{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T01:11:43Z","timestamp":1760145103178,"version":"build-2065373602"},"reference-count":37,"publisher":"MDPI AG","issue":"6","license":[{"start":{"date-parts":[[2024,6,19]],"date-time":"2024-06-19T00:00:00Z","timestamp":1718755200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Anhui Provincial Major Science and Technology Project","award":["202103a07020004","2021YFF0306402","KJ2020ZD40"],"award-info":[{"award-number":["202103a07020004","2021YFF0306402","KJ2020ZD40"]}]},{"name":"National Key Research and Development Program of China","award":["202103a07020004","2021YFF0306402","KJ2020ZD40"],"award-info":[{"award-number":["202103a07020004","2021YFF0306402","KJ2020ZD40"]}]},{"name":"Natural Science Research Project of Anhui Province Higher Education Institutions","award":["202103a07020004","2021YFF0306402","KJ2020ZD40"],"award-info":[{"award-number":["202103a07020004","2021YFF0306402","KJ2020ZD40"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Entropy"],"abstract":"<jats:p>Coreference resolution is a key task in Natural Language Processing. It is difficult to evaluate the similarity of long-span texts, which makes text-level encoding somewhat challenging. This paper first compares the impact of commonly used methods to improve the global information collection ability of the model on the BERT encoding performance. Based on this, a multi-scale context information module is designed to improve the applicability of the BERT encoding model under different text spans. In addition, improving linear separability through dimension expansion. Finally, cross-entropy loss is used as the loss function. After adding BERT and span BERT to the module designed in this article, F1 increased by 0.5% and 0.2%, respectively.<\/jats:p>","DOI":"10.3390\/e26060529","type":"journal-article","created":{"date-parts":[[2024,6,20]],"date-time":"2024-06-20T03:46:29Z","timestamp":1718855189000},"page":"529","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Coreference Resolution Based on High-Dimensional Multi-Scale Information"],"prefix":"10.3390","volume":"26","author":[{"given":"Yu","family":"Wang","sequence":"first","affiliation":[{"name":"Hefei Institutes of Physical Science, Chinese Academy of Sciences, Hefei 230031, China"},{"name":"Science Island Branch, Graduate School of USTC (University of Science and Technology of China), Hefei 230026, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Zenghui","family":"Ding","sequence":"additional","affiliation":[{"name":"Hefei Institutes of Physical Science, Chinese Academy of Sciences, Hefei 230031, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-2926-3977","authenticated-orcid":false,"given":"Tao","family":"Wang","sequence":"additional","affiliation":[{"name":"Hefei Institutes of Physical Science, Chinese Academy of Sciences, Hefei 230031, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Shu","family":"Xu","sequence":"additional","affiliation":[{"name":"Hefei Institutes of Physical Science, Chinese Academy of Sciences, Hefei 230031, China"},{"name":"Science Island Branch, Graduate School of USTC (University of Science and Technology of China), Hefei 230026, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Xianjun","family":"Yang","sequence":"additional","affiliation":[{"name":"Hefei Institutes of Physical Science, Chinese Academy of Sciences, Hefei 230031, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yining","family":"Sun","sequence":"additional","affiliation":[{"name":"Hefei Institutes of Physical Science, Chinese Academy of Sciences, Hefei 230031, China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2024,6,19]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","unstructured":"Zeldes, A. (2021). Can we Fix the Scope for Coreference? Problems and Solutions for Benchmarks beyond OntoNotes. arXiv.","DOI":"10.5210\/dad.2022.102"},{"key":"ref_2","unstructured":"Brack, A., M\u00fcller, D.U., Hoppe, A., and Ewerth, R. (April, January 28). Coreference resolution in research papers from multiple domains. Proceedings of the European Conference on Information Retrieval, Virtual Event."},{"key":"ref_3","doi-asserted-by":"crossref","unstructured":"Xu, L., and Choi, J.D. (2022). Modeling task interactions in document-level joint entity and relation extraction. arXiv.","DOI":"10.18653\/v1\/2022.naacl-main.395"},{"key":"ref_4","doi-asserted-by":"crossref","unstructured":"Ye, D., Lin, Y., Li, P., and Sun, M. (2021). Packed levitated marker for entity and relation extraction. arXiv.","DOI":"10.18653\/v1\/2022.acl-long.337"},{"key":"ref_5","doi-asserted-by":"crossref","unstructured":"Li, X., Yin, F., Sun, Z., Li, X., Yuan, A., Chai, D., Zhou, M., and Li, J. (2019). Entity-relation extraction as multi-turn question answering. arXiv.","DOI":"10.18653\/v1\/P19-1129"},{"key":"ref_6","doi-asserted-by":"crossref","unstructured":"Perevalov, A., Diefenbach, D., Usbeck, R., and Both, A. (2022, January 26\u201328). Qald-9-plus: A multilingual dataset for question answering over dbpedia and wikidata translated by native speakers. Proceedings of the 2022 IEEE 16th International Conference on Semantic Computing (ICSC), Laguna Hills, CA, USA.","DOI":"10.1109\/ICSC52841.2022.00045"},{"key":"ref_7","doi-asserted-by":"crossref","unstructured":"Yu, S., Song, J., Kim, H., Lee, S.m., Ryu, W.J., and Yoon, S. (2021). Rare tokens degenerate all tokens: Improving neural text generation via adaptive gradient gating for rare token embeddings. arXiv.","DOI":"10.18653\/v1\/2022.acl-long.3"},{"key":"ref_8","doi-asserted-by":"crossref","unstructured":"Chen, J., and Durrett, G. (2020). Robust question answering through sub-part alignment. arXiv.","DOI":"10.18653\/v1\/2021.naacl-main.98"},{"key":"ref_9","unstructured":"Falke, T., Meyer, C.M., and Gurevych, I. (2017, January 1). Concept-map-based multi-document summarization using concept coreference resolution and global importance optimization. Proceedings of the Eighth International Joint Conference on Natural Language Processing (Volume 1: Long Papers), Taipei, Taiwan."},{"key":"ref_10","doi-asserted-by":"crossref","unstructured":"Pasunuru, R., Liu, M., Bansal, M., Ravi, S., and Dreyer, M. (2021, January 6\u201311). Efficiently summarizing text and graph encodings of multi-document clusters. Proceedings of the 2021 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Online.","DOI":"10.18653\/v1\/2021.naacl-main.380"},{"key":"ref_11","unstructured":"Pra\u017e\u00e1k, O., and Konopik, M. (2022). End-to-end Multilingual Coreference Resolution with Mention Head Prediction. arXiv."},{"key":"ref_12","doi-asserted-by":"crossref","unstructured":"Shaw, P., Uszkoreit, J., and Vaswani, A. (2018). Self-attention with relative position representations. arXiv.","DOI":"10.18653\/v1\/N18-2074"},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"1057","DOI":"10.1007\/s11042-019-08208-6","article-title":"Multi-scale dilated convolution of convolutional neural network for crowd counting","volume":"79","author":"Wang","year":"2020","journal-title":"Multimed. Tools Appl."},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"626","DOI":"10.1016\/j.neucom.2022.01.005","article-title":"Review the state-of-the-art technologies of semantic segmentation based on deep learning","volume":"493","author":"Mo","year":"2022","journal-title":"Neurocomputing"},{"key":"ref_15","unstructured":"Weischedel, R., Pradhan, S., Ramshaw, L., Palmer, M., Xue, N., Marcus, M., Taylor, A., Greenberg, C., Hovy, E., and Belvin, R. (2011). Ontonotes Release 4.0, Linguistic Data Consortium. LDC2011T03."},{"key":"ref_16","doi-asserted-by":"crossref","unstructured":"Lee, K., He, L., Lewis, M., and Zettlemoyer, L. (2017). End-to-end neural coreference resolution. arXiv.","DOI":"10.18653\/v1\/D17-1018"},{"key":"ref_17","doi-asserted-by":"crossref","unstructured":"Lee, K., He, L., and Zettlemoyer, L. (2018). Higher-order coreference resolution with coarse-to-fine inference. arXiv.","DOI":"10.18653\/v1\/N18-2108"},{"key":"ref_18","unstructured":"Chiyah-Garcia, F.J., Suglia, A., Lopes, J., Eshghi, A., and Hastie, H. (2022). Exploring Multi-Modal Representations for Ambiguity Detection & Coreference Resolution in the SIMMC 2.0 Challenge. arXiv."},{"key":"ref_19","doi-asserted-by":"crossref","unstructured":"Miculicich, L., and Henderson, J. (2022). Graph refinement for coreference resolution. arXiv.","DOI":"10.18653\/v1\/2022.findings-acl.215"},{"key":"ref_20","doi-asserted-by":"crossref","unstructured":"Joshi, M., Levy, O., Weld, D.S., and Zettlemoyer, L. (2019). BERT for coreference resolution: Baselines and analysis. arXiv.","DOI":"10.18653\/v1\/D19-1588"},{"key":"ref_21","first-page":"15908","article-title":"Transformer in transformer","volume":"34","author":"Han","year":"2021","journal-title":"Adv. Neural Inf. Process. Syst."},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"64","DOI":"10.1162\/tacl_a_00300","article-title":"Spanbert: Improving pre-training by representing and predicting spans","volume":"8","author":"Joshi","year":"2020","journal-title":"Trans. Assoc. Comput. Linguist."},{"key":"ref_23","doi-asserted-by":"crossref","unstructured":"Xia, P., and Van Durme, B. (2021). Moving on from OntoNotes: Coreference resolution model transfer. arXiv.","DOI":"10.18653\/v1\/2021.emnlp-main.425"},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"454","DOI":"10.1016\/j.ins.2021.08.015","article-title":"Learning with joint cross-document information via multi-task learning for named entity recognition","volume":"579","author":"Wang","year":"2021","journal-title":"Inf. Sci."},{"key":"ref_25","doi-asserted-by":"crossref","unstructured":"Caciularu, A., Cohan, A., Beltagy, I., Peters, M.E., Cattan, A., and Dagan, I. (2021). CDLM: Cross-document language modeling. arXiv.","DOI":"10.18653\/v1\/2021.findings-emnlp.225"},{"key":"ref_26","doi-asserted-by":"crossref","unstructured":"Hsu, B., and Horwood, G. (2022). Contrastive representation learning for cross-document coreference resolution of events and entities. arXiv.","DOI":"10.18653\/v1\/2022.naacl-main.267"},{"key":"ref_27","doi-asserted-by":"crossref","unstructured":"Kirstain, Y., Ram, O., and Levy, O. (2021). Coreference resolution without span representations. arXiv.","DOI":"10.18653\/v1\/2021.acl-short.3"},{"key":"ref_28","unstructured":"Otmazgin, S., Cattan, A., and Goldberg, Y. (2022). F-coref: Fast, accurate and easy to use coreference resolution. arXiv."},{"key":"ref_29","doi-asserted-by":"crossref","unstructured":"Otmazgin, S., Cattan, A., and Goldberg, Y. (2022). Lingmess: Linguistically informed multi expert scorers for coreference resolution. arXiv.","DOI":"10.18653\/v1\/2023.eacl-main.202"},{"key":"ref_30","unstructured":"Kong, H. (2024). HuixiangDou-CR: Coreference Resolution in Group Chats. arXiv."},{"key":"ref_31","unstructured":"Devlin, J., Chang, M.W., Lee, K., and Toutanova, K. (2018). Bert: Pre-training of deep bidirectional transformers for language understanding. arXiv."},{"key":"ref_32","unstructured":"Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A.N., Kaiser, \u0141., and Polosukhin, I. (2017). Attention is all you need. Adv. Neural Inf. Process. Syst., 30."},{"key":"ref_33","doi-asserted-by":"crossref","unstructured":"Lin, T.Y., Doll\u00e1r, P., Girshick, R., He, K., Hariharan, B., and Belongie, S. (2017, January 21\u201316). Feature pyramid networks for object detection. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Honolulu, HI, USA.","DOI":"10.1109\/CVPR.2017.106"},{"key":"ref_34","doi-asserted-by":"crossref","first-page":"7663","DOI":"10.1109\/TIP.2021.3107211","article-title":"Person re-identification via attention pyramid","volume":"30","author":"Chen","year":"2021","journal-title":"IEEE Trans. Image Process."},{"key":"ref_35","doi-asserted-by":"crossref","first-page":"3499","DOI":"10.1109\/TBME.2019.2906667","article-title":"Dilated-inception net: Multi-scale feature aggregation for cardiac right ventricle segmentation","volume":"66","author":"Li","year":"2019","journal-title":"IEEE Trans. Biomed. Eng."},{"key":"ref_36","doi-asserted-by":"crossref","unstructured":"Szegedy, C., Ioffe, S., Vanhoucke, V., and Alemi, A. (2017, January 4\u20139). Inception-v4, inception-resnet and the impact of residual connections on learning. Proceedings of the AAAI Conference on Artificial Intelligence, San Francisco, CA, USA.","DOI":"10.1609\/aaai.v31i1.11231"},{"key":"ref_37","unstructured":"Pradhan, S., Moschitti, A., Xue, N., Uryupina, O., and Zhang, Y. (2012, January 13). CoNLL-2012 shared task: Modeling multilingual unrestricted coreference in OntoNotes. Proceedings of the Joint Conference on EMNLP and CoNLL-Shared Task, Jeju Island, Republic of Korea."}],"container-title":["Entropy"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1099-4300\/26\/6\/529\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,10]],"date-time":"2025-10-10T15:01:23Z","timestamp":1760108483000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1099-4300\/26\/6\/529"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,6,19]]},"references-count":37,"journal-issue":{"issue":"6","published-online":{"date-parts":[[2024,6]]}},"alternative-id":["e26060529"],"URL":"https:\/\/doi.org\/10.3390\/e26060529","relation":{},"ISSN":["1099-4300"],"issn-type":[{"type":"electronic","value":"1099-4300"}],"subject":[],"published":{"date-parts":[[2024,6,19]]}}}