Power entity recognition based on bidirectional long short-term memory and conditional random fields

With the application of artificial intelligence technology in the power industry,the knowledge graph is expected to play a key role in power grid dispatch processes,intelligent maintenance,and customer service response provision.Knowledge graphs are usually constructed based on entity recognition.Specifically,based on the mining of entity attributes and relationships,domain knowledge graphs can be constructed through knowledge fusion.In this work,the entities and characteristics of power entity recognition are analyzed,the mechanism of entity recognition is clarified,and entity recognition techniques are analyzed in the context of the power domain.Power entity recognition based on the conditional random fields (CRF) and bidirectional long short-term memory (BLSTM) models is investigated,and the two methods are comparatively analyzed.The results indicated that the CRF model,with an accuracy of 83%,can better identify the power entities compared to the BLSTM.The CRF approach can thus be applied to the entity extraction for knowledge graph construction in the power field.

A CONDITIONAL RANDOM FIELDS APPROACH TO BIOMEDICAL NAMED ENTITY RECOGNITION

Named entity recognition is a fundamental task in biomedical data mining. In this letter, a named entity recognition system based on CRFs (Conditional Random Fields) for biomedical texts is presented. The system makes extensive use of a diverse set of features, including local features, full text features and external resource features. All features incorporated in this system are described in detail, and the impacts of different feature sets on the performance of the system are evaluated. In order to improve the performance of system, post-processing modules are exploited to deal with the abbrevia- tion phenomena, cascaded named entity and boundary errors identification. Evaluation on this system proved that the feature selection has important impact on the system performance, and the post-processing explored has an important contribution on system performance to achieve better re- sults.

Standardization of Robot Instruction Elements Based on Conditional Random Fields and Word Embeddin

Natural language processing has got great progress recently. Controlling robots with spoken natural language has become expectable. With the reliability problem of this kind of control in mind a confirmation process of natural language instruction should be included before carried out by the robot autonomously and the prototype dialog system was designed thus the standardization problem was raised for the natural and understandable language interaction. In the application background of remotely navigating a mobile robot inside a building with Chinese natural spoken language considering that as an important navigation element in instructions a place name can be expressed with different lexical terms in spoken language this paper proposes a model for substituting different alternatives of a place name with a standard one (called standardization). First a CRF (Conditional Random Fields) model is trained to label the term required be standardized then a trained word embedding model is to represent lexical terms as digital vectors. In the vector space similarity of lexical terms is defined and used to find out the most similar one to the term picked out to be standardized. Experiments show that the method proposed works well and the dialog system responses to confirm the instructions are natural and understandable.

Rockhead profile simulation using an improved generation method of conditional random field

Rockhead profile is an important part of geological profiles and can have significant impacts on some geotechnical engineering practice,and thus,it is necessary to establish a useful method to reverse the rockhead profile using site investigation results.As a general method to reflect the spatial distribution of geo-material properties based on field measurements,the conditional random field(CRF)was improved in this paper to simulate rockhead profiles.Besides,in geotechnical engineering practice,measurements are generally limited due to the limitations of budget and time so that the estimation of the mean value can have uncertainty to some extent.As the Bayesian theory can effectively combine the measurements and prior information to deal with uncertainty,CRF was implemented with the aid of the Bayesian framework in this study.More importantly,this simulation procedure is achieved as an analytical solution to avoid the time-consuming sampling work.The results show that the proposed method can provide a reasonable estimation about the rockhead depth at various locations against measurement data and as a result,the subjectivity in determining prior mean can be minimized.Finally,both the measurement data and selection of hyper-parameters in the proposed method can affect the simulated rockhead profiles,while the influence of the latter is less significant than that of the former.

Conditional Random Field Tracking Model Based on a Visual Long Short Term Memory Network

In dense pedestrian tracking,frequent object occlusions and close distances between objects cause difficulty when accurately estimating object trajectories.In this study,a conditional random field tracking model is established by using a visual long short term memory network in the three-dimensional(3D)space and the motion estimations jointly performed on object trajectory segments.Object visual field information is added to the long short term memory network to improve the accuracy of the motion related object pair selection and motion estimation.To address the uncertainty of the length and interval of trajectory segments,a multimode long short term memory network is proposed for the object motion estimation.The tracking performance is evaluated using the PETS2009 dataset.The experimental results show that the proposed method achieves better performance than the tracking methods based on the independent motion estimation.

Exploiting PLSA model and conditional random field for refining image annotation

This paper presents a new method for refining image annotation by integrating probabilistic latent semantic analysis(PLSA) with conditional random field(CRF).First a PLSA model with asymmetric modalities is constructed to predict a candidate set of annotations with confidence scores,and then model semantic relationship among the candidate annotations by leveraging conditional random field.In CRF,the confidence scores generated by the PLSA model and the Flickr distance between pairwise candidate annotations are considered as local evidences and contextual potentials respectively.The novelty of our method mainly lies in two aspects:exploiting PLSA to predict a candidate set of annotations with confidence scores as well as CRF to further explore the semantic context among candidate annotations for precise image annotation.To demonstrate the effectiveness of the method proposed in this paper,an experiment is conducted on the standard Corel dataset and its results are compared favorably with several state-of-the-art approaches.

融合多尺度CNN和CRF的通用细粒度事件检测

事件检测是自然语言处理领域中事件抽取的主要任务之一,它旨在从众多非结构化信息中自动提取出结构化的关键信息.现有的方法存在特征提取不全面、特征分布不均等情况.为了提高事件检测的准确率,提出了一种融合BERT预训练模型与多尺度CNN的神经网络模型(BMCC,BERT+Multi-scale CNN+CRF).首先通过BERT(Bidirectional Encoder Representations from Transformers)预训练模型来进行词向量的嵌入,并利用其双向训练的Transformer机制来提取序列的状态特征;其次使用不同尺度的卷积核在多个卷积通道中进行卷积训练,以此来提取不同视野的语义信息,丰富其语义表征.最后将BIO机制融入到条件随机场(CRF)来对序列进行标注,实现事件的检测.实验结果表明,所提出的模型在MAVEN数据集上的F1值为65.17%,表现了该模型的良好性能.

基于ResUNet和Dense CRF模型的地震裂缝识别方法

针对人工解释地震资料耗时长、效率低、受主观因素影响较大的不足,提出了一种基于ResUNet和全连接条件随机场(dense conditional random field, Dense CRF)模型的裂缝识别方法。该方法首先使用ResUNet模型提取地震振幅数据体中裂缝的不同分辨率的特征,实现地震裂缝识别;然后利用Dense CRF模型进一步优化识别结果,从而实现地震裂缝的精准识别。将该方法与传统UNet、ResUNet模型在合成地震振幅数据体和F3工区地震数据体进行了实验比较,结果表明运用所提方法识别的裂缝更准确、裂缝尺寸更细、连续性更好。

基于ERNIE-BiGRU-Attention-CRF的电子病历命名实体识别方法

目的/意义改善中文电子病历命名实体识别模型的性能,更好地开展医疗信息的组织和挖掘。方法/过程构建ERNIE-BiGRU-Attention-CRF中文电子病历命名实体识别模型,首先采用ERNIE1.0预训练模型生成具有语义特征的词向量,然后利用BiGRU捕获全局语义特征与语法结构特征,通过Attention机制进一步增强语义特征的捕获,最后连接CRF解码层输出全局概率最大的标签序列。结果/结论在公开的医疗文本数据集CCKS2017开展对比实验、消融实验,利用生成的模型进行实例分析,取得较好的识别效果。

基于BERT-BiLSTM-CRF模型的油气领域命名实体识别

针对油气领域知识图谱构建过程中命名实体识别使用传统方法存在实体特征信息提取不准确、识别效率低的问题,提出了一种基于BERT-BiLSTM-CRF模型的命名实体识别研究方法。该方法首先利用BERT(bidirectional encoder representations from transformers)预训练模型得到输入序列语义的词向量;然后将训练后的词向量输入双向长短期记忆网络(bi-directional long short-term memory,BiLSTM)模型进一步获取上下文特征;最后根据条件随机场(conditional random fields,CRF)的标注规则和序列解码能力输出最大概率序列标注结果,构建油气领域命名实体识别模型框架。将BERT-BiLSTM-CRF模型与其他2种命名实体识别模型(BiLSTM-CRF、BiLSTM-Attention-CRF)在包括3万多条文本语料数据、4类实体的自建数据集上进行了对比实验。实验结果表明,BERT-BiLSTM-CRF模型的准确率(P)、召回率(R)和F_(1)值分别达到91.3%、94.5%和92.9%,实体识别效果优于其他2种模型。

基于条件随机场(CRFs)的中文词性标注方法

本文提出一种基于CRFs模型的中文词性标注方法。该方法利用CRFs模型能够添加任意特征的优点,在使用词的上下文信息的同时,针对兼类词和未登录词添加了新的统计特征。在《人民日报》1月份语料库上进行的封闭测试和开放测试中,该方法的标注准确率分别为98.56%和96.60%。

SparkCRF：一种基于Spark的并行CRFs算法实现

条件随机场(condition random fields,CRFs)可用于解决各种文本分析问题,如自然语言处理(natural language processing,NLP)中的序列标记、中文分词、命名实体识别、实体间关系抽取等.传统的运行在单节点上的条件随机场在处理大规模文本时,面临一系列挑战.一方面,个人计算机遇到处理的瓶颈从而难以胜任;另一方面,服务器执行效率较低.而通过升级服务器的硬件配置来提高其计算能力的方法,在处理大规模的文本分析任务时,终究不能从根本上解决问题.为此,采用"分而治之"的思想,基于Apache Spark的大数据处理框架设计并实现了运行在集群环境下的分布式CRFs——SparkCRF.实验表明,SparkCRF在文本分析任务中,具有高效的计算能力和较好的扩展性,并且具有与传统的单节点CRF++相同水平的准确率.

基于CRFs模型的敏感话题识别研究

条件随机场(CRFs)是一种判别式概率无向图学习模型,将其引入敏感话题识别中,提出了基于CRFs模型的敏感话题识别方法。将随机挑选出的一篇待检测文本s和剩余的待检测文本分别作为CRFs模型的观察序列和状态序列来计算文本s和其余待检测文本间的相关性概率值;然后将相关性最高的那篇文本和文本s合并表征一个类别;同时,将相关性最低的那篇文本作为另一个类别,将这两个类别作为CRFs模型新的状态序列,剩余的待检测文本作为新的观察序列进行迭代,据此实现敏感话题的识别。在数据集上进行的实验中,该方法的耗费函数的值为0.01943,宏平均F度量的值为0.8235,都取得了很好的效果。

基于CRFs边缘概率的中文分词

将分词问题转化为序列标注问题,使用CRFs标注器进行序列标注是近年来广泛采用的分词方法。针对这一方法中CRFs的标记错误问题,该文提出基于CRFs边缘概率的分词方法。该方法从标注结果中发掘边缘概率高的候选词,重组边缘概率低的候选词,提出FMM的奖励机制修正重组后的子串。在第四届SIGHAN Bakeoff中文简体语料SXU和NCC上进行闭式测试,分别在F-1值上达到了96.41%和94.30%的精度。

分布式策略与CRFs相结合识别汉语组块

该文提出了一种基于CRFs的分布式策略及错误驱动的方法识别汉语组块。该方法首先将11种类型的汉语组块进行分组,结合CRFs构建不同的组块识别模型来识别组块;之后利用基于CRFs的错误驱动技术自动对分组组块进行二次识别;最后依据各分组F值大小顺序处理类型冲突。实验结果表明,基于CRFs的分布式策略及错误驱动方法识别汉语组块是有效的,系统开放式测试的精确率、召回率、F值分别达到94.90%、91.00%和92.91%,好于单独的CRFs方法、分布式策略方法及其他组合方法。

基于CRFs和MPCP特征的和弦识别研究

和弦识别是音乐调式分析和自动标注的基础,同时在分析音乐的结构和旋律方面有着非常重要的作用。结合音乐理论和信号处理知识,提出一种基于MPCP(Mel Pitch Class Profile)特征和CRFs(Conditional Random Fields)模型的和弦识别方法。利用短时傅里叶变换(STFT)对音乐信号进行时频变换,定义了一种新的MPCP特征,最后用CRFs对和弦进行识别。实验结果表明,提出的方法在识别率上优于其他方法,具有一定的潜力。

基于改进Bi-LSTM-CRF的农业问答系统研究

针对农业领域问答系统面临的实体识别困难的问题,提出一种基于改进Bi-LSTM-CRF的实体识别方法。首先通过BERT预训练模型的预处理,生成基于上下文信息的词向量,然后将训练出的词向量输入Bi-LSTM-CRF做进一步的训练处理,最后,利用Python的Django框架设计农业领域的实体识别、实体查询、农知问答等子系统。经过试验对比,所提出的改进的Bi-LSTM-CRF在农业信息领域具有更好的实体识别能力,在农业信息语料库上的精确率、召回率和F1值分别为93.23%、91.08%和92.16%。实现农业领域实体识别和农业信息问答的知识图谱网站演示,对农业信息化的发展具有重要意义。

基于CRFs和歧义模型的越南语分词

通过对越南语词法特点的研究,把越南语的基本特征融入到条件随机场中(Condition random fields,CRFs),提出了一种基于CRFs和歧义模型的越南语分词方法。通过机器标注、人工校对的方式获取了25 981条越南语分词语料作为CRFs的训练语料。越南语中交叉歧义广泛分布在句子中,为了克服交叉歧义的影响,通过词典的正向和逆向匹配算法从训练语料中抽取了5 377条歧义片段,并通过最大熵模型训练得到一个歧义模型,并融入到分词模型中。把训练语料均分为10份做交叉验证实验,分词准确率达到了96.55%。与已有越南语分词工具VnTokenizer比较,实验结果表明该方法提高了越南语分词的准确率、召回率和F值。

基于CRFs和领域规则的业务名称识别

提出一种基于条件随机场(CRFs)和领域规则的业务名称识别方法。通过实验词及词性的不同组合选择特征集合,由该特征训练得到CRFs模型,利用该模型测试得到业务术语,采用2-gram及编辑距离2种度量方式进行相似度计算,利用领域规则和相似度计算方法得到业务名称。实验结果证明了该方法的有效性。

基于BERT-BILSTM-CRF模型的电力行业事故文本智能分析

为解决电力行业事故报告文本较长、语义复杂,难以进行有效文本识别问题,提出1种以BERT作为底层的预训练模型,并设计1种双重注意力机制编码器,结合BILSTM-CRF深度挖掘事故文本语义特征,从而实现文本智能分析。首先构建电力词典,通过对BERT预训练,进行BIO标注,然后引入BILSTM-CRF模型实现对文本标签智能分类,最后将该模型与现行其他4种深度学习模型进行对比。研究结果表明:该模型智能识别精确率、召回率及F 1值(查准率)均达到约97%,较其他4种模型中效果最好的模型分别提高0.02,0.03,0.02。研究结果可为电力行业事故报告文本分析提供1种新思路。