emnlp/HeMCAL21.bib},
  bibsource    = {dblp computer science bibliography, https://dblp.org}
}

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