基于跨度和特征融合的实体关系联合抽取模型

实体关系联合抽取模型在实体关系抽取中具有重要作用,针对现有的实体关系联合抽取模型无法有效识别重叠关系中的实体关系三元组问题,提出一种新型的基于跨度和特征融合的实体关系联合抽取模型SFFM。将文本输入BERT预训练模型转变为词向量,根据跨度进行词向量划分形成跨度序列,并基于卷积神经网络过滤跨度序列中不包含实体的跨度序列,使用双向长短时记忆提取剩余跨度序列融合文本信息后的特征并通过Softmax回归实现实体识别,将文本中的实体和关系映射到不同的跨度序列中,当重叠关系中的实体和距离较远的实体之间存在关系时,按照跨度进行划分使可能存在关系的实体对划分到同一个跨度序列中,以更好地利用文本中的重叠关系。在此基础上,通过注意力机制获取跨度序列中的依赖关系,运用Softmax回归对跨度序列中的关系进行分类。实验结果表明,与基线模型相比,该模型在CoNLL04数据集上的微平均和宏平均分别提升了1.87和1.73个百分点,在SciERC数据集上的微平均提升了5.95个百分点。     

基于双层注意力循环神经网络的方面级情感分析

在方面级情感分析中,常用的方法是将循环神经网络和注意力机制结合,利用注意力获取序列中不同单词的重要程度,但这并不能获取不同句子的重要程度,仅仅依赖单层注意力难于获取深层次情感特征信息。为了解决上述问题,该文提出一种基于双层注意力循环神经网络模型。通过双层注意力分别对单词层和句子层进行建模,捕获不同单词和不同句子的重要性,利用双向的循环神经网络获取更多的序列上下文依赖信息。情感特征信息具有重要性,但在深层神经网络中却未被充分利用。因此,该文将方面、词性信息和位置信息作为模型的辅助信息,进行更深层次情感特征信息学习,有效识别不同方面的情感极性。该方法与IAN模型相比,在SemEval 2014中的Restaurant数据集和Laptop数据集上进行实验,分类准确率分别提升了2.0%和5.2%。在与TD-LSTM模型对比中,Twitter数据集的分类准确率提升了1.7%。     

基于ALBERT-BGRU-CRF的中文命名实体识别方法

命名实体识别是知识图谱构建、搜索引擎、推荐系统等上层自然语言处理任务的重要基础,中文命名实体识别是对一段文本序列中的专有名词或特定命名实体进行标注分类。针对现有中文命名实体识别方法无法有效提取长距离语义信息及解决一词多义的问题,提出一种基于ALBERT-双向门控循环单元（BGRU）-条件随机场（CRF）模型的中文命名实体识别方法。使用ALBERT预训练语言模型对输入文本进行词嵌入获取动态词向量,有效解决了一词多义的问题。采用BGRU提取上下文语义特征进一步理解语义,获取长距离词之间的语义特征。将拼接后的向量输入至CRF层并利用维特比算法解码,降低错误标签输出概率。最终得到实体标注信息,实现中文命名实体识别。实验结果表明,ALBERT-BGRU-CRF模型在MSRA语料库上的中文命名实体识别准确率和召回率分别达到95.16%和94.58%,同时相比于片段神经网络模型和CNN-BiLSTM-CRF模型的F1值提升了4.43和3.78个百分点。     

基于滑动窗口注意力网络的关系分类模型

实体关系抽取是构建知识图谱过程中至关重要的一步。将注意力机制引入卷积神经网络或循环神经网络是目前关系抽取任务中比较主流的解决方法,谷歌最新提出的BERT模型在多项自然语言处理任务中都取得了非常好的效果。为了充分融合局部信息和全局信息,并提高处理效率,该文提出了滑动窗口注意力网络模型(Sliding Window Attention Network, SWAN)。该模型首先通过预训练的word2vec生成词向量,加入位置表示并使用TransE模型对实体进行表征以充分突出实体信息,再采用基于BERT的SBERT模型对句子进行表征,在此基础上采用多种滑动窗口注意力机制捕获局部信息,然后在聚集层对抽取到的局部信息进行聚合,最后利用softmax函数来实现实体关系的分类。实验结果表明,提出的SWAN模型在SemEval2010 Task 8数据集上取得了较高的准确率,优于对比的现有关系抽取模型,同时模型训练效率也得到极大提升。     

融合深度BiGRU与全局图卷积的方面级情感分析模型

现有基于深度学习的方面级情感分析模型需要考虑如何提取深层次的语义信息,其次通过依存树提取句法结构时可能存在信息丢失与数据稀疏问题.针对以上问题,本文提出了基于深度双向门控循环单元与全局双向图卷积网络的神经网络模型(DBG-GBGCN).该模型通过深度双向门控循环单元捕获深层次的语义特征,得到上下文的隐层表示.然后将依存树的邻接矩阵转变为带有全局句法信息的全局矩阵,将此矩阵与上下文的隐层表示一起输入至双向图卷积网络进行特征融合,最后经过掩码层和注意力层得到一个包含深层语义特征与句法结构信息结合的分类特征.实验结果证明,该模型在5个公开数据集上的准确率与F1值均比对比模型有着一定的提升.     

融合语义信息与注意力的图神经网络推荐算法

基于图神经网络的推荐算法通过从图中获取知识生成节点的特征表示,提高了推荐结果的可解释性.然而,随着推荐系统原始数据规模的不断扩大,大量包含语义信息的文本数据没有得到有效利用.同时图神经网络在融合图中邻居信息时没有区分关键节点,使得模型难以学习到高质量的实体特征,进而导致推荐质量下降.本文将图神经网络与语义模型相结合,提出一种融合语义信息与注意力的图神经网络推荐算法.该算法基于SpanBERT语义模型处理实体相关的文本信息,生成包含语义信息的特征嵌入,并将注意力机制引入到基于用户社交关系以及用户-项目交互的影响传播融合过程中,从而实现用户和项目两类实体特征的有效更新.在公开数据集上的对比实验结果表明,本文所提出的方法较现有基准方法在各项指标上均有所提升.     

基于卷积神经网络的中文医疗弱监督关系抽取

随着医疗领域受到越来越多的关注,自然语言处理的理论和应用逐渐拓展到该领域,其中信息抽取技术在该领域的应用成为研究热点。针对信息抽取技术在医疗领域实体关系抽取中的应用,提出一种基于卷积神经网络的弱监督关系抽取方法。该方法通过添加人工规则使训练语料带有实体关系标签,然后将该弱关系训练语料转换为向量特征矩阵,并输入到卷积神经网络进行分类模型训练,最终实现实体关系抽取。实验结果表明,该方法比常规机器学习方法更加准确高效。     

基于循环卷积神经网络的实体关系抽取方法研究

针对目前大多数关系抽取中对于文本语料中较长的实体共现句,往往只能获取到局部的特征,并不能学习到长距离依赖信息的问题,提出了一种基于循环卷积神经网络与注意力机制的实体关系抽取模型。将擅长处理远距离依赖关系的循环神经网络GRU加入到卷积神经网络的向量表示阶段,通过双向GRU学习得到词语的上下文信息向量,在卷积神经网络的池化层采取分段最大池化方法,在获取实体对结构信息的同时,提取更细粒度的特征信息,同时在模型中加入基于句子级别的注意力机制。在NYT数据集的实验结果表明提出方法能有效提高实体关系抽取的准确率与召回率。     

基于自注意力的协同演进推荐

为解决时序模型不能有效获取用户和项目交互序列的语义关系,以及因参数共享而导致的信息丢失问题,提出基于自注意力的协同演进推荐模型(BSFRNN).将循环神经网络提取的序列特征和自注意力机制提取的语义特征进行融合表征用户以及项目的短期特征,将矩阵分解描述的长期特征和短期时序特征进行融合,将融合的特征向量通过多层感知机进行预...     

基于多视角学习的时序多模态情感分类研究

针对多模态融合效果不佳,不能充分挖掘特定时间段,多视角关键情感信息的问题,提出了一种基于多视角的时序多模态情感分类模型,用于提取特定时间段,多视角下的关键情感信息。首先,对文本标题及文本内容两种视角下的数据进行低维空间词嵌入和序列表达,提取不同视角的多模态时序特征,对图片截取,水平镜像两种视角下的数据进行特征提取;其次,采用循环神经网络构建多模态数据的时序序列交互特征,增大互信息;最后,基于对比学习进行联合训练,完成情感分类。该模型在两个多模态情感分类基准数据集Yelp和Mutli-Zol上评估,准确度分别为73.92%、69.15%。综合实验表明,多视角的特定时间段多模态语句序列可提升模型性能。     

Only connect: teaching, technology and telesis

Abstract This paper describes an approach to the design of interactive multimedia materials being developed in a European Community project. The developmental process is seen as a dialogue between technologists and teachers. This dialogue is often problematic because of the differences in training, experience and culture between them. Conditions needed for fruitful dialogue are described and the generic model for learning design used in the project is explained.     

European Community policy and the market

Abstract This paper starts with some reflections on the policy considerations and priorities which are shaping European Commission (EC) research programmes. Then it attempts to position the current projects which seek to capitalise on information and communications technologies for learning in relation to these priorities and the apparent realities of the marketplace. It concludes that while there are grounds to be optimistic about the contribution EC programmes can make to the efficiency and standard of education and training, they are still too technology driven.     

一种自适应子融合集成多分类器方法

融合集成方法已经广泛应用在模式识别领域,然而一些基分类器实时性能稳定性较差,导致多分类器融合性能差,针对上述问题本文提出了一种新的基于多分类器的子融合集成分类器系统。该方法考虑在度量层融合层次之上通过对各类基多分类器进行动态选择,票数最多的类别作为融合系统中对特征向量识别的类别,构成一种新的自适应子融合集成分类器方法。实验表明,该方法比传统的分类器以及分类融合方法识别准确率明显更高,具有更好的鲁棒性。     

Avoiding semantic and temporal gaps in developing software intensive systems

Development of software intensive systems (systems) in practice involves a series of self-contained phases for the lifecycle of a system. Semantic and temporal gaps, which occur among phases and among developer disciplines within and across phases, hinder the ongoing development of a system because of the interdependencies among phases and among disciplines. Such gaps are magnified among systems that are developed at different times by different development teams, which may limit reuse of artifacts of systems development and interoperability among the systems. This article discusses such gaps and a systems development process for avoiding them.     

Designing economic np control charts: A programmed simulation approach

This paper presents control charts models and the necessary simulation software for the location of economic values of the control parameters. The simulation program is written in FORTRAN, requires only 10K of main storage, and can run on most mini and micro computers. Two models are presented - one describes the process when it is operating at full capacity and the other when the process is operating under capacity. The models allow the product quality to deteriorate to a further level before an existing out-of-control state is detected, and they can also be used in situations where no prior knowledge exists of the out-of-control causes and the resulting proportion defectives.     

The development of robot art

Going through a few examples of robot artists who are recognized worldwide, we try to analyze the deepest meaning of what is called “robot art” and the related art field definition. We also try to highlight its well-marked borders, such as kinetic sculptures, kinetic art, cyber art, and cyberpunk. A brief excursion into the importance of the context, the message, and its semiotics is also provided, case by case, together with a few hints on the history of this discipline in the light of an artistic perspective. Therefore, the aim of this article is to try to summarize the main characteristics that might classify robot art as a unique and innovative discipline, and to track down some of the principles by which a robotic artifact can or cannot be considered an art piece in terms of social, cultural, and strictly artistic interest. This work was presented in part at the 13th International Symposium on Artificial Life and Robotics, Oita, Japan, January 31–February 2, 2008     

Explanation and prediction: an architecture for default and abductive reasoning

Although there are many arguments that logic is an appropriate tool for artificial intelligence, there has been a perceived problem with the monotonicity of classical logic. This paper elaborates on the idea that reasoning should be viewed as theory formation where logic tells us the consequences of our assumptions. The two activities of predicting what is expected to be true and explaining observations are considered in a simple theory formation framework. Properties of each activity are discussed, along with a number of proposals as to what should be predicted or accepted as reasonable explanations. An architecture is proposed to combine explanation and prediction into one coherent framework. Algorithms used to implement the system as well as examples from a running implementation are given.     

Three Process Perspectives: Organizations, Teams, and People

This paper provides the author's personal views and perspectives on software process improvement. Starting with his first work on technology assessment in IBM over 20 years ago, Watts Humphrey describes the process improvement work he has been directly involved in. This includes the development of the early process assessment methods, the original design of the CMM, and the introduction of the Personal Software Process (PSP)^SM and Team Software Process (TSP){^SM}. In addition to describing the original motivation for this work, the author also reviews many of the problems he and his associates encountered and why they solved them the way they did. He also comments on the outstanding issues and likely directions for future work. Finally, this work has built on the experiences and contributions of many people. Mr. Humphrey only describes work that he was personally involved in and he names many of the key contributors. However, so many people have been involved in this work that a full list of the important participants would be impractical.     

基于复小波噪声方差显著修正的SAR图像去噪

提出了一种基于复小波域统计建模与噪声方差估计显著性修正相结合的合成孔径雷达(Synthetic Aperture Radar,SAR)图像斑点噪声滤波方法。该方法首先通过对数变换将乘性噪声模型转化为加性噪声模型,然后对变换后的图像进行双树复小波变换(Dualtree Complex Wavelet Transform,DCWT),并对复数小波系数的统计分布进行建模。在此先验分布的基础上,通过运用贝叶斯估计方法从含噪系数中恢复原始系数,达到滤除噪声的目的。实验结果表明该方法在去除噪声的同时保留了图像的细节信息,取得了很好的降噪效果。     

基于QTE的蒙古文显示机制研究与实现

蒙古语言是中国蒙古族使用的通用语言,由于蒙古文区别于其他文字的书写方式和其自身变形机制等特点,在很多通用的文字处理引擎中都不被支持。在嵌入式产品开发与应用领域中Linux加QTE已经成为流行方式。该文给出了一种在QTE环境上实现基于标准Unicode的蒙古文点阵显示和变形算法, 并自定义了支持蒙古文的QTE组件,扩展了QTE功能,为在Linux加QTE方式的嵌入式体系结构中处理蒙古文提供了一种解决方法。