Research on the Automatic Pattem Abstraction and Recognition Methodology for Large-scale Database System based on Natural Language Processing

In this research paper, we research on the automatic pattern abstraction and recognition method for large-scale database system based on natural language processing. In distributed database, through the network connection between nodes, data across different nodes and even regional distribution are well recognized. In order to reduce data redundancy and model design of the database will usually contain a lot of forms we combine the NLP theory to optimize the traditional method. The experimental analysis and simulation proves the correctness of our method.

Automated Handwriting Recognition and Speech Synthesizer for Indigenous Language Processing

In recent years,researchers in handwriting recognition analysis relating to indigenous languages have gained significant internet among research communities.The recent developments of artificial intelligence(AI),natural language processing(NLP),and computational linguistics(CL)find useful in the analysis of regional low resource languages.Automatic lexical task participation might be elaborated to various applications in the NLP.It is apparent from the availability of effective machine recognition models and open access handwritten databases.Arabic language is a commonly spoken Semitic language,and it is written with the cursive Arabic alphabet from right to left.Arabic handwritten Character Recognition(HCR)is a crucial process in optical character recognition.In this view,this paper presents effective Computational linguistics with Deep Learning based Handwriting Recognition and Speech Synthesizer(CLDL-THRSS)for Indigenous Language.The presented CLDL-THRSS model involves two stages of operations namely automated handwriting recognition and speech recognition.Firstly,the automated handwriting recognition procedure involves preprocessing,segmentation,feature extraction,and classification.Also,the Capsule Network(CapsNet)based feature extractor is employed for the recognition of handwritten Arabic characters.For optimal hyperparameter tuning,the cuckoo search(CS)optimization technique was included to tune the parameters of the CapsNet method.Besides,deep neural network with hidden Markov model(DNN-HMM)model is employed for the automatic speech synthesizer.To validate the effective performance of the proposed CLDL-THRSS model,a detailed experimental validation process takes place and investigates the outcomes interms of different measures.The experimental outcomes denoted that the CLDL-THRSS technique has demonstrated the compared methods.

Continuous Arabic Sign Language Recognition in User Dependent Mode

Arabic Sign Language recognition is an emerging field of research. Previous attempts at automatic vision-based recog-nition of Arabic Sign Language mainly focused on finger spelling and recognizing isolated gestures. In this paper we report the first continuous Arabic Sign Language by building on existing research in feature extraction and pattern recognition. The development of the presented work required collecting a continuous Arabic Sign Language database which we designed and recorded in cooperation with a sign language expert. We intend to make the collected database available for the research community. Our system which we based on spatio-temporal feature extraction and hidden Markov models has resulted in an average word recognition rate of 94%, keeping in the mind the use of a high perplex-ity vocabulary and unrestrictive grammar. We compare our proposed work against existing sign language techniques based on accumulated image difference and motion estimation. The experimental results section shows that the pro-posed work outperforms existing solutions in terms of recognition accuracy.

Generating Factual Text via Entailment Recognition Task

Generating diverse and factual text is challenging and is receiving increasing attention.By sampling from the latent space,variational autoencoder-based models have recently enhanced the diversity of generated text.However,existing research predominantly depends on summarizationmodels to offer paragraph-level semantic information for enhancing factual correctness.The challenge lies in effectively generating factual text using sentence-level variational autoencoder-based models.In this paper,a novel model called fact-aware conditional variational autoencoder is proposed to balance the factual correctness and diversity of generated text.Specifically,our model encodes the input sentences and uses them as facts to build a conditional variational autoencoder network.By training a conditional variational autoencoder network,the model is enabled to generate text based on input facts.Building upon this foundation,the input text is passed to the discriminator along with the generated text.By employing adversarial training,the model is encouraged to generate text that is indistinguishable to the discriminator,thereby enhancing the quality of the generated text.To further improve the factual correctness,inspired by the natural language inference system,the entailment recognition task is introduced to be trained together with the discriminator via multi-task learning.Moreover,based on the entailment recognition results,a penalty term is further proposed to reconstruct the loss of our model,forcing the generator to generate text consistent with the facts.Experimental results demonstrate that compared with competitivemodels,ourmodel has achieved substantial improvements in both the quality and factual correctness of the text,despite only sacrificing a small amount of diversity.Furthermore,when considering a comprehensive evaluation of diversity and quality metrics,our model has also demonstrated the best performance.

自然语言处理研究综述

近年来,自然语言处理因在分析与建模人类语言任务领域取得诸多成果而备受关注。当前,大规模预训练语言模型展现出强大的对话问答和文本生成能力,带来自然语言处理研究的新一轮热潮。自然语言处理在机器翻译、文本摘要、信息抽取等领域应用广泛。文本首先讨论自然语言处理针对语言学四个不同层次文本信息的分析手段,对自然语言处理的基本任务组成进行概述;其次,讨论自然语言处理在具体下游任务中的应用现状,包括自然语言处理在具体任务中的应用历史、当前的研究趋势以及面临的挑战;最后,在大规模预训练语言模型研究对数据集提出更高要求的背景下,对自然语言处理领域已有的数据集及评测基准集等进行讨论。

Automatic Text Summarization Using Genetic Algorithm and Repetitive Patterns

Taking into account the increasing volume of text documents,automatic summarization is one of the important tools for quick and optimal utilization of such sources.Automatic summarization is a text compression process for producing a shorter document in order to quickly access the important goals and main features of the input document.In this study,a novel method is introduced for selective text summarization using the genetic algorithm and generation of repetitive patterns.One of the important features of the proposed summarization is to identify and extract the relationship between the main features of the input text and the creation of repetitive patterns in order to produce and optimize the vector of the main document features in the production of the summary document compared to other previous methods.In this study,attempts were made to encompass all the main parameters of the summary text including unambiguous summary with the highest precision,continuity and consistency.To investigate the efficiency of the proposed algorithm,the results of the study were evaluated with respect to the precision and recall criteria.The results of the study evaluation showed the optimization the dimensions of the features and generation of a sequence of summary document sentences having the most consistency with the main goals and features of the input document.

SUBDIVIDING VERBS TO IMPROVE SYNTACTIC PARSING

This paper proposes a new way to improve the performance of dependency parser: subdividing verbs according to their grammatical functions and integrating the information of verb subclasses into lexicalized parsing model. Firstly,the scheme of verb subdivision is described. Secondly,a maximum entropy model is presented to distinguish verb subclasses. Finally,a statistical parser is developed to evaluate the verb subdivision. Experimental results indicate that the use of verb subclasses has a good influence on parsing performance.

Number Entities Recognition in Multiple Rounds of Dialogue Systems

As a representative technique in natural language processing(NLP),named entity recognition is used in many tasks,such as dialogue systems,machine translation and information extraction.In dialogue systems,there is a common case for named entity recognition,where a lot of entities are composed of numbers,and are segmented to be located in different places.For example,in multiple rounds of dialogue systems,a phone number is likely to be divided into several parts,because the phone number is usually long and is emphasized.In this paper,the entity consisting of numbers is named as number entity.The discontinuous positions of number entities result from many reasons.We find two reasons from real-world dialogue systems.The first reason is the repetitive confirmation of different components of a number entity,and the second reason is the interception of mood words.The extraction of number entities is quite useful in many tasks,such as user information completion and service requests correction.However,the existing entity extraction methods cannot extract entities consisting of discontinuous entity blocks.To address these problems,in this paper,we propose a comprehensive method for number entity recognition,which is capable of extracting number entities in multiple rounds of dialogues systems.We conduct extensive experiments on a real-world dataset,and the experimental results demonstrate the high performance of our method.

Person-specific named entity recognition using SVM with rich feature sets

Purpose: The purpose of the study is to explore the potential use of nature language process(NLP) and machine learning(ML) techniques and intents to find a feasible strategy and effective approach to fulfill the NER task for Web oriented person-specific information extraction.Design/methodology/approach: An SVM-based multi-classification approach combined with a set of rich NLP features derived from state-of-the-art NLP techniques has been proposed to fulfill the NER task. A group of experiments has been designed to investigate the influence of various NLP-based features to the performance of the system,especially the semantic features. Optimal parameter settings regarding with SVM models,including kernel functions,margin parameter of SVM model and the context window size,have been explored through experiments as well.Findings: The SVM-based multi-classification approach has been proved to be effective for the NER task. This work shows that NLP-based features are of great importance in datadriven NE recognition,particularly the semantic features. The study indicates that higher order kernel function may not be desirable for the specific classification problem in practical application. The simple linear-kernel SVM model performed better in this case. Moreover,the modified SVM models with uneven margin parameter are more common and flexible,which have been proved to solve the imbalanced data problem better.Research limitations/implications: The SVM-based approach for NER problem is only proved to be effective on limited experiment data. Further research need to be conducted on the large batch of real Web data. In addition,the performance of the NER system need be tested when incorporated into a complete IE framework.Originality/value: The specially designed experiments make it feasible to fully explore the characters of the data and obtain the optimal parameter settings for the NER task,leading to a preferable rate in recall,precision and F1measures. The overall system performance(F1value) for all types of name entities can achieve above 88.6%,which can meet the requirements for the practical application.

Dart Games Optimizer with Deep Learning-Based Computational Linguistics Named Entity Recognition

Computational linguistics is an engineering-based scientific discipline.It deals with understanding written and spoken language from a computational viewpoint.Further,the domain also helps construct the artefacts that are useful in processing and producing a language either in bulk or in a dialogue setting.Named Entity Recognition(NER)is a fundamental task in the data extraction process.It concentrates on identifying and labelling the atomic components from several texts grouped under different entities,such as organizations,people,places,and times.Further,the NER mechanism identifies and removes more types of entities as per the requirements.The significance of the NER mechanism has been well-established in Natural Language Processing(NLP)tasks,and various research investigations have been conducted to develop novel NER methods.The conventional ways of managing the tasks range from rule-related and hand-crafted feature-related Machine Learning(ML)techniques to Deep Learning(DL)techniques.In this aspect,the current study introduces a novel Dart Games Optimizer with Hybrid Deep Learning-Driven Computational Linguistics(DGOHDL-CL)model for NER.The presented DGOHDL-CL technique aims to determine and label the atomic components from several texts as a collection of the named entities.In the presented DGOHDL-CL technique,the word embed-ding process is executed at the initial stage with the help of the word2vec model.For the NER mechanism,the Convolutional Gated Recurrent Unit(CGRU)model is employed in this work.At last,the DGO technique is used as a hyperparameter tuning strategy for the CGRU algorithm to boost the NER’s outcomes.No earlier studies integrated the DGO mechanism with the CGRU model for NER.To exhibit the superiority of the proposed DGOHDL-CL technique,a widespread simulation analysis was executed on two datasets,CoNLL-2003 and OntoNotes 5.0.The experimental outcomes establish the promising performance of the DGOHDL-CL technique over other models.

语义和句法双增强的交互式方面级情感分析

针对目前研究存在的语义和句法信息提取不充分以及忽略二者交互的问题,提出一种利用交互注意力机制融合语义和句法信息的方面级情感分析模型。将方面间依赖关系与局部语义融合,获得综合的全局语义信息,将全局与局部信息进行交互得到更深层的语义信息;利用改进的图卷积网络增强模型提取上下文句法信息的能力;使用多头交互注意力完成方面词与上下文之间以及增强语义和句法之间的交互。为验证模型的有效性,在Laptop14、Restaurat14和Twitter基准数据集上进行实验,实验结果表明,所提模型取得的性能优于比较方法。

中文命名实体识别研究综述

命名实体识别(named entity recognition,NER)是自然语言处理中最基本的任务之一,其主要内容是识别自然语言文本中具有特定意义的实体类型和边界。然而,中文命名实体识别(Chinese named entity recognition,CNER)的数据样本存在词边界模糊、语义多样化、形态特征模糊以及中文语料库内容较少等问题,导致中文命名实体识别性能难以大幅提升。介绍了CNER的数据集、标注方案和评价指标。按照CNER的研究进程,将CNER方法分为基于规则的方法、基于统计的方法和基于深度学习的方法三类,并对近五年来基于深度学习的CNER主要模型进行总结。探讨CNER的研究趋势,为新方法的提出和未来研究方向提供一定参考。

基于词性和语序分析的法律知识图谱自动构建方法

文章挖掘法律文本中的实体和关系,构建法律知识图谱,为实现智能判案提供支持,完善法律知识图谱的构建方法。应用基于LexNLP的自然语言处理方法,分析法律文本,以句子为单位进行词性分析,标注出名词且为主语或宾语时作为实体,动词且为谓语时标注为关系。在这一基础上,将同一个句子中的实体和关系按照<实体1,关系,实体2>进行排列组合,生成不重复的知识三元组,以生成高质量的法律知识图谱。提出了一种基于词性和语序分析的法律知识图谱自动构建方法,并基于美国Caselaw Access Project项目所含的法律判例为原始数据,并对生成三元组进行质量评估,最后生成了关于法律的知识图谱。

面向淋巴水肿疾病的电子病历命名实体识别应用研究

目的/意义探讨人工智能技术应用于淋巴水肿患者电子病历非结构化文本数据的关键实体识别问题。方法/过程阐述样本稀缺背景下模型微调训练的解决方案,选取首都医科大学附属北京世纪坛医院淋巴外科既往收治患者594例为研究对象,依据临床医生标注的15种关键实体类别,微调GlobalPointer模型的预测层,借助其全局指针识别嵌套和非嵌套的关键实体。分析实验结果的准确性和临床应用可行性。结果/结论微调后模型总体精准率、召回率和Macro_F1均值分别为0.795、0.641和0.697,为淋巴水肿电子病历数据精准挖掘奠定基础。

融合多粒度语言知识与层级信息的中文命名实体识别模型

针对当前大多数命名实体识别(NER)模型只使用字符级信息编码且缺乏对文本层次信息提取的问题,提出一种融合多粒度语言知识与层级信息的中文NER(CNER)模型(CMH)。首先,使用经过多粒度语言知识预训练的模型编码文本,使模型能够同时捕获文本的细粒度和粗粒度语言信息,从而更好地表征语料;其次,使用ON-LSTM(Ordered Neurons Long Short-Term Memory network)模型提取层级信息,利用文本本身的层级结构信息增强编码间的时序关系;最后,在模型的解码端结合文本的分词信息,并将实体识别问题转化为表格填充问题,以更好地解决实体重叠问题并获得更准确的实体识别结果。同时,为解决当前模型在不同领域中的迁移能力较差的问题,提出通用实体识别的理念,通过筛选多领域的通用实体类型,构建一套提升模型在多领域中的泛化能力的通用NER数据集MDNER(Multi-Domain NER dataset)。为验证所提模型的效果,在数据集Resume、Weibo、MSRA上进行实验,与MECT(Multi-metadata Embedding based Cross-Transformer)模型相比,F1值分别提高了0.94、4.95和1.58个百分点。为了验证所提模型在多领域中的实体识别效果,在MDNER上进行实验,F1值达到了95.29%。实验结果表明,多粒度语言知识预训练、文本层级结构信息提取和高效指针解码器对模型的性能提升至关重要。

人工智能数据采集在慢性乙型肝炎患者真实世界研究中的应用

目的:开发一套慢性乙型肝炎(乙肝)的人工智能(AI)数据采集工具,以解决传统的多中心数据采集效率低下的问题。方法:基于国际通用的数据标准,将AI的文字识别和自然语言处理应用于慢性乙肝真实世界队列研究数据采集,实现多种格式数据(包括图片格式的原始数据)的电子化采集、结构化处理,随后自动将数据填入研究电子数据采集(REDCap)系统中的电子病历报告表(eCRF)。结果:AI工具辅助数据采集与纯人工数据采集具有相同的平均准确率,均达到98.66%(P=0.23),但前者所需时间减少75.49%(P<0.05)。结论:本研究开发的AI数据采集工具可显著提高研究数据采集效率,为真实世界研究数据采集提供了新的模式。

自然语言处理在医疗设备采购参数制订中的应用价值研究

目的:构建智能化医疗设备采购参数生成系统,实现医疗设备采购参数制订表达清晰和需求匹配精准,提高招标结果的认可度和招标效率。方法:基于自然语言处理(NLP)、网络爬虫和机器学习方法,构建自动化数据更新机制,实现海量采购参数数据提取,并将实体识别方法用于既往采购参数数据分析,实现医疗设备信息及参数名称等实体自动化提取,基于相似性设备推荐及医疗设备模板派生方法,采用向导式交互工具构建智能化医疗设备采购参数生成系统。对比采用智能化医疗设备采购参数生成系统与4名具有3年采购经验的招标采购人员进行10份医疗设备采购参数文件制订的差异。结果:采用智能化医疗设备采购参数生成系统的医疗设备采购参数文件平均生成时长为15.23min,而招标采购人员制订医疗设备采购参数文件平均时长为173.40min。经招标采购专家评估,采用智能化医疗设备采购参数生成系统生成医疗设备采购参数文件效率及质量均优于3年采购经验招标采购人员制订的医疗设备采购参数文件。结论:智能化医疗设备采购参数生成系统应用于医疗设备采购参数制订,可实现医疗设备采购参数的专业信息采集、存储和管理,缩短医疗设备采购参数制订周期,为医疗设备招标采购从业人员提供智能化辅助生成工具,提高采购参数制订效能,提升医疗设备采购效率。

基于字词融合的低词汇信息损失中文命名实体识别方法

中文命名实体识别(CNER)任务是一种自然语言处理技术,旨在识别文本中具有特定类别的实体,如人名、地名、组织机构名等,它是问答系统、机器翻译、信息抽取等自然语言应用的基础底层任务。由于中文不具备类似英文这样的天然分词结构,基于词的NER模型在中文命名实体识别上的效果会因分词错误而显著降低,基于字符的NER模型又忽略了词汇信息的作用,因此,近年来许多研究开始尝试将词汇信息融入字符模型中。WC-LSTM通过在词汇的开始字符和结束字符中注入词汇信息,使模型性能获得了显著的提升。然而,该模型依然没有充分利用词汇信息,因此在其基础上提出了基于字词融合的低词汇信息损失NER模型LLL-WCM,对词汇的所有中间字符融入词汇信息,避免了词汇信息损失。同时,引入了两种编码策略平均(avg)和自注意力机制(self-attention)以提取所有词汇信息。在4个中文数据集上进行实验,结果表明,与WC-LSTM相比,该方法的F1值分别提升了1.89%,0.29%,1.10%和1.54%。

融合文本摘要和情绪感知的抑郁倾向识别

抑郁症作为世界第四大疾病,严重影响着人们的生理和心理健康。随着互联网的发展,社交媒体的发布内容已经成为研究精神疾病的重要数据源,研究者开始应用自然语言处理技术自动检测抑郁倾向。现存算法无法充分捕捉到长文本中的关键信息,忽略了对用户情绪状态的时序性建模,进而造成抑郁倾向识别性能不佳。该文提出一种融合文本摘要和情绪感知的抑郁倾向识别模型,首先利用文本摘要算法抽取用户历史文本的全局语义特征,在压缩文本长度的同时保留了与用户真实意图强相关的内容;然后利用词汇增强算法计算句子级的细粒度情绪表示,并结合深度神经网络捕获了用户的情绪变化特征。实验结果表明,该文提出的模型取得了更佳的识别效果,在抑郁倾向识别数据集上将检测结果的正类F 1值提升至75.61%。

面向自然语言需求的验证性质生成方法

安全关键系统和软件的安全性、可靠性需要形式化验证来保障,使用形式化验证的前提是从自然语言需求文本中提取相关验证性质并将其转化为形式化规约,这已成为当前形式化验证领域研究的热点和难点.当前的形式化规约提取工作大多针对英文需求,较少针对中文自然语言需求.此外,由于AADL具有强大的表达能力和完善的验证机制,已成为航空航天领域的主要建模语言之一,而现有的工作较少考虑如何从需求中提取AADL模型的验证性质.为了解决上述问题,本文提出一种面向自然语言需求的AADL模型验证性质自动生成方法,从自然语言需求中提取验证的相关性质,并将其转化为AADL模型验证工具AGREE可识别的形式化规约.首先,定义了模式定义语言(Contract Pattern Language,CPL),将需求划分为不同模式,并给出由固定句型和占位符组成的需求模板;其次,通过自然语言处理技术解析需求文本,获取替换需求模板中占位符的原子命题,以便生成完整的形式化规约;最后,设计并实现了相关工具,并将其用于工业界实际案例来说明该方法的可用性和有效性.