Network Configuration Entity Extraction Method Based on Transformer with Multi-Head Attention Mechanism

Nowadays,ensuring thequality of networkserviceshas become increasingly vital.Experts are turning toknowledge graph technology,with a significant emphasis on entity extraction in the identification of device configurations.This research paper presents a novel entity extraction method that leverages a combination of active learning and attention mechanisms.Initially,an improved active learning approach is employed to select the most valuable unlabeled samples,which are subsequently submitted for expert labeling.This approach successfully addresses the problems of isolated points and sample redundancy within the network configuration sample set.Then the labeled samples are utilized to train the model for network configuration entity extraction.Furthermore,the multi-head self-attention of the transformer model is enhanced by introducing the Adaptive Weighting method based on the Laplace mixture distribution.This enhancement enables the transformer model to dynamically adapt its focus to words in various positions,displaying exceptional adaptability to abnormal data and further elevating the accuracy of the proposed model.Through comparisons with Random Sampling(RANDOM),Maximum Normalized Log-Probability(MNLP),Least Confidence(LC),Token Entrop(TE),and Entropy Query by Bagging(EQB),the proposed method,Entropy Query by Bagging and Maximum Influence Active Learning(EQBMIAL),achieves comparable performance with only 40% of the samples on both datasets,while other algorithms require 50% of the samples.Furthermore,the entity extraction algorithm with the Adaptive Weighted Multi-head Attention mechanism(AW-MHA)is compared with BILSTM-CRF,Mutil_Attention-Bilstm-Crf,Deep_Neural_Model_NER and BERT_Transformer,achieving precision rates of 75.98% and 98.32% on the two datasets,respectively.Statistical tests demonstrate the statistical significance and effectiveness of the proposed algorithms in this paper.

SciCN:A Scientific Dataset for Chinese Named Entity Recognition

Named entity recognition(NER)is a fundamental task of information extraction(IE),and it has attracted considerable research attention in recent years.The abundant annotated English NER datasets have significantly promoted the NER research in the English field.By contrast,much fewer efforts are made to the Chinese NER research,especially in the scientific domain,due to the scarcity of Chinese NER datasets.To alleviate this problem,we present aChinese scientificNER dataset–SciCN,which contains entity annotations of titles and abstracts derived from 3,500 scientific papers.We manually annotate a total of 62,059 entities,and these entities are classified into six types.Compared to English scientific NER datasets,SciCN has a larger scale and is more diverse,for it not only contains more paper abstracts but these abstracts are derived from more research fields.To investigate the properties of SciCN and provide baselines for future research,we adapt a number of previous state-of-theart Chinese NER models to evaluate SciCN.Experimental results show that SciCN is more challenging than other Chinese NER datasets.In addition,previous studies have proven the effectiveness of using lexicons to enhance Chinese NER models.Motivated by this fact,we provide a scientific domain-specific lexicon.Validation results demonstrate that our lexicon delivers better performance gains than lexicons of other domains.We hope that the SciCN dataset and the lexicon will enable us to benchmark the NER task regarding the Chinese scientific domain and make progress for future research.The dataset and lexicon are available at:https://github.com/yangjingla/SciCN.git.

RoBGP:A Chinese Nested Biomedical Named Entity Recognition Model Based on RoBERTa and Global Pointer

Named Entity Recognition(NER)stands as a fundamental task within the field of biomedical text mining,aiming to extract specific types of entities such as genes,proteins,and diseases from complex biomedical texts and categorize them into predefined entity types.This process can provide basic support for the automatic construction of knowledge bases.In contrast to general texts,biomedical texts frequently contain numerous nested entities and local dependencies among these entities,presenting significant challenges to prevailing NER models.To address these issues,we propose a novel Chinese nested biomedical NER model based on RoBERTa and Global Pointer(RoBGP).Our model initially utilizes the RoBERTa-wwm-ext-large pretrained language model to dynamically generate word-level initial vectors.It then incorporates a Bidirectional Long Short-Term Memory network for capturing bidirectional semantic information,effectively addressing the issue of long-distance dependencies.Furthermore,the Global Pointer model is employed to comprehensively recognize all nested entities in the text.We conduct extensive experiments on the Chinese medical dataset CMeEE and the results demonstrate the superior performance of RoBGP over several baseline models.This research confirms the effectiveness of RoBGP in Chinese biomedical NER,providing reliable technical support for biomedical information extraction and knowledge base construction.

A U-Shaped Network-Based Grid Tagging Model for Chinese Named Entity Recognition

Chinese named entity recognition(CNER)has received widespread attention as an important task of Chinese information extraction.Most previous research has focused on individually studying flat CNER,overlapped CNER,or discontinuous CNER.However,a unified CNER is often needed in real-world scenarios.Recent studies have shown that grid tagging-based methods based on character-pair relationship classification hold great potential for achieving unified NER.Nevertheless,how to enrich Chinese character-pair grid representations and capture deeper dependencies between character pairs to improve entity recognition performance remains an unresolved challenge.In this study,we enhance the character-pair grid representation by incorporating both local and global information.Significantly,we introduce a new approach by considering the character-pair grid representation matrix as a specialized image,converting the classification of character-pair relationships into a pixel-level semantic segmentation task.We devise a U-shaped network to extract multi-scale and deeper semantic information from the grid image,allowing for a more comprehensive understanding of associative features between character pairs.This approach leads to improved accuracy in predicting their relationships,ultimately enhancing entity recognition performance.We conducted experiments on two public CNER datasets in the biomedical domain,namely CMeEE-V2 and Diakg.The results demonstrate the effectiveness of our approach,which achieves F1-score improvements of 7.29 percentage points and 1.64 percentage points compared to the current state-of-the-art(SOTA)models,respectively.

GeoNER:Geological Named Entity Recognition with Enriched Domain Pre-Training Model and Adversarial Training

As important geological data,a geological report contains rich expert and geological knowledge,but the challenge facing current research into geological knowledge extraction and mining is how to render accurate understanding of geological reports guided by domain knowledge.While generic named entity recognition models/tools can be utilized for the processing of geoscience reports/documents,their effectiveness is hampered by a dearth of domain-specific knowledge,which in turn leads to a pronounced decline in recognition accuracy.This study summarizes six types of typical geological entities,with reference to the ontological system of geological domains and builds a high quality corpus for the task of geological named entity recognition(GNER).In addition,Geo Wo BERT-adv BGP(Geological Word-base BERTadversarial training Bi-directional Long Short-Term Memory Global Pointer)is proposed to address the issues of ambiguity,diversity and nested entities for the geological entities.The model first uses the fine-tuned word granularitybased pre-training model Geo Wo BERT(Geological Word-base BERT)and combines the text features that are extracted using the Bi LSTM(Bi-directional Long Short-Term Memory),followed by an adversarial training algorithm to improve the robustness of the model and enhance its resistance to interference,the decoding finally being performed using a global association pointer algorithm.The experimental results show that the proposed model for the constructed dataset achieves high performance and is capable of mining the rich geological information.

SHEL:a semantically enhanced hardware-friendly entity linking method

With the help of pre-trained language models,the accuracy of the entity linking task has made great strides in recent years.However,most models with excellent performance require fine-tuning on a large amount of training data using large pre-trained language models,which is a hardware threshold to accomplish this task.Some researchers have achieved competitive results with less training data through ingenious methods,such as utilizing information provided by the named entity recognition model.This paper presents a novel semantic-enhancement-based entity linking approach,named semantically enhanced hardware-friendly entity linking(SHEL),which is designed to be hardware friendly and efficient while maintaining good performance.Specifically,SHEL's semantic enhancement approach consists of three aspects:(1)semantic compression of entity descriptions using a text summarization model;(2)maximizing the capture of mention contexts using asymmetric heuristics;(3)calculating a fixed size mention representation through pooling operations.These series of semantic enhancement methods effectively improve the model's ability to capture semantic information while taking into account the hardware constraints,and significantly improve the model's convergence speed by more than 50%compared with the strong baseline model proposed in this paper.In terms of performance,SHEL is comparable to the previous method,with superior performance on six well-established datasets,even though SHEL is trained using a smaller pre-trained language model as the encoder.

A Novel Optimization Scheme for Named Entity Recognition with Pre-trained Language Models

Named Entity Recognition(NER)is crucial for extracting structured information from text.While traditional methods rely on rules,Conditional Random Fields(CRFs),or deep learning,the advent of large-scale Pre-trained Language Models(PLMs)offers new possibilities.PLMs excel at contextual learning,potentially simplifying many natural language processing tasks.However,their application to NER remains underexplored.This paper investigates leveraging the GPT-3 PLM for NER without fine-tuning.We propose a novel scheme that utilizes carefully crafted templates and context examples selected based on semantic similarity.Our experimental results demonstrate the feasibility of this approach,suggesting a promising direction for harnessing PLMs in NER.

Entity Framework在KR脱硫自动控制系统中的应用

本文介绍一种以EF为框架的KR脱硫自动控制系统。该系统用PLC控制脱硫设备和处理生产数据,2个S7-300PLC分别控制两台扒渣机,1个S7-400控制搅拌头。2个工控机实现系统的双机热备。重点研究了以EF为技术框架,运用EF中新特性的功能,如LINQ、EDM,完成PC上位机与控制系统中PLC下位机之间不同类型数据处理方法,展现EF在传统ADO基础上新特性在控制系统中的应用。为基于C#以EF为技术框架的上位机控制大型机械设备控制系统提供了参考。

Bean管理的EntityBean与Oracle数据库的互操作应用

重点讨论了Bean管理的EntityBean对Oracle数据库的访问 ,包括连接、插入、查询、更新和删除操作。

基于LINQ to Entity数据访问技术的应用研究

在应用系统编写的过程中．提高编写数据访问代码的效率及代码的健壮性是程序员追求的重要目标。在VisualStudio2008中提供的LINQ技术可以帮助程序员实现这两个目标。LINQ的语法类似于SQL，同时可以使用同样的语法结构对数据集对象、关系数据库、XML数据及EntityFramework数据进行操作。

基于Entity Framework的图书馆光盘管理系统

随着计算机与多媒体技术的普及和应用,图书出版形式呈现多样化,随盘图书越来越多,而在高校数字图书馆的建设过程中,数据存储和光盘共享是一个关键环节,也是一个难题。针对广州大学华软软件学院图书馆在光盘管理方面存在的问题,结合实际情况提出一个基于Entity Framework的光盘管理系统,分析系统实现的关键技术并给出最终实现的效果。

基于Entity Framework数据持久化技术浅析

在面向对象的应用开发中,对象的持久化问题一直是最受程序员关注的问题之一。Entity Framework是微软新一代对象关系映射解决方案。该项技术基于传统的实体联系模型建立,概念清晰,明显提高开发效率,这一技术必将成为基于.NET平台开发的主流数据持久化技术。

ADO.NET Entity Framework建模技术研究

Entity Framework是微软以ADO.NET为基础所发展出来的对象关系对应解决方案,本文总结了部分常见的数据库模型对应的Entity Framework建模参考模型,以提高程序开发效率及代码的正确性,更好地发挥Entity Framework技术优势。

Entity Framework技术及其应用

Entity Framework(简称EF)是微软推出LINQ to SQL后的新一代ORM技术。目前企业级应用软件开发均采用层次逻辑架构,重点讨论基于.NET的ORM技术变迁,分析EF框架的EDM三部分映射文件,并通过EDM工具的DataBase First方式生成实体类和映射文件,完成数据访问及实例。

Entity Framework数据访问性能优化的几种方法

Entity Framework一般通过LINQ代码或lambda表达式自动生成SQL语句,开发效率很高,然而使用时如果不注意会引来性能问题。通过对不同方法使用Entity Framework进行比较,提出Entity Framework数据访问性能优化的几种方法,包括更新数据时使用无跟踪查询、通过附加数据修改和删除数据、合理使用ToList()和FirstOrDefault()、合理使用预先加载。经过性能测试,这些方法是有效的。

运用Linq to Entity快速构建信息系统结构

信息系统的层次化设计是目前主流的程序设计方法,Entity Framework是微软力推的windows平台数据访问技术,如何使用Entity Framework设计优雅、可靠的信息系统,已经成为广泛讨论的热点问题,本文就运用EF对信息系统分层以及EF在各层中的布署进行了讨论。

基于Entity Framework的防作弊称重系统的设计与实现

ORM(对象关系映射)可以较好的解决面向集合的关系型数据库与面向对象的程序设计语言之间的不匹配问题。本文应用了微软的ORM框架——Entity Framework,进行了数据库对象到应用程序对象的映射,建立了防作弊称重系统中的数据模型,使得在应用程序中能够以一种透明的方式实现数据的持久化操作。ORM技术的应用一方面使开发人员的精力集中到系统业务逻辑的实现上,另一方面大大提高了系统的开发效率。

基于asp.net mvc+entity的小区物业管理系统的设计与实现

小区物业管理具有繁杂性、多样化、管理复杂等多种特征,并且收缴费用与设备维护相对繁琐。如何有效的开展小区物业管理工作,成为当前社会关注的焦点问题之一,笔者根据这一情况,引入计算机管理信息系统,开展物业管理工作,从而使物业管理更加准确、快捷、高效、透明。

CMP2.0 Entity Bean数据库操作的应用

文章重点讨论了CMP2 .0EntityBean的一些新特性 ,并通过实例介绍了在构建CMP2 .

基于视觉与文本语义增强的多模态命名实体识别方法

为了解决视觉特征和文本特征融合后存在部分语义缺失从而导致视觉信息对文本信息的补充有较大偏差的问题,提出了一种基于视觉与文本语义增强的多模态命名实体识别方法。融合BERT文本特征提取和CLIP(contrastive language–image pre-training)视觉特征提取方法,设计了基于协同交叉注意力机制的特征交互单元,以增强视觉信息和文本信息之间的语义关系。CLIP通过对比学习框架进行预训练,优化模型以正确匹配视觉和对应的文本描述,最大化正样本(匹配的视觉-文本对)的相似性,同时最小化负样本(不匹配的视觉-文本对)的相似性。采用通用领域数据集TWITTER-2015和TWITTER-2017作为实验数据集。实验结果表明,本模型相比传统方法在多模态命名实体识别任务中的准确率、召回率、F1值均有显著提升。