1.EMNLP 2023 信息抽取（NER、码解RE、码解EE）文章列表
2.MongoDB初学快速入门
EMNLP 2023 信息抽取（NER、码解RE、码解EE）文章列表
EMNLP文章列表如下： NER（命名实体识别）2INER: 稀少样本条件下具有指导性和上下文学习的码解命名实体识别
Structure and Label Constrained Data Augmentation for Cross-domain Few-shot NER
Alignment Precedes Fusion: Open-Vocabulary Named Entity Recognition as Context-Type Semantic Matching
ScdNER: 基于一致性感知的文档级命名实体识别
Towards Building More Robust NER datasets: An Empirical Study on NER Dataset Bias from a Dataset Difficulty View
Continual Named Entity Recognition without Catastrophic Forgetting
Adversarial Robustness for Large Language NER models using Disentanglement and Word Attributions
ESPVR: 多模态命名实体识别中的实体跨度位置视觉区域
CleanCoNLL: 几乎无噪声的命名实体识别数据集
NERetrieve: 用于下一代命名实体识别和检索的数据集
Prompting ChatGPT in MNER: 提升辅助精炼知识的多模态命名实体识别
Taxonomy Expansion for Named Entity Recognition
MProto: 基于去噪最优运输的多原型网络远距离监督命名实体识别
Empirical Study of Zero-Shot NER with ChatGPT
Less than One-shot: 非常弱监督下的命名实体识别
A Boundary Offset Prediction Network for Named Entity Recognition
Enhancing Low-resource Fine-grained Named Entity Recognition by Leveraging Coarse-grained Datasets
EXPLAIN, EDIT, GENERATE: 基于反事实数据增强的多跳事实验证
In-context Learning for Few-shot Multimodal Named Entity Recognition
Addressing NER Annotation Noises with Uncertainty-Guided Tree-Structured CRFs
A Query-Parallel Machine Reading Comprehension Framework for Low-resource NER
Causal Intervention-based Few-Shot Named Entity Recognition
MultiCoNER v2: 多语言精细和嘈杂命名实体识别的大型数据集
NERvous About My Health: 构建印地语医疗命名实体识别数据集
Type-Aware Decomposed Framework for Few-Shot Named Entity Recognition
GSAP-NER: 学术实体提取聚焦于机器学习模型和数据集的新型任务、语料库和基准
Learning to Rank Context for Named Entity Recognition Using a Synthetic Dataset
Re-weighting Tokens: 名称实体识别的码解青岛购买溯源码燕窝简单有效主动学习策略
SmartSpanNER: 低资源场景下命名实体识别的稳健性
Toward a Critical Toponymy Framework for Named Entity Recognition: 纽约市案例研究
Biomedical Named Entity Recognition via Dictionary-based Synonym Generalization
SKD-NER: 通过强化学习的跨度知识蒸馏进行持续命名实体识别
CASSI: 基于上下文和语义结构的插值增强低资源命名实体识别
RE（关系抽取）Always the Best Fit: 从因果角度填充域差距的少样本关系抽取
Noise-Robust Semi-Supervised Learning for Distantly Supervised Relation Extraction
Anaphor Assisted Document-Level Relation Extraction
Explore the Way: 通过连接实体构建推理路径进行跨文档关系抽取
Chain of Thought with Explicit Evidence Reasoning for Few-shot Relation Extraction
Joint Entity and Relation Extraction with Span Pruning and Hypergraph Neural Networks
Semi-automatic Data Enhancement for Document-Level Relation Extraction with Distant Supervision from Large Language Models
Chinese Metaphorical Relation Extraction
Self-distilled Transitive Instance Weighting for Denoised Distantly Supervised Relation Extraction
CoVariance-based Causal Debiasing for Entity and Relation Extraction
GPT-RE: 使用大型语言模型的上下文学习关系抽取
Reasoning Makes Good Annotators : 自动任务特定规则精炼框架低资源关系抽取
Towards Zero-shot Relation Extraction in Web Mining: 多模态方法结合相对XML路径的零样本关系抽取
A Spectral Viewpoint on Continual Relation Extraction
HFMRE: 使用哈夫曼树在袋中查找优秀实例进行远距离监督关系抽取
RAPL: 关系感知原型学习方法进行少样本文档级关系抽取
HyperNetwork-based Decoupling to Improve Model Generalization for Few-Shot Relation Extraction
Generating Commonsense Counterfactuals for Stable Relation Extraction
Improving Unsupervised Relation Extraction by Augmenting Diverse Sentence Pairs
Adaptive Hinge Balance Loss for Document-Level Relation Extraction
Global Structure Knowledge-Guided Relation Extraction Method for Visually-Rich Document
EE（事件检测和提取）Three Stream Based Multi-level Event Contrastive Learning for Text-Video Event Extraction
AniEE: 动物实验文献的事件抽取数据集
An Iteratively Parallel Generation Method with the Pre-Filling Strategy for Document-level Event Extraction
Continual Event Extraction with Semantic Confusion Rectification
Transitioning Representations between Languages for Cross-lingual Event Detection via Langevin Dynamics
BioDEX: 生物医学药物不良事件提取的大型数据集
GLEN: 通用事件检测的数千种类型
GenKIE: 生成式多模态文档关键信息提取
Set Learning for Generative Information Extraction
Exploiting Asymmetry for Synthetic Training Data Generation: SynthIE and the Case of Information Extraction
Lazy-k Decoding: 信息抽取的约束解码
Mirror: 多种信息抽取任务的通用框架
Guideline Learning for In-Context Information Extraction
Open Information Extraction via Chunks
Reading Order Matters: 通过预测标记路径信息抽取富媒体文档
Information Extraction from Legal Wills: GPT-4的表现如何?
Efficient Data Learning for Open Information Extraction with Pre-trained Language Models
On Event Individuation for Document-Level Information Extraction
Abstractive Open Information Extraction
Preserving Knowledge Invariance: 信息抽取鲁棒性评估的重新思考
Dialogue Medical Information Extraction with Medical-Item Graph and Dialogue-Status Enriched Representation
From Speculation Detection to Trustworthy Relational Tuples in Information Extraction
Information Extraction（信息抽取）Instruct and Extract: 指令调整的按需信息抽取
RexUIE: 具有明确模式指导的通用信息抽取递归方法
GenKIE: 生成式多模态文档关键信息提取
Set Learning for Generative Information Extraction
Exploiting Asymmetry for Synthetic Training Data Generation: SynthIE and the Case of Information Extraction
Lazy-k Decoding: 信息抽取的约束解码
Mirror: 多种信息抽取任务的通用框架
Guideline Learning for In-Context Information Extraction
Open Information Extraction via Chunks
Reading Order Matters: 通过预测标记路径信息抽取富媒体文档
Information Extraction from Legal Wills: GPT-4的表现如何?
Efficient Data Learning for Open Information Extraction with Pre-trained Language Models
On Event Individuation for Document-Level Information Extraction
Abstractive Open Information Extraction
Preserving Knowledge Invariance: 信息抽取鲁棒性评估的重新思考
Dialogue Medical Information Extraction with Medical-Item Graph and Dialogue-Status Enriched Representation
From Speculation Detection to Trustworthy Relational Tuples in Information Extraction
MongoDB初学快速入门
MongoDB，作为非关系型数据库的码解一种，以其灵活的码解文档存储结构和强大的数据查询能力而闻名。安装MongoDB及MongoDB Database Tool，码解用户便能轻松地进行数据导入、码解导出，码解为项目开发提供便利。码解通过实例演示数据操作，码解虚拟币量化交易机器人源码用户能快速上手。码解
启动MongoDB，码解进行基础操作，如插入与查找数据。深入了解数据管理基础，为更复杂的搜狗输入法源码安装包操作打下坚实基础。通过学习如何插入数据，用户能有效构建数据库，而查找功能则允许用户快速检索所需信息。
进阶查询操作涵盖了Limit和Count、Sort与Skip、$gt与$lt等。游戏网站盲盒抽奖系统源码这些操作帮助用户在大量数据中精确筛选所需信息，$gt与$lt尤其适用于特定范围内的数据筛选，而Limit和Count则允许用户控制结果数量，Sort与Skip则实现排序与跳过功能，使得数据呈现更加有序。
数组操作包括使用$or、超级玛丽网页版小游戏源码$and、$all、$in等关键字，通过组合这些操作，用户能实现复杂的数据匹配，满足多样化的查询需求。
在修改数据方面，$set、$unset、inc等操作适用于字段值的修改，而当涉及数组字段时，$push、$pull等操作则更加适用，提供对数组元素的增删功能。此外，delete操作允许用户彻底移除数据库中的元素，实现高效的数据管理。
利用Pycharm与Mongo的连接，用户能进行更深层次的开发，如在ComplexEventExtraction事理图谱抽取实战中，实现数据的高效处理与分析。
总结，MongoDB以其强大的数据处理能力，成为了众多开发者的首选。通过本文提供的内容，希望读者能快速掌握MongoDB的基础及进阶操作，为项目开发提供有力支持。