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Found 7 papers, 2 papers with code
融入篇章信息的文学作品命名实体识别(Document-level Literary Named Entity Recognition)
“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。”
MMDAG: Multimodal Directed Acyclic Graph Network for Emotion Recognition in Conversation
Emotion recognition in conversation is important for an empathetic dialogue system to understand the user’s emotion and then generate appropriate emotional responses.
MRC-based Nested Medical NER with Co-prediction and Adaptive Pre-training
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.
FaiMA: Feature-aware In-context Learning for Multi-domain Aspect-based Sentiment Analysis
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)
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A Corpus for Named Entity Recognition in Chinese Novels with Multi-genres
Entities like person, location, organization are important for literary text analysis.
Knowledge-injected Prompt Learning for Chinese Biomedical Entity Normalization
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.
Zhongjing: Enhancing the Chinese Medical Capabilities of Large Language Model through Expert Feedback and Real-world Multi-turn Dialogue
Recent advances in Large Language Models (LLMs) have achieved remarkable breakthroughs in understanding and responding to user intents.
