EXPLOITING BERT FOR MALFORMED SEGMENTATION DETECTION TO IMPROVE SCIENTIFIC WRITINGS

Almuhareb, A. a.-T. (2019). Arabic word segmentation with long short-term memory neural networks and word embedding. IEEE Access, 7, 12879-12887. https://doi.org/10.1109/ACCESS.2019.2893460
DOI: https://doi.org/10.1109/ACCESS.2019.2893460   Google Scholar

Barrow, J., Jain, R., Morariu, V., & Manjunatha, V. (2020). A joint model for document segmentation and segment labeling. Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, (pp. 313-322). Association for Computational Linguistics. https://doi.org/10.18653/v1/2020.acl-main.29
DOI: https://doi.org/10.18653/v1/2020.acl-main.29   Google Scholar

Cer, D., Diab, M., Agirre, E., Lopez-Gazpio, I., & Specia, L. (2017). Semeval-2017 task 1: Semantic textual similarity-multilingual and cross-lingual focused evaluation. arXiv. https://doi.org/10.48550/arXiv.1708.00055
DOI: https://doi.org/10.18653/v1/S17-2001   Google Scholar

Cer, D., Yang, Y., Kong, S., Hua, N., Limtiaco, N., John, R. S., Constant, N., Guajardo- Cespedes, M., Yuan, S., Tar, Ch., Sung, Y.-H. Strope, B., & Kurzweil, R. (2018). Universal sentence encoder. arXiv. https://doi.org/10.48550/arXiv.1803.11175
DOI: https://doi.org/10.18653/v1/D18-2029   Google Scholar

Devlin, J., Chang, M.-W., Lee, K., & Toutanova, K. (2018). Bert: Pre-training of deep bidirectional transformers for language understanding. arXiv. https://doi.org/10.48550/arXiv.1810.04805
  Google Scholar

Galanopoulos, D., & Mezaris, V.(2019). Temporal lecture video fragmentation using word embeddings. In Kompatsiaris, I., Huet, B., Mezaris, V., Gurrin, C., Cheng, W.-H., & Vrochidis, S. (Eds.) MultiMedia Modeling: 25th International Conference, MMM 2019, Thessaloniki, Greece, January 8--11, 2019, Proceedings, Part II (vol. 25, pp. 254--265). Springer. https://doi.org/10.1007/978-3-030-05716-9_21
DOI: https://doi.org/10.1007/978-3-030-05716-9_21   Google Scholar

Hearst, M. A. (1997). Text tiling: Segmenting text into multi-paragraph subtopic passages. Computational linguistics, 23(1), 33-64.
  Google Scholar

Hinkel, E. (2001). Matters of cohesion in L2 academic texts. Applied language learning, 12(2), 111-132.
  Google Scholar

ielts-mentor. (2022). Retrieved from https://www.ielts-mentor.com/reading-sample/gt-reading/3162- employment-in-japan ?
  Google Scholar

Levy, C. M., & Ransdell. S. (1996). The science of writing: Theories, methods, individual differences and applications. Routledge. https://doi.org/10.4324/9780203811122
DOI: https://doi.org/10.4324/9780203811122   Google Scholar

Lin, M., Nunamaker, J.F., Chau, M., & Chen, H. (2004). Segmentation of lecture videos based on text: a method combining multiple linguistic features. 37th Annual Hawaii International Conference on System Sciences. (pp. 9-9). IEEE. https://doi.org/10.1109/HICSS.2004.1265045
DOI: https://doi.org/10.1109/HICSS.2004.1265045   Google Scholar

Lin, M., Chau, M., Cao, J., & Nunamaker, J. F. (2005). Automated video segmentation for lecture videos: A linguistics-based approach. International Journal of Technology and Human Interaction (IJTHI), 1(2), 27-45. https://doi.org/10.4018/jthi.2005040102
DOI: https://doi.org/10.4018/jthi.2005040102   Google Scholar

Lo, K., Jin, Y., Tan, W., Liu, M., Du, L., & Buntine, W. (2021). Transformer over Pre-trained Transformer for Neural Text Segmentation with Enhanced Topic Coherence. arXiv. https://doi.org/10.48550/arXiv.2110.07160
DOI: https://doi.org/10.18653/v1/2021.findings-emnlp.283   Google Scholar

Luckert, M., & Schaefer- Kehnert, M. (2016). Using machine learning methods for evaluating the quality of technical documents.
  Google Scholar

Maraj, A., Martin, M. V., & Makrehchi, M. (2021). A More Effective Sentence-Wise Text Segmentation Approach Using BERT. In Llads, J., Lopresti, D., & Uchida, S (Eds.), Document Analysis and Recognition--ICDAR 2021, (pp. 236-250). Springer. https://doi.org/10.1007/978-3-030-86337-1_16
DOI: https://doi.org/10.1007/978-3-030-86337-1_16   Google Scholar

Ponceleon, D., & Srinivasan, S. (2001). Automatic discovery of salient segments in imperfect speech transcripts. Proceedings of the tenth international conference on Information and knowledge management, 490- 497. The ACM Digital Library. https://doi.org/10.1145/502585.502668
DOI: https://doi.org/10.1145/502585.502668   Google Scholar

Precision_and_recall. (2022). Retrieved from wikipedia: https://en.wikipedia.org/wiki/Precision_and_recall?oldformat=true
  Google Scholar

Reimers, N., & Gurevyvh, I. (2019). Sentence-BERT: Sentence embeddings using siamese BERT-networks. arXiv. https://doi.org/10.48550/arXiv.1908.10084
DOI: https://doi.org/10.18653/v1/D19-1410   Google Scholar

Schroff, F., Kalenichenko, D., & Philbin, J. (2015). Facenet: A unified embedding for face recognition and clustering. IEEE conference on computer vision and pattern recognition (CVPR) (pp.815-823). IEEE. https://doi.org/10.1109/CVPR.2015.7298682
DOI: https://doi.org/10.1109/CVPR.2015.7298682   Google Scholar

Shah, R. R., Yu, Y., Skaikh, A. D., & Zimmermann, R. (2015). TRACE: linguistic-based approach for automatic lecture video segmentation leveraging Wikipedia texts. 2015 IEEE International Symposium on Multimedia (ISM) (pp. 217-220). IEEE. https://doi.org/10.1109/ISM.2015.18
DOI: https://doi.org/10.1109/ISM.2015.18   Google Scholar

Soares, E. R., & Barrére, E. (2019). An optimization model for temporal video lecture segmentation using word2vec and acoustic features. Proceedings of the 25th Brazillian Symposium on Multimedia and the Web, 513-520. The ACM Digital Library. https://doi.org/10.1145/3323503.3349548
DOI: https://doi.org/10.1145/3323503.3349548   Google Scholar

Solbiati, A., Heffernan, K., Damaskinos, G., Poddar, S., Modi, S., & Cali, J. (2021). Unsupervised topic segmentation of meetings with BERT embeddings. arXiv. https://doi.org/10.48550/arXiv.2106.12978
  Google Scholar

Glavas, G., & Somasundaran, S. (2020). Two-level transformer and auxiliary coherence modeling for improved text segmentation. Proceedings of the AAAI Conference on Artificial Intelligence, 34(05), 7797-7804. https://doi.org/10.1609/aaai.v34i05.6284
DOI: https://doi.org/10.1609/aaai.v34i05.6284   Google Scholar

Text_segmentation. (2011). Retrieved from wikipedia: https://en.wikipedia.org/wiki/Text_segmentation
  Google Scholar

Ugur Akinci, G. K. (2012). Writing Transition Phrases and Sentences: 12 Types of Sentence and Paragraph Transitions with 112 Examples.
  Google Scholar

University, UAH. (n.d.). WRITING EFFECTIVE TRANSITIONS. Retrieved from https://www.uah.edu/images/administrative/student-successcenter/resources/handouts/handouts_2019/writing_effective_transitions.pdf
  Google Scholar

Wang, Y., Li, S., & Yang, J. (2018). Toward fast and accurate neural discourse segmentation. arXiv. https://doi.org/10.48550/arXiv.1808.09147
DOI: https://doi.org/10.18653/v1/D18-1116   Google Scholar