no code implementations • CCL 2021 • Yuxiang Jia, Rui Chao, Hongying Zan, Huayi Dou, Shuai Cao, Shuo Xu
“命名实体识别是文学作品智能分析的基础性工作, 当前文学领域命名实体识别的研究还较薄弱, 一个主要的原因是缺乏标注语料。本文从金庸小说入手, 对两部小说180余万字进行了命名实体的标注, 共标注4类实体5万多个。针对小说文本的特点, 本文提出融入篇章信息的命名实体识别模型, 引入篇章字典保存汉字的历史状态, 利用可信度计算融合BiGRU-CRF与Transformer模型。实验结果表明, 利用篇章信息有效地提升了命名实体识别的效果。最后, 我们还探讨了命名实体识别在小说社会网络构建中的应用。”
no code implementations • LREC 2022 • Shuo Xu, Yuxiang Jia, Changyong Niu, Hongying Zan
Emotion recognition in conversation is important for an empathetic dialogue system to understand the user’s emotion and then generate appropriate emotional responses.
no code implementations • 23 Mar 2024 • Xiaojing Du, Hanjie Zhao, Danyan Xing, Yuxiang Jia, Hongying Zan
In medical information extraction, medical Named Entity Recognition (NER) is indispensable, playing a crucial role in developing medical knowledge graphs, enhancing medical question-answering systems, and analyzing electronic medical records.
1 code implementation • 2 Mar 2024 • Songhua Yang, Xinke Jiang, Hanjie Zhao, Wenxuan Zeng, Hongde Liu, Yuxiang Jia
While existing research narrowly focuses on single-domain applications constrained by methodological limitations and data scarcity, the reality is that sentiment naturally traverses multiple domains.
Aspect-Based Sentiment Analysis Aspect-Based Sentiment Analysis (ABSA) +4
no code implementations • 27 Nov 2023 • Hanjie Zhao, Jinge Xie, Yuchen Yan, Yuxiang Jia, Yawen Ye, Hongying Zan
Entities like person, location, organization are important for literary text analysis.
no code implementations • 23 Aug 2023 • Songhua Yang, Chenghao Zhang, Hongfei Xu, Yuxiang Jia
However, existing research falls short in tackling the more complex Chinese BEN task, especially in the few-shot scenario with limited medical data, and the vast potential of the external medical knowledge base has yet to be fully harnessed.
1 code implementation • 7 Aug 2023 • Songhua Yang, Hanjie Zhao, Senbin Zhu, Guangyu Zhou, Hongfei Xu, Yuxiang Jia, Hongying Zan
Recent advances in Large Language Models (LLMs) have achieved remarkable breakthroughs in understanding and responding to user intents.