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Found 19 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。”
糖尿病电子病历实体及关系标注语料库构建(Construction of Corpus for Entity and Relation Annotation of Diabetes Electronic Medical Records)
“电子病历是医疗信息的重要来源, 包含大量与医疗相关的领域知识。本文从糖尿病电子病历文本入手, 在调研了国内外已有的电子病历语料库的基础上, 参考坉圲坂圲实体及关系分类, 建立了糖尿病电子病历实体及实体关系分类体系, 并制定了标注规范。利用实体及关系标注平台, 进行了实体及关系预标注及多轮人工校对工作, 形成了糖尿病电子病历实体及关系标注语料库(Diabetes Electronic Medical Record entity and Related Corpus DEMRC)。所构建的DEMRC包含8899个实体、456个实体修饰及16564个关系。对DEMRC进行一致性评价和分析, 标注结果达到了较高的一致性。针对实体识别和实体关系抽取任务, 分别采用基于迁移学习的Bi-LSTM-CRF模型和RoBERTa模型进行初步实验, 并对语料库中的各类实体及关系进行评估, 为后续糖尿病电子病历实体识别及关系抽取研究以及糖尿病知识图谱构建打下基础。”
脑卒中疾病电子病历实体及实体关系标注语料库构建(Corpus Construction for Named-Entity and Entity Relations for Electronic Medical Records of Stroke Disease)
“本文探讨了在脑卒中疾病中文电子病历文本中实体及实体间关系的标注问题, 提出了适用于脑卒中疾病电子病历文本的实体及实体关系标注体系和规范。在标注体系和规范的指导下, 进行了多轮的人工标注及校正工作, 完成了158万余字的脑卒中电子病历文本实体及实体关系的标注工作。构建了脑卒中电子病历实体及实体关系标注语料库(Stroke Electronic Medical Record entity and entity related Corpus SEMRC)。所构建的语料库共包含命名实体10594个, 实体关系14457个。实体名标注一致率达到85. 16%, 实体关系标注一致率达到94. 16%。”
Self-Supervised Curriculum Learning for Spelling Error Correction
By contrast, Curriculum Learning (CL) utilizes training data differently during training and has shown its effectiveness in improving both performance and training efficiency in many other NLP tasks.
Konwledge-Enabled Diagnosis Assistant Based on Obstetric EMRs and Knowledge Graph
In this paper, we treat the diagnosis assistant as a multi-label classification task and propose a Knowledge-Enabled Diagnosis Assistant (KEDA) model for the obstetric diagnosis assistant.
Chinese Grammatical Error Diagnosis Based on RoBERTa-BiLSTM-CRF Model
Chinese Grammatical Error Diagnosis (CGED) is a natural language processing task for the NLPTEA6 workshop.
Chinese Grammatical Errors Diagnosis System Based on BERT at NLPTEA-2020 CGED Shared Task
The detection stage is a sequential labelling model based on BiLSTM-CRF and BERT contextual word representation.
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.
ParaZh-22M: A Large-Scale Chinese Parabank via Machine Translation
Paraphrasing, i. e., restating the same meaning in different ways, is an important data augmentation approach for natural language processing (NLP).
期货领域知识图谱构建(Construction of Knowledge Graph in Futures Field)
“期货领域是数据最丰富的领域之一, 本文以商品期货的研究报告为数据来源构建了期货领域知识图谱(Commodity Futures Knowledge Graph, CFKG)。以期货产品为核心, 确立了概念分类体系及关系描述体系, 形成图谱的概念层;在MHS-BIA与GPN模型的基础上, 通过领域专家指导对242万字的研报文本进行标注与校对, 形成了CFKG数据层, 并设计了可视化查询系统。所构建的CFKG包含17, 003个农产品期货关系三元组、13, 703种非农产品期货关系三元组, 为期货领域文本分析、舆情监控和推理决策等应用提供知识支持。”
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.
OpenEval: Benchmarking Chinese LLMs across Capability, Alignment and Safety
In addition to these benchmarks, we have implemented a phased public evaluation and benchmark update strategy to ensure that OpenEval is in line with the development of Chinese LLMs or even able to provide cutting-edge benchmark datasets to guide the development of Chinese LLMs.
A Corpus for Named Entity Recognition in Chinese Novels with Multi-genres
Entities like person, location, organization are important for literary text analysis.
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.
Optimizing Deep Transformers for Chinese-Thai Low-Resource Translation
In this paper, we study the use of deep Transformer translation model for the CCMT 2022 Chinese-Thai low-resource machine translation task.
NAPG: Non-Autoregressive Program Generation for Hybrid Tabular-Textual Question Answering
Hybrid tabular-textual question answering (QA) requires reasoning from heterogeneous information, and the types of reasoning are mainly divided into numerical reasoning and span extraction.
CBLUE: A Chinese Biomedical Language Understanding Evaluation Benchmark
Artificial Intelligence (AI), along with the recent progress in biomedical language understanding, is gradually changing medical practice.
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