Automated Question Answering in Webclopedia - A Demonstration
一文详解general language model-概述说明以及解释
一文详解general language model-概述说明以及解释1.引言1.1 概述引言部分是一篇文章的开端，用来向读者介绍文章的主题和目的。
在本篇文章中的引言部分，我们将对general language model进行概述。
General language model是一种基于深度学习的自然语言处理模型，它具有广泛的应用领域和重要性。
它通过大规模的语料库进行训练，以学习语言的潜在结构、语义和上下文依赖关系。
具体而言，general language model使用概率模型来预测一个给定上下文下的下一个单词或字符，从而实现对语言的理解和生成。
在过去的几年中，general language model取得了令人瞩目的成果，并在各个领域展现出巨大的潜力。
它可以被广泛应用于机器翻译、语言生成、自动问答、语义分析、情感分析和文本分类等任务中。
通过将general language model应用于这些任务，我们可以提高自然语言处理系统的表现，并改善人机交互的体验。
本文将对general language model的原理、应用领域以及其未来的发展进行详细的讨论。
我们将探讨general language model在不同领域的成功案例，并分析其优势和局限性。
同时，我们也会展望general language model在未来的进一步发展，并对其可能的应用和挑战进行展望。
通过本文的阅读，读者将能够全面了解general language model的概念、原理和应用领域。
同时，我们也希望读者能够对general language model在未来的发展趋势有一定的了解，并认识到这一领域所面临的挑战和机遇。
请开始阅读正文，进一步了解general language model的精髓。
1.2文章结构1.2 文章结构本文将按照以下结构来展开对general language model的详细解析：引言部分将概述general language model的基本概念和应用场景，并介绍本文的目的。
OpenText IDOL自然语言问答系统介绍说明书
FlyerSemantic Search T oolsAnswer search queries in a conversational manner through natural language processing (NLP). With access to multiple information types, the automated humanlike capabilities engage in dialog that furthers knowledge discovery.Create queries in natural human form. OpenT ext IDOL uses natural language question answering to provide the best results, not the best keywords.Natural LanguageQuestion AnsweringLike humans, IDOL pulls from many different sources to give a highly matched answer to natural language queries.When you ask someone a question, they are pulling from vast reserves of knowledge before they give you an answer. A chatbot should act in the same way. This is what IDOL does: it pulls from many different sources to give a highly matched answer to natural language queries. IDOL derives contextual and conceptual insights from data. This capability allows computers to recognize the relationships that exist within virtually any type of information—structured or unstructured. Like natural language processing (NLP), the ability to understand the data makes it possible to automate manual operations in real time: it extracts meaning from information and then performs an action.Dynamic Question AnsweringIDOL powers a range of Artificial Intelligence-powered chatbot solutions that allow organizations to offer their customers and employeesaccess to relevant information and timesavingprocesses. The chatbot uses an automated,humanlike operator that engages in naturallanguage dialogues and facilitates knowledgediscovery.The technology can understand, process, andanswer direct questions. This function helpsto streamline the retrieval process and allowsinformation to be obtained in a more convenient and userfriendly fashion. Y our users canask normal natural questions and receive theanswer they required versus being directed tothe technology that the information resides on.Answer BankMany organizations train their human supportagents on an existing set of frequently askedquestions. For example, if a user encounters aproblem on his mobile phone, the manufacturerhas established steps the user should follow tocorrect the problem. Answer Bank uses NLP toidentify the FAQ response that best answersa query.Fact BankThe Fact Bank contains a store of informationthat helps to return simple, factual answers. If aquery is looking for specific figures related to afield within a structured database such as “whatwas the yearoveryear variation in revenue forQ2 of 2021,” IDOL’s Fact Bank query responsesearches through the active databases to findthe correct response.Passage ExtractionThe Passage Extractor links to a store of documents that might be useful and returns shortsentences that contain relevant answers upona query.In many cases, the information requested issimply not present in either an FAQ data setor a structured database, so an extended approach is required. IDOL passage extractionlooks through the collection of data to findsegments of documents that best answer thequery directly.Learn more at/en-us/products/semantic-search/overview/opentext 261-000073-001 | O | 03/23 | © 2023 Open T ext。
使用AI技术进行智能问答与知识图谱构建
使用AI技术进行智能问答与知识图谱构建一、智能问答系统智能问答系统（Intelligent Question Answering, IQA）是一种基于人工智能技术的应用，旨在帮助用户快速准确地获取信息。
由于互联网上储存了大量的知识和数据，使用传统搜索引擎往往返回大量无关或重复的结果，给用户带来困扰。
而智能问答系统能够根据用户提供的问题进行语义理解和自动推理，并给出精确的回答或相关信息。
1.1 语义理解与自动回答实现智能问答系统首先需要进行语义理解，即将用户提出的问题转化为机器可以理解的形式。
常见的方法包括文本处理、词向量模型以及自然语言处理技术等。
通过对问题的分析和归纳，系统可以确定问题类型，并为后续步骤做好准备。
在获得了经过语义理解之后的问题后，接下来系统需要根据知识库或网络上的资源进行信息检索和推断，以获取与问题相关的答案和信息。
这就需要构建一个强大且可靠的知识图谱。
二、知识图谱构建知识图谱（Knowledge Graph）是一个结构化、链接和丰富的知识数据库，它抽象了现实世界中各种实体和关系之间的关联性。
通过将不同领域的知识与概念进行链接，构建了一个大规模的、多维度的知识网络。
因此，在智能问答系统中，构建一个精确而全面的知识图谱是非常重要的。
2.1 知识图谱构建过程知识图谱构建分为三个主要步骤：数据收集、知识抽取和关系建立。
* 数据收集：首先需要从可靠并且权威的数据源收集相关数据。
这些数据源可以是结构化、半结构化或者非结构化的信息，包括但不限于网页、语料库、数据库等。
* 知识抽取：在获取到原始数据后，需要使用信息抽取技术对其中的有用信息进行提取。
这可能涉及到实体提取、属性抽取及关系提取等任务。
* 关系建立：在得到抽取出来的实体、属性和关系之后，需要根据其内在联系，通过链接相应关联信息来构建一个完整而准确的知识图谱。
2.2 AI技术在知识图谱构建中的应用在传统的知识图谱构建中，大量的人工参与是不可避免的。
问答系统的设计与实现
1目录引言 (3)第一章研究背景 (4)1.1问答系统研究背景 (4)1.2传统的问答系统的不足 (4)1.3问答系统研究现状 (4)1.4问答系统的类型区分 (5)1.5问题的类型进行区分 (6)1.6中文问答系统研究 (6)1.7相关评测 (7)第二章系统分析 (8)2.1市场调查 (8)2.2问答系统的问题分析 (8)2.3问题分类 (8)2.4问题相似性判定 (9)2.5关键词扩展 (10)第三章数据库设计 (12)3.1数据库的需求分析 (12)3.2数据库表结构设计 (12)3.3E-R模型 (14)第四章系统详细设计与实现 (17)4.1系统工作原理介绍 (17)4.2系统数据流图 (18)4.3系统的实现算法 (18)4.4注册模块的设计与实现 (21)4.5注册模块的设计与实现 (33)4.6 系统首页的设计与实现 (36)4.7用户提问模块的设计与实现 (39)4.8问题显示模块的设计与实现 (42)4.9问题回答模块的登录与实现 (44)4.10后台管理模块的设计与实现 (45)第五章系统测试 (47)第六章总结 (48)致谢 (49)参考文献 (50)引言问答系统的设计目标是用简治、准确的答案回答用户用自然语言提出的问题。
在人工智能和自然语言处理领域，问答系统都有着较长的历史。
1950年英国数学家图灵(A．M．Turin8)在论文“Computing Machinery and Intelligence”中形象地指出了什么是人工智能，以及机器应该达到的智能标准。
也就是通过自然语言问答的方式，判断机器是否具有智能。
20世纪70年代随着自然语言理解技术的发展，出现了第一个实现用普通英语与计算机对话的人机接口LUNAR，该系统是伍德(W．Woods)于1972年开发用来协助地质学家查找、比较和评价阿波罗一号飞船带回的月球岩石和土壤标本的化学分析数据的系统。
本文将简要介绍国内外问答系统研究的进展情况。
2023-2024学年湖北省长阳土家族自治县第一高级中学高一上学期期中考试英语试题
2023-2024学年湖北省长阳土家族自治县第一高级中学高一上学期期中考试英语试题1. How long will the concert last?A．Two hours. B．One and a half hours. C．One hour.2. Who will carry out the plan?A．Sophie. B．David. C．Mary.3. What does the woman think of the course?A．Worth taking. B．Too hard. C．Very easy.4. Where will the speakers meet?A．At the cafe. B．At the bus stop. C．At the entrance to thestadium.5. What does the woman want the man to do?A．Speak louder. B．Say sorry to her. C．Turn off the radio.听下面一段较长对话，回答以下小题。
6. How much money should the man pay?A．£315. B．£350. C．£375.7. How will the man pay?A．In cash. B．By credit card. C．By check.听下面一段较长对话，回答以下小题。
8. Why does the man feel worn out?A．He has trouble in learning law.B．He has difficulty with Chinese.C．He works hard to defeat others.9. What does the woman advise the man to do?A．Attend talks in Law Department.B．Practice listening and speaking more.C．Talk to native speakers as much as possible.10. What does the woman offer to do?A．Practice Chinese with the man.B．Go to the Law Department with the man.C．Help the man prepare for the coming test.听下面一段较长对话，回答以下小题。
【2021.03.07】看论文神器知云文献翻译、百度翻译API申请、机器学习术语库
【2021.03.07】看论⽂神器知云⽂献翻译、百度翻译API申请、机器学习术语库最近在看论⽂，因为论⽂都是全英⽂的，所以需要论⽂查看的软件，在macOS上找到⼀款很好⽤的软件叫做知云⽂献翻译知云⽂献翻译界⾯长这样，可以长段翻译，总之很不错百度翻译API申请使⽤⾃⼰的api有两个好处：⼀、更加稳定⼆、可以⾃定义词库，我看的是医疗和机器学习相关的英⽂⽂献，可以⾃定义api申请在上⽅控制台、根据流程申请后可以在这⾥看到⾃⼰的ID和密钥填⼊就可以了⾃定义术语库我看的是机器学习的⽂献，因此在术语库⾥添加，导⼊⽂件（我会把⽂本放在后⾯导⼊后完成，有部分词语不翻译，⽐如MNIST这样的专有词语，就会报错，忽略掉就可以了开启术语库就⾏了机器学习术语库Supervised Learning|||监督学习Unsupervised Learning|||⽆监督学习Semi-supervised Learning|||半监督学习Reinforcement Learning|||强化学习Active Learning|||主动学习Online Learning|||在线学习Transfer Learning|||迁移学习Automated Machine Learning (AutoML)|||⾃动机器学习Representation Learning|||表⽰学习Minkowski distance|||闵可夫斯基距离Gradient Descent|||梯度下降Stochastic Gradient Descent|||随机梯度下降Over-fitting|||过拟合Regularization|||正则化Cross Validation|||交叉验证Perceptron|||感知机Logistic Regression|||逻辑回归Maximum Likelihood Estimation|||最⼤似然估计Newton’s method|||⽜顿法K-Nearest Neighbor|||K近邻法Mahanalobis Distance|||马⽒距离Decision Tree|||决策树Naive Bayes Classifier|||朴素贝叶斯分类器Generalization Error|||泛化误差PAC Learning|||概率近似正确学习Empirical Risk Minimization|||经验风险最⼩化Growth Function|||成长函数VC-dimension|||VC维Structural Risk Minimization|||结构风险最⼩化Eigendecomposition|||特征分解Singular Value Decomposition|||奇异值分解Moore-Penrose Pseudoinverse|||摩尔－彭若斯⼴义逆Marginal Probability|||边缘概率Conditional Probability|||条件概率Expectation|||期望Variance|||⽅差Covariance|||协⽅差Critical points|||临界点Support Vector Machine|||⽀持向量机Decision Boundary|||决策边界Convex Set|||凸集Lagrange Duality|||拉格朗⽇对偶性KKT Conditions|||KKT条件Coordinate ascent|||坐标下降法Sequential Minimal Optimization (SMO)|||序列最⼩化优化Ensemble Learning|||集成学习Bootstrap Aggregating (Bagging)|||装袋算法Random Forests|||随机森林Boosting|||提升⽅法Stacking|||堆叠⽅法Decision Tree|||决策树Classification Tree|||分类树Adaptive Boosting (AdaBoost)|||⾃适应提升Decision Stump|||决策树桩Meta Learning|||元学习Gradient Descent|||梯度下降Deep Feedforward Network (DFN)|||深度前向⽹络Backpropagation|||反向传播Activation Function|||激活函数Multi-layer Perceptron (MLP)|||多层感知机Perceptron|||感知机Mean-Squared Error (MSE)|||均⽅误差Chain Rule|||链式法则Logistic Function|||逻辑函数Hyperbolic Tangent|||双曲正切函数Rectified Linear Units (ReLU)|||整流线性单元Residual Neural Networks (ResNet)|||残差神经⽹络Regularization|||正则化Overfitting|||过拟合Data(set) Augmentation|||数据增强Parameter Sharing|||参数共享Ensemble Learning|||集成学习Dropout|||L2 Regularization|||L2正则化Taylor Series Approximation|||泰勒级数近似Taylor Expansion|||泰勒展开Bayesian Prior|||贝叶斯先验Bayesian Inference|||贝叶斯推理Gaussian Prior|||⾼斯先验Maximum-a-Posteriori (MAP)|||最⼤后验Linear Regression|||线性回归L1 Regularization|||L1正则化Constrained Optimization|||约束优化Lagrange Function|||拉格朗⽇函数Denoising Autoencoder|||降噪⾃动编码器Label Smoothing|||标签平滑Eigen Decomposition|||特征分解Convolutional Neural Networks (CNNs)|||卷积神经⽹络Semi-Supervised Learning|||半监督学习Generative Model|||⽣成模型Discriminative Model|||判别模型Multi-Task Learning|||多任务学习Bootstrap Aggregating (Bagging)|||装袋算法Multivariate Normal Distribution|||多元正态分布Sparse Parametrization|||稀疏参数化Sparse Representation|||稀疏表⽰Student-t Prior|||学⽣T先验KL Divergence|||KL散度Orthogonal Matching Pursuit (OMP)|||正交匹配追踪算法Adversarial Training|||对抗训练Matrix Factorization (MF)|||矩阵分解Root-Mean-Square Error (RMSE)|||均⽅根误差Collaborative Filtering (CF)|||协同过滤Nonnegative Matrix Factorization (NMF)|||⾮负矩阵分解Singular Value Decomposition (SVD)|||奇异值分解Latent Sematic Analysis (LSA)|||潜在语义分析Bayesian Probabilistic Matrix Factorization (BPMF)|||贝叶斯概率矩阵分解Wishart Prior|||Wishart先验Sparse Coding|||稀疏编码Factorization Machines (FM)|||分解机second-order method|||⼆阶⽅法cost function|||代价函数training set|||训练集objective function|||⽬标函数expectation|||期望data generating distribution|||数据⽣成分布empirical risk minimization|||经验风险最⼩化generalization error|||泛化误差empirical risk|||经验风险overfitting|||过拟合feasible|||可⾏loss function|||损失函数derivative|||导数gradient descent|||梯度下降surrogate loss function|||代理损失函数early stopping|||提前终⽌Hessian matrix|||⿊塞矩阵second derivative|||⼆阶导数Taylor series|||泰勒级数Ill-conditioning|||病态的critical point|||临界点local minimum|||局部极⼩点local maximum|||局部极⼤点saddle point|||鞍点local minima|||局部极⼩值global minimum|||全局最⼩点convex function|||凸函数weight space symmetry|||权重空间对称性Newton’s method|||⽜顿法activation function|||激活函数fully-connected networks|||全连接⽹络Resnet|||残差神经⽹络gradient clipping|||梯度截断recurrent neural network|||循环神经⽹络long-term dependency|||长期依赖eigen-decomposition|||特征值分解feedforward network|||前馈⽹络vanishing and exploding gradient problem|||梯度消失与爆炸问题contrastive divergence|||对⽐散度validation set|||验证集stochastic gradient descent|||随机梯度下降learning rate|||学习速率momentum|||动量gradient descent|||梯度下降poor conditioning|||病态条件nesterov momentum|||Nesterov 动量partial derivative|||偏导数moving average|||移动平均quadratic function|||⼆次函数positive definite|||正定quasi-newton method|||拟⽜顿法conjugate gradient|||共轭梯度steepest descent|||最速下降reparametrization|||重参数化standard deviation|||标准差coordinate descent|||坐标下降skip connection|||跳跃连接convolutional neural network|||卷积神经⽹络convolution|||卷积pooling|||池化feedforward neural network|||前馈神经⽹络maximum likelihood|||最⼤似然back propagation|||反向传播artificial neural network|||⼈⼯神经⽹络deep feedforward network|||深度前馈⽹络hyperparameter|||超参数sparse connectivity|||稀疏连接parameter sharing|||参数共享receptive field|||接受域chain rule|||链式法则tiled convolution|||平铺卷积object detection|||⽬标检测error rate|||错误率activation function|||激活函数overfitting|||过拟合attention mechanism|||注意⼒机制transfer learning|||迁移学习autoencoder|||⾃编码器unsupervised learning|||⽆监督学习back propagation|||反向传播pretraining|||预训练dimensionality reduction|||降维curse of dimensionality|||维数灾难feedforward neural network|||前馈神经⽹络encoder|||编码器decoder|||解码器cross-entropy|||交叉熵tied weights|||绑定的权重PCA|||PCAprincipal component analysis|||主成分分析singular value decomposition|||奇异值分解SVD|||SVDsingular value|||奇异值reconstruction error|||重构误差covariance matrix|||协⽅差矩阵Kullback-Leibler (KL) divergence|||KL散度denoising autoencoder|||去噪⾃编码器sparse autoencoder|||稀疏⾃编码器contractive autoencoder|||收缩⾃编码器conjugate gradient|||共轭梯度fine-tune|||精调local optima|||局部最优posterior distribution|||后验分布gaussian distribution|||⾼斯分布reparametrization|||重参数化recurrent neural network|||循环神经⽹络artificial neural network|||⼈⼯神经⽹络feedforward neural network|||前馈神经⽹络sentiment analysis|||情感分析machine translation|||机器翻译pos tagging|||词性标注teacher forcing|||导师驱动过程back-propagation through time|||通过时间反向传播directed graphical model|||有向图模型speech recognition|||语⾳识别question answering|||问答系统attention mechanism|||注意⼒机制vanishing and exploding gradient problem|||梯度消失与爆炸问题jacobi matrix|||jacobi矩阵long-term dependency|||长期依赖clip gradient|||梯度截断long short-term memory|||长短期记忆gated recurrent unit|||门控循环单元hadamard product|||Hadamard乘积back propagation|||反向传播attention mechanism|||注意⼒机制feedforward network|||前馈⽹络named entity recognition|||命名实体识别Representation Learning|||表征学习Distributed Representation|||分布式表征Multi-task Learning|||多任务学习Multi-Modal Learning|||多模态学习Semi-supervised Learning|||半监督学习NLP|||⾃然语⾔处理Neural Language Model|||神经语⾔模型Neural Probabilistic Language Model|||神经概率语⾔模型RNN|||循环神经⽹络Neural Tensor Network|||神经张量⽹络Graph Neural Network|||图神经⽹络Graph Covolutional Network (GCN)|||图卷积⽹络Graph Attention Network|||图注意⼒⽹络Self-attention|||⾃注意⼒机制Feature Learning|||表征学习Feature Engineering|||特征⼯程One-hot Representation|||独热编码Speech Recognition|||语⾳识别DBM|||深度玻尔兹曼机Zero-shot Learning|||零次学习Autoencoder|||⾃编码器Generative Adversarial Network（GAN）|||⽣成对抗⽹络Approximate Inference|||近似推断Bag-of-Words Model|||词袋模型Forward Propagation|||前向传播Huffman Binary Tree|||霍夫曼⼆叉树NNLM|||神经⽹络语⾔模型N-gram|||N元语法Skip-gram Model|||跳元模型Negative Sampling|||负采样CBOW|||连续词袋模型Knowledge Graph|||知识图谱Relation Extraction|||关系抽取Node Embedding|||节点嵌⼊Graph Neural Network|||图神经⽹络Node Classification|||节点分类Link Prediction|||链路预测Community Detection|||社区发现Isomorphism|||同构Random Walk|||随机漫步Spectral Clustering|||谱聚类Asynchronous Stochastic Gradient Algorithm|||异步随机梯度算法Negative Sampling|||负采样Network Embedding|||⽹络嵌⼊Graph Theory|||图论multiset|||多重集Perron-Frobenius Theorem|||佩龙—弗罗贝尼乌斯定理Stationary Distribution|||稳态分布Matrix Factorization|||矩阵分解Sparsification|||稀疏化Singular Value Decomposition|||奇异值分解Frobenius Norm|||F-范数Heterogeneous Network|||异构⽹络Graph Convolutional Network （GCN）|||图卷积⽹络CNN|||卷积神经⽹络Semi-Supervised Classification|||半监督分类Chebyshev polynomial|||切⽐雪夫多项式Gradient Exploding|||梯度爆炸Gradient Vanishing|||梯度消失Batch Normalization|||批标准化Neighborhood Aggregation|||邻域聚合LSTM|||长短期记忆⽹络Graph Attention Network|||图注意⼒⽹络Self-attention|||⾃注意⼒机制Rescaling|||再缩放Attention Mechanism|||注意⼒机制Jensen-Shannon Divergence|||JS散度Cognitive Graph|||认知图谱Generative Adversarial Network(GAN)|||⽣成对抗⽹络Generative Model|||⽣成模型Discriminative Model|||判别模型Gaussian Mixture Model|||⾼斯混合模型Variational Auto-Encoder（VAE）|||变分编码器Markov Chain|||马尔可夫链Boltzmann Machine|||玻尔兹曼机Kullback–Leibler divergence|||KL散度Vanishing Gradient|||梯度消失Surrogate Loss|||替代损失Mode Collapse|||模式崩溃Earth-Mover/Wasserstein-1 Distance|||搬⼟距离/EMD Lipschitz Continuity|||利普希茨连续Feedforward Network|||前馈⽹络Minimax Game|||极⼩极⼤博弈Adversarial Learning|||对抗学习Outlier|||异常值/离群值Rectified Linear Unit|||线性修正单元Logistic Regression|||逻辑回归Softmax Regression|||Softmax回归SVM|||⽀持向量机Decision Tree|||决策树Nearest Neighbors|||最近邻White-box|||⽩盒（测试 etc. ）Lagrange Multiplier|||拉格朗⽇乘⼦Black-box|||⿊盒（测试 etc. ）Robustness|||鲁棒性/稳健性Decision Boundary|||决策边界Non-differentiability|||不可微Intra-technique Transferability|||相同技术迁移能⼒Cross-technique Transferability|||不同技术迁移能⼒Data Augmentation|||数据增强Adaboost|||recommender system|||推荐系统Probability matching|||概率匹配minimax regret|||face detection|||⼈脸检测i.i.d.|||独⽴同分布Minimax|||极⼤极⼩linear model|||线性模型Thompson Sampling|||汤普森抽样eigenvalues|||特征值optimization problem|||优化问题greedy algorithm|||贪⼼算法Dynamic Programming|||动态规划lookup table|||查找表Bellman equation|||贝尔曼⽅程discount factor|||折现系数Reinforcement Learning|||强化学习gradient theorem|||梯度定理stochastic gradient descent|||随机梯度下降法Monte Carlo|||蒙特卡罗⽅法function approximation|||函数逼近Markov Decision Process|||马尔可夫决策过程Bootstrapping|||引导Shortest Path Problem|||最短路径问题expected return|||预期回报Q-Learning|||Q学习temporal-difference learning|||时间差分学习AlphaZero|||Backgammon|||西洋双陆棋finite set|||有限集Markov property|||马尔可夫性质sample complexity|||样本复杂性Cartesian product|||笛卡⼉积Kevin Leyton-Brown|||SVM|||⽀持向量机MNIST|||ImageNet|||Ensemble learning|||集成学习Neural networks|||神经⽹络Neuroevolution|||神经演化object recognition|||⽬标识别Multi-task learning|||多任务学习Treebank|||树图资料库covariance|||协⽅差Hamiltonian Monte Carlo|||哈密顿蒙特卡罗Inductive bias|||归纳偏置bilevel optimization|||双层规划genetic algorithms|||遗传算法Bayesian linear regression|||贝叶斯线性回归ANOVA|||⽅差分析Extrapolation|||外推法activation function|||激活函数CIFAR-10|||Gaussian Process|||⾼斯过程k-nearest neighbors|||K最近邻Neural Turing machine|||神经图灵机MCMC|||马尔可夫链蒙特卡罗Collaborative filtering|||协同过滤AlphaGo|||random forests|||随机森林multivariate Gaussian|||多元⾼斯Bayesian Optimization|||贝叶斯优化meta-learning|||元学习iterative algorithm|||迭代算法Viterbi algorithm|||维特⽐算法Gibbs distribution|||吉布斯分布Discriminative model|||判别模型Maximum Entropy Markov Model|||最⼤熵马尔可夫模型Information Extraction|||信息提取clique|||⼩圈⼦conditional random field|||条件随机场CRF|||条件随机场triad|||三元关系Naïve Bayes|||朴素贝叶斯social network|||社交⽹络Bayesian network|||贝叶斯⽹络SVM|||⽀持向量机Joint probability distribution|||联合概率分布Conditional independence|||条件独⽴性sequence analysis|||序列分析Perceptron|||感知器Markov Blanket|||马尔科夫毯Hidden Markov Model|||隐马尔可夫模型finite-state|||有限状态Shallow parsing|||浅层分析Active learning|||主动学习Speech recognition|||语⾳识别convex|||凸transition matrix|||转移矩阵factor graph|||因⼦图forward-backward algorithm|||前向后向算法parsing|||语法分析structural holes|||结构洞graphical model|||图模型Markov Random Field|||马尔可夫随机场Social balance theory|||社会平衡理论Generative model|||⽣成模型probalistic topic model|||概率语义模型TFIDF|||词频-⽂本逆向频率LSI|||潜在语义索引Bayesian network|||贝叶斯⽹络模型Markov random field|||马尔科夫随机场restricted boltzmann machine|||限制玻尔兹曼机LDA|||隐式狄利克雷分配模型PLSI|||概率潜在语义索引模型EM algorithm|||最⼤期望算法Gibbs sampling|||吉布斯采样法MAP (Maximum A Posteriori)|||最⼤后验概率算法Markov Chain Monte Carlo|||马尔科夫链式蒙特卡洛算法Monte Carlo Sampling|||蒙特卡洛采样法Univariate|||单变量Hoeffding Bound|||Hoeffding界Chernoff Bound|||Chernoff界Importance Sampling|||加权采样法invariant distribution|||不动点分布Metropolis-Hastings algorithm|||Metropolis-Hastings算法Probablistic Inference|||概率推断Variational Inference|||变量式推断HMM|||隐式马尔科夫模型mean field|||平均场理论mixture model|||混合模型convex duality|||凸对偶belief propagation|||置信传播算法non-parametric model|||⾮参模型Gaussian process|||正态过程multivariate Gaussian distribution|||多元正态分布Dirichlet process|||狄利克雷过程stick breaking process|||断棒过程Chinese restaurant process|||中餐馆过程Blackwell-MacQueen Urn Scheme|||Blackwell-MacQueen桶法De Finetti's theorem|||de Finetti定理collapsed Gibbs sampling|||下陷吉布斯采样法Hierarchical Dirichlet process|||阶梯式狄利克雷过程Indian Buffet process|||印度餐馆过程。
Open-domain QA systems
Open-domain QA systemsAnswerBusLCC2([7]), QuASM3, IONAUT4([1]),START5([11]) and Webclopedia6([10]).AnswerBus: 句子级，多语言支持functional words deletion (prepositions, determiners/pronouns, conjunctions, interjections,and discourse particles.)use of word frequency table （delete frequently used words）special words deletionword form modification.候选答案提取words, then an answer candidate sentence should have at least two of them. When a sentence meets the condition as indicated by the above formula, it will receive a primary score basedon the number of matching words it contains. Otherwise, it will receive a score of “0.”候选答案排序问题类型→答案类型(who→ name)问题类型→关键词扩展(多远→千米)名字实体提取Coreference resolution (他→何靖)(AnswerBus only solves the coreferences in theadjacent sentences. When this type of coreference isdetected, the later sentence receives part of score fromits previous sentence.)搜索引擎返回的顺序答案句子评分WebclopediaPrevious work in automated question answering has often categorized questions by question wordalone or by a mixture of question word and the semantic class of the answer (Srihari and Li, 2000; Moldovan et al., 2000). To ensure full coverage of all forms of simple question and answer, we have been developing a QA Typology as a taxonomy of QA types, becoming increasingly specific as one moves from root downward.To create the QA Typology, we analyzed 17,384 questions and their answers (downloaded from); see (Gerber, 2001). The Typology contains 94 nodes, of which 47 are leaf nodes;a section of it appears in Figure 2.By CONTEXTNaturally, this forces the patterns tocontain not only surface forms (words and punctuation, butalso type markers (Date, NumericalAmount, MoneyAmount...).A Question/Answer Typology with Surface Text Patterns问题分类树pattern自动提取（suffix tree,precision）(NAME_OF_PERSON BIRTHYEAR),pattern提取查询评估每个pattern的precision 查询银平Patterns of Potential Answer Expressions as Clues to the Right Answers TextRollersearches for candidate answers using key words (from the question text) and chooses the most probable answer using patterns.In the literature we find approaches attempting to distinguish between the main (primary) andadditional (secondary) query words. In (Sneiders, 1998) this distinction isdiscussed as applied tosearching for answers to FAQs, where the answers are represented as sentences. Primary keywordsare the words that convey the essence of the sentence. They cannot be ignored. Secondarykeywords are the less-relevant words for a particular sentence. They help to convey the meaning ofthe sentence but can be omitted without changing the essence of the meaning. Answer ExtractionRanking1.In most cases, the matching is boolean:2.a couple of special cases where finer distinctionsare made.How many lives were lost in the Lockerbie air crash, entities such as 270 lives or almost 300 lives would be ranked above entities such as 200 pumpkins or150. 23. the frequency and position of occurrences of agiven entity within the retrieved passages.。
电子商务与贸易实务(英文版)(ppt 60)精品文档
One-to-One Marketing
Relationship marketing
“Overt attempt of exchange partners to build a long term association, characterized by purposeful cooperation and mutual dependence on the development of social, as well as structural, bonds”
Pure Vs. Partial Electronic Commerce
Three dimensions
the product (service) sold [physical / digital]; the process [physical / digital] the delivery agent (or intermediary) [physical / digital]
An electronic market is a place where shoppers and sellers meet electronically.
In electronic markets, sellers and buyers negotiate, submit bids, agree on an order, and finish the execution on- or off-line.
有没有网站，有没有信箱，利用率如何。 • 你，以及单位，在与其他机构，如政府部门之间的文
件交换是什么形式？政府部门有没有要求这你或单位 提供电子文档？ • 单位有没有计算机管理系统？如ERP，如有，利用水 平如何。 • 单位有没有集成制造系统，如有，利用率如何？
基于ChatGPT的医学领域自动问答系统：整合知识图谱与对话生成的智能助手(英文中文双语版优质文档)
基于ChatGPT的医学领域自动问答系统：整合知识图谱与对话生成的智能助手（英文中文双语版优质文档）ChatGPT-based automatic question answering system in the medical field: an intelligent assistant that integrates knowledge graphs and dialogue generation (high-quality documents in English and Chinese bilingual versions) ChatGPT-based automatic question answering system in the medical field: an intelligent assistant integrating knowledge graph and dialogue generationSummaryWith the rapid development of artificial intelligence, the application of automatic question answering system in the medical field is becoming more and more important. This paper proposes an automatic question answering system in the medical field based on ChatGPT, which realizes the function of an intelligent assistant by integrating knowledge graph and dialogue generation. We trained a large language model based on the GPT-3.5 architecture to understand and answer medical questions. At the same time, we construct a knowledge graph in the medical field, which contains rich medical knowledge and entity relations. By combining knowledge graphs with language models, we achieve more accurate and comprehensive question answering capabilities. Experimental results show that our system achieves satisfactory performance on question answering tasks in the medical domain, and can effectively assist medical professionals in question answering and decision support.1 IntroductionWith the continuous development of medical technology and the rapid accumulation of medical knowledge, professional knowledge in the medical field is becoming more and more complex. Medical professionals often face various problems in daily practice, and need to consult a large amount of medical literature and knowledge to obtain accurate answers. However, traditional information retrieval methods are often inefficient and have information noise, which is difficult to meet the needs of doctors to obtain information quickly and accurately. Therefore, it is particularly important to develop an efficient and accurate medical automatic question answering system.2. Related workIn medicine, natural language processing and artificial intelligence technologies have made some important advances. Some researchers have proposed rule-based methods to answer medical questions, but such methods often require a large number of artificial rules and domain knowledge, and it is difficult to cover all situations. Other researchers take a statistically based approach by analyzing large volumes of medical literature and corpora to answer questions. However, this approach is often limited by the quality and scale of data, making it difficult to deal with complex medical problems.3. System architectureOur system uses the ChatGPT model based on the GPT-3.5 architecture as the core component. The model has been trained on a large scale and has the ability to understand and generate natural language. In order to provide more accurate and comprehensive medical Q&A, we also construct a knowledge graph in the medical field. The knowledge graph contains medical entities (such as diseases, symptoms, drugs, etc.) and the relationships between them. The construction of the knowledge map is based on the knowledge of professional medical databases and domain experts, which ensures the accuracy and authority of knowledge.When the system is running, when a user asks a medical question, the system first understands and analyzes the question through the ChatGPT model. The model can infer the intent of the question and extract key information. Then, the system matches the key information of the question with the knowledge graph to obtain relevant medical knowledge. Through the query and reasoning of the knowledge graph, the system can generate accurate answers and return them to the user in natural language.In order to improve the interactivity and user experience of the system, we also introduce the technology of dialogue generation. The system can conduct dialogues according to the context, understand the user's inquiry and supplementary information, and make further explanations and reasoning as needed. In this way, the system can better meet the needs of users and provide more personalized and humanized answers.To evaluate the performance of the system, we use a medical domain question answering dataset for testing. Experimental results demonstrate that our system achieves satisfactory accuracy and completeness in answering medical questions. Our system provides answers while also being able to explain and reason, providing deeper understanding and support than traditional information retrieval methods.The chatGPT-based automatic question answering system in the medical field proposed in this paper realizes the function of an intelligent assistant on the basis of integrating knowledge graph and dialogue generation. The system can accurately and comprehensively answer medical questions and provide a personalized interactive experience. The system has broad application prospects in the medical field and can help doctors quickly acquire accurate medical knowledge and improve the effectiveness of diagnosis and treatment. Future research can further optimize the performance of the system and expand its application range to meet the continuous development and changing needs of the medical field.基于ChatGPT的医学领域自动问答系统：整合知识图谱与对话生成的智能助手摘要随着人工智能的迅猛发展，自动问答系统在医学领域的应用变得越来越重要。
中文自动问答系统探讨
中文自动问答系统探讨中文自动问答系统（Chinese Automatic Question Answering system, C-AQA）是一种能够处理来自用户的中文自然语言查询，并将相应的答案自动回复给用户的一种计算机应用技术。
它是一个自动回答中文查询的系统，它可以准确、快捷地完成用户的查询，提高人们的查询效率。
它没有被计算机束缚，是一种深度学习系统，它可以快速适应用户提问的变化，能够做出更好的及时回应，从而提高工作效率。
从计算机的角度来看，中文自动问答的核心技术主要包括语言处理，知识表示及其获取，智能搜索等。
1990年代以来，中文自动问答系统的进展非常不错，出现了很多新技术和新理论，如机器翻译技术、知识表示技术以及知识管理研究等，为中文自动问答理论和实现提供了有力支持。
此外，人工智能（AI）在中文自动问答中的应用也有了很大的发展，AI以其高效的智能搜索及自主学习性能，大大提升了中文自动问答系统的性能和覆盖面。
中文自动问答系统的应用非常广泛，它可以被用在接待客户的服务中心，聊天室、QQ群等，几乎可以被应用于所有与客户服务有关的场合，可以帮助客服人员减轻工作任务，提高客服服务质量。
中文自动问答系统还可以用于学校的教学，建立一个自动问答系统，教师之前设想的答案错误，系统能够根据查询情况，给出最基本的答案，也可以设置自动的提示查询，帮助学生完成学习任务。
另外，中文自动问答系统还可以被用于搜索引擎，它将问题转化为搜索引擎应该搜索的关键词，从而提供答案，改善搜索效率，使得网页搜索变得更快更精准。
中文自动问答系统具有独特的发展优势，因其便捷、准确性等优点，目前正被广泛用于客户服务、教育、互联网搜索等领域。
预计未来的发展也将继续加快，在应用的范围和深度方面也将更加广泛，把自然语言处理、知识图谱、机器翻译等技术应用到中文自动问答上，建立更智能的系统，未来可以实现真正的人与计算机的无缝对话，将让人们的查询变得方便快捷。
2023年本科学位英语考试真题
2023年本科学位英语考试真题全文共3篇示例，供读者参考篇12023 Undergraduate Degree English ExamSection 1: Reading Comprehension (40 points)Text 1The Importance of ReadingIn today's fast-paced world, the importance of reading cannot be overstated. Reading is not only a way to gain knowledge and information, but also a way to expand your imagination and creativity. By reading different types of books, you can broaden your perspectives and develop critical thinking skills.Text 2The Benefits of TravelingTraveling is a great way to explore new cultures, meet new people, and experience new things. It can help you broaden your horizons and gain a better understanding of the world aroundyou. Traveling also allows you to step out of your comfort zone and challenge yourself in new ways.Text 3The Impact of Technology on SocietyTechnology has greatly impacted society in both positive and negative ways. On one hand, technology has made our lives more convenient and efficient. On the other hand, it has also led to concerns about privacy and security. It is important to strike a balance between the benefits and drawbacks of technology in order to create a more sustainable future.Section 2: Writing (60 points)Question 1: In your opinion, what are the advantages and disadvantages of social media?Question 2: Describe a person who has had a significant impact on your life and explain why.Question 3: Discuss the role of education in shaping the future of society.Question 4: Write a letter to a friend recommending a book that has had a profound impact on you.Overall, the 2023 Undergraduate Degree English Exam aims to test students' reading comprehension and writing skills, as well as their ability to think critically and express their ideas effectively in English. Good luck to all the candidates!篇22023 Undergraduate English Degree ExamSection A: Reading Comprehension1. Read the following passage and answer the questions below."The Impact of Climate Change on BiodiversityClimate change is one of the greatest challenges facing our planet today. Rising temperatures, changing weather patterns, and increasing sea levels are all having a significant impact on biodiversity. As the global climate continues to warm, many species of plants and animals are being forced to adapt or face extinction.One of the key ways in which climate change is affecting biodiversity is through habitat loss. As temperatures rise, certain habitats are no longer suitable for the species that rely on themfor survival. This can lead to a decline in population numbers and even the complete loss of certain species.Another major impact of climate change on biodiversity is the disruption of food chains and ecosystems. Changes in temperature and weather patterns can alter the availability of food sources for many species, leading to a decline in their population numbers. This can have a cascading effect on entire ecosystems, causing widespread disruption and potential collapse.In order to address the threat of climate change to biodiversity, it is essential that we take action to reduce our carbon emissions and mitigate the impacts of global warming. By investing in renewable energy sources, protecting and restoring habitats, and implementing sustainable land use practices, we can help to ensure a more sustainable future for all living organisms on Earth."Questions:a) What are some of the key ways in which climate change is affecting biodiversity?b) Why is habitat loss a significant issue for species facing climate change?c) How can individuals and communities help to address the threat of climate change to biodiversity?2. Read the following passage and write a summary in your own words."Artificial Intelligence and the Future of WorkArtificial intelligence (AI) is transforming the way we work, with machines becoming increasingly capable of performing tasks that were once the domain of humans. While AI has the potential to revolutionize industries and improve efficiency, it also raises concerns about the impact on the future of work.As AI continues to advance, many traditional jobs are at risk of being automated, leading to potential job losses for millions of workers. However, AI also has the potential to create new job opportunities in emerging industries and sectors. It is essential that workers adapt to these changes by developing new skills and knowledge to remain competitive in the job market.Overall, the future of work in the age of AI will require a balance between human and machine capabilities. While AI can help to streamline processes and increase productivity, it is essential that humans retain control over decision-making and ethical considerations in the workplace. By embracing thepotential of AI while also recognizing its limitations, we can create a future of work that is both innovative and sustainable."Section B: WritingWrite an essay discussing the role of education in preparing individuals for the challenges of the 21st century. In your essay, address the following points:- The importance of lifelong learning and continuous education- The role of schools and universities in developing essential skills for the future- The impact of technology on the education system and learning outcomes- The challenges and opportunities presented by the changing landscape of educationRemember to support your arguments with examples and evidence from your own experiences or research.Section C: Listening ComprehensionListen to the audio recording and answer the following questions:1. What is the topic of the discussion?2. What are some key points made by the speakers?3. What is the main conclusion drawn by the speakers?Good luck with your exam! Remember to manage your time effectively and carefully read and listen to each question before answering.篇32023 Undergraduate English ExamSection A: Reading ComprehensionRead the following passage and answer the questions that follow.The rise of technology in the workplace has created both challenges and opportunities for employees. On one hand, automation and artificial intelligence have led to the elimination of some job roles. On the other hand, new technologies have also created new job opportunities and increased efficiency in many industries.One of the biggest challenges for employees in the modern workplace is the need to adapt to rapidly changing technology. Workers must be willing to continuously learn new skills andadapt to new tools and systems. This requires a proactive approach to professional development and a willingness to embrace change.However, technology has also created opportunities for employees to work more flexibly and remotely. The ability to work from home or from anywhere in the world has opened up new possibilities for workers to balance their personal and professional lives. This flexibility can lead to increased job satisfaction and productivity.Overall, the key to success in the modern workplace is to embrace technology while also recognizing the importance of soft skills such as communication, collaboration, and adaptability. By combining technical expertise with strong soft skills, employees can thrive in the ever-changing world of work.Questions:1. What are some of the challenges that technology has created for employees in the workplace?2. How can employees adapt to rapidly changing technology?3. What are some of the opportunities that technology has created for employees in the workplace?4. Why is it important for employees to develop both technical expertise and soft skills?5. How can employees balance their personal and professional lives in the modern workplace?Section B: WritingWrite an essay on the following topic:"The impact of technology on the future of work."In your essay, please address the following points:- How has technology changed the way we work?- What are some of the potential benefits of technology in the workplace?- What are some of the potential challenges of technology in the workplace?- How can employees and organizations adapt to the changing landscape of work in the digital age?Your essay should be well-organized and provide evidence to support your points.Section C: Listening ComprehensionListen to the audio recordings and answer the questions that follow.Section D: SpeakingIn this section, you will be asked to discuss a series of topics with the examiner. Each topic will require you to express your opinions, provide examples, and engage in a discussion.The speaking section will assess your ability to communicate effectively in English and demonstrate your critical thinking skills.Good luck on your exam!。
我在网上寻找答案的专栏英语作文
我在网上寻找答案的专栏英语作文In this modern age of information technology, the internet has become an indispensable tool for seeking answers to a wide range of questions. Whether it's obtaining information for academic research, troubleshooting a technical issue, or simply satisfying our curiosity, the wealth of knowledge available online has made it easier than ever to find the answers we seek.Personally, I have found myself turning to the internet on numerous occasions to find answers to various questions. One of the most common scenarios in which I utilize the internet as a resource is when I encounter a new concept or topic that I am unfamiliar with. Instead of relying solely on traditional sources such as textbooks or encyclopedias, I often turn to online articles, forums, and educational websites to gain a better understanding of the subject matter.For example, when I recently came across the term "blockchain technology" in an article, I was curious to learn more about its applications and implications. With a quick search on a popular search engine, I was able to access a plethora of articles, videos, and forums discussing the topic in detail. Through my online research, I not only gained a better understanding of blockchain technology but also discovered its potential torevolutionize industries such as finance, healthcare, and supply chain management.In addition to academic inquiries, the internet has also proven to be a valuable resource for troubleshooting technical issues. Whether it's a malfunctioning smartphone, a software glitch on my computer, or a problem with my internet connection, a quick search online can often provide step-by-step guides, troubleshooting tips, and insights from other users who have encountered similar issues.For instance, when my laptop recently started displaying an error message upon startup, I was able to find a forum thread where other users had shared their experiences and solutions. With the help of their suggestions, I was able to diagnose the problem as a corrupt system file and resolve it with a simple software repair tool. Without the assistance of the online community, I would have likely struggled to identify the issue and find a solution on my own.Furthermore, the internet has become an invaluable resource for staying informed on current events, trends, and developments around the world. With news websites, social media platforms, and online publications delivering real-time updates on a wide range of topics, I can easily access the latestinformation on politics, economics, science, culture, and more with just a few clicks.In conclusion, the internet has revolutionized the way we seek answers to our questions, solve problems, and keep ourselves informed. By harnessing the vast array of resources available online, we can tap into a global network of knowledge and expertise that empowers us to learn, grow, and adapt in an ever-changing world. As I continue to navigate the digital landscape in search of answers, I am grateful for the wealth of information at my fingertips and the endless opportunities for discovery and enlightenment that the internet provides.。
英文问答系统的自动回答与知识获取研究
英文问答系统的自动回答与知识获取研究In recent years, there has been a growing interest in the research and development of English question-answering systems. These systems aim to automatically answer questions and acquire knowledge from various sources. This article will explore the advancements and challenges in the field of English question-answering systems as well as discuss the research on automatic answer generation and knowledge acquisition.English question-answering systems are designed to help users find specific information by providing concise and accurate answers to their questions. These systems have the potential to revolutionize the way we search for and retrieve information, as they eliminate the need to sift through vast amounts of data and documents. Instead, users can simply ask a question, and the system will retrieve the most relevant information and present it in a concise manner.One of the key challenges in developing English question-answering systems is the ability to generate accurate and informative answers automatically. Traditional keyword-based search engines often return a list of documents that may contain the answer, requiring users to manually locate the relevant information. In contrast, question-answering systems aim to provide direct answers to questions without requiring users to search for the answer themselves.To achieve this, researchers have explored various techniques for automatic answer generation. One approach is based on information retrieval, where the system retrieves relevant information from a large corpus of documents and extracts the most relevant fragments as answers. Another approach is based on natural language processing and machine learning techniques, where the system uses patterns and models to understand the question and generate appropriate answers.Knowledge acquisition is another important aspect of English question-answering systems. These systems require a vast amount of knowledge to accurately answer questions across different domains. Manual knowledge acquisition is a labor-intensive and time-consuming process. Therefore, researchers have focused on automatic knowledge acquisition methods that can efficiently gather information from diverse sources.One common method for knowledge acquisition is through text mining and information extraction. This involves analyzing large amounts of text to extract structured information, such as facts, relationships, and entities. Other methods include using structured knowledge bases like Wikipedia or utilizing pre-existing knowledge graphs to enhance the knowledge base of the question-answering system.Despite the advancements in English question-answering systems, there are still several challenges that need to be addressed. One challenge is handling ambiguous queries or questions with multiple valid interpretations. English language can be inherently ambiguous, and resolving such ambiguities accurately is vital for providing precise answers. Anotherchallenge is the ability to deal with incomplete or contradictory information present in different sources.Moreover, the development of English question-answering systems requires access to large annotated datasets for training and evaluating the system. Creating such datasets can be a time-consuming and costly process, as it requires human experts to create question-answer pairs. Therefore, researchers are exploring methods to automatically generate training data to facilitate the development and evaluation of these systems.In conclusion, the research on English question-answering systems has made significant progress in recent years. The ability to automatically answer questions and acquire knowledge has the potential to greatly enhance information retrieval and user experience. By leveraging techniques such as automatic answer generation and knowledge acquisition, researchers are continually advancing the capabilities and accuracy of these systems. However, challenges such as ambiguity handling and incomplete information still need to be addressed to further improve the performance of English question-answering systems.。
自然语言处理在智能问答系统中的应用
自然语言处理在智能问答系统中的应用Chapter One: IntroductionNatural Language Processing (NLP) is a subfield of Artificial Intelligence that deals with the interaction between human language and computers. NLP has become an integral part of many cutting-edge technologies, including chatbots, voice assistants, and search engines. In recent years, NLP has been widely used in developing intelligent Question Answering (QA) systems, which can understand and extract information from the user's natural language inputs to provide relevant and accurate answers. In this article, we will discuss the application of NLP in intelligent QA systems and highlight the challenges and potential future advancements.Chapter Two: NLP Techniques for QA SystemsThe core component of any QA system is the Natural Language Understanding (NLU) module, which is responsible for interpreting and analyzing the user's input. NLP techniques such as Named Entity Recognition (NER), Part-of-Speech (POS) tagging, and Semantic Role Labeling (SRL) are commonly used to extract information from the user's input. NER identifies and categorizes entities such as people, organizations, and locations. POS tagging helps identify the grammatical structure of a sentence, which is crucial for understanding the meaning of the user's input. SRL is used to identify the subject,predicate, and object of a sentence, which can help answer complex questions that require deeper understanding of the sentence structure.Chapter Three: QA System ArchitectureThe architecture of a QA system includes several modules, including preprocessing, parsing, semantic analysis, and answer generation. The preprocessing module is responsible for cleaning and transforming the user's input into a standard format. The parsing module uses syntax analysis to create a structured representation of the input. The semantic analysis module extracts the meaning of the query and links it to knowledge sources such as databases or knowledge graphs. Finally, the answer generation module generates a relevant and concise answer based on the analysis of the query and knowledge sources.Chapter Four: Challenges and Future DevelopmentsDespite the significant advancements in NLP, several challenges still exist in developing intelligent QA systems. One challenge is the lack of data and knowledge sources, which can limit the accuracy and scope of the system's answers. Another challenge is the ambiguity and complexity of human language, which can lead to inaccurate interpretations of the user's input. Improvements in machine learning techniques and the availability of massive datasets could address these challenges and improve the accuracy and performance of QA systems.Future developments in NLP could focus on enhancing the system's ability to understand the context and intent of the user's input. This could involve integrating contextual information such as user behavior or location into the QA system. Also, advancements in Natural Language Generation (NLG) could enable QA systems to generate complex and informative answers without relying on pre-defined templates.Chapter Five: ConclusionIn conclusion, NLP has become an essential component of intelligent QA systems, enabling them to understand and extract information from the user's natural language inputs to provide relevant and accurate answers. The application of NLP techniques such as NER, POS tagging, and SRL are critical in building the NLU module of QA systems. Developing intelligent QA systems also requires a well-designed architecture that includes several modules such as preprocessing, parsing, and semantic analysis. Although several challenges still exist, advancements in NLP could address these challenges and enhance the accuracy and performance of QA systems, enabling them to provide more informative and valuable answers to users.。
现在大多人使用互联网搜索答案英语作文
现在大多人使用互联网搜索答案英语作文Using the Internet to Find AnswersThe internet is really cool! It has so much information on pretty much everything. Whenever I have a question about something, I can just ask my parents if I can use their phone or computer to look it up online. A lot of times, they'll say yes because they know how useful the internet can be for learning.My favorite website for finding answers is probably Google. All you have to do is type in what you want to know about, and it gives you a huge list of websites that might have that information. My parents have taught me to try to pick the websites that seem the most trustworthy and factual, like sites from universities, libraries, museums, and well-known organizations. Some random personal blogs or sketchy sites might not always have accurate info.I've used Google to find answers for all kinds of questions - stuff for school projects, topics I'm just curious about, instructions for games or apps I'm playing, and more. Like when I was wondering how meteors and asteroids are different, I searched for that and found some great explanations and images from NASA's website. Or when I needed to know whatthe biggest desert in the world was for a geography assignment, I could discover that it's the Antarctic desert.The internet is also brilliant for looking up tips and tutorials for hobbies I'm interested in, like skateboarding tricks, magic tricks, or origami designs. There are so many videos andstep-by-step guides out there made by people who are really good at those activities. I've been able to learn a bunch of new skills thanks to those online resources.Another awesome way the internet provides answers is through question-and-answer sites and forums. If I can't seem to find what I need through a regular search, I can join a relevant online community for that topic and ask my specific question directly. Then people who are experts on that subject can chime in with their advice and knowledge. It's like having access to a huge group of teachers and mentors, but online.My parents have rules about what kinds of websites I'm allowed to go on, and they monitor my internet use. That's because they want to make sure I don't accidentally stumble across anything inappropriate or get drawn into internet tricks and scams. As long as I stick to the safe, approved websites for kids my age, then the internet is an amazing resource.Overall, using the internet to search for information and find answers to my questions has been incredibly helpful. It's so much quicker and more convenient than having to lug around heavy books and encyclopedias everywhere. I have the world's knowledge at my fingertips! I don't know what kids did before the internet existed. It must have been really hard to get answers and learn about new subjects if you couldn't just look it up online. The internet is the best for satisfying my curiosity on any random topic I can think of.。
基于结构数据的多模式智能问答消歧系统
IBM 的 Watson 项目将自然语言问句进行文本处理后,通过 DBpedia和Yago进行逻辑推理得到答案。2012年,Watson机器人 在著名智力竞赛节目"Jeopardy"战胜了人类。
本文提出了使用自然语言处理和图像检索两种方式对用户提供 的问句进行分析,在基于文本的智能问答技术上进行拓展,建立 多模式问答系统。在用户提出自然语言问句的同时,我们还允许 用户提供照片、手绘图等方式,表达其所想得到的答案的图像。
然后,我们对文本和图像处理进行并行处理,处理的结果在Yago 知识库上进行逻辑推理,通过线性优化的方式得到最优解。实验 表明,使用文本和图像两种方式,能够使得用户表达的信息更为 明确,更好的使机器理解用户的问句含义,对消除用户问句中实 体的歧义,非常有效果。
随着用户通过移动设备获取图像越来越便捷,我们扩展智能问答 技术的输入也成为可能,用户使用移动设备可以很便捷的同时提 供文本和图像进行查询,综合考虑自然语言和图像检索时可提升 问题回答的准确率。另外,本文研究也有助于推广利用多种交互 模式条件下的多媒体知识库。
另一类方法是试图通过知识库(Knowledge Bases)或知识图谱 (Knowledge Graph)方式来解答问题。早期基于知识库的智能问 答系统,如BASEBALL、SHRDLU和LUNAR,只能解决某特定领域内的 问题。
随着知识库的发展,智能问答技术逐渐从特定领域问答扩展到多 领域,如早期的依托手动创建的知识库的Unix Consultant和 LILOG系统。近年来,智能问答系统更多的依赖于关联数据网络 (web of linked data),如 DBpedia、Freebase 和 Yago 等。
随着在线多媒体内容的爆发式增长和移动设备的高度普及,将来 对多种交互模式(音频和视频)下的多媒体搜索领域的研究需求 会不断增长。
基于规则的中文阅读理解问题回答技术研究
基于规则的中文阅读理解问题回答技术研究李济洪;杨杏丽;王瑞波;张娜;李国臣【摘要】该文针对中文阅读理解问答中的时间、人物、地点、数值、实体、描述六类问题,制定了各类问题回答的启发式规则集.对规则集中每条规则赋予一个相应权值,利用正交表对各规则所对应的权值进行了调优选取,给出了各候选答案句基于相应规则的得分计算方法.该文方法在山西大学自主开发的中文阅读理解语料库CRCC v1.1上进行了实验,在整个语料库上得到了83.09%的HumSent准确率.为了与文献[10]中的最大熵方法比较,该文在与文献[10]中完全相同的训练集上调优规则的权值,在相同的测试集上测试,最终得到HumSent准确率81.13%,比最大熵的方法高大约1%,且在全部的六类问题上,该文方法的HumSent准确率都不低于最大熵方法.【期刊名称】《中文信息学报》【年(卷),期】2009(023)004【总页数】7页(P3-9)【关键词】计算机应用;中文信息处理;阅读理解;问答系统;规则;正交表【作者】李济洪;杨杏丽;王瑞波;张娜;李国臣【作者单位】山西大学,计算中心,山西,太原,030006;山西大学,数学科学学院,山西,太原,030006;山西大学,计算机与信息技术学院,山西,太原,030006;山西大学,数学科学学院,山西,太原,030006;山西大学,计算机与信息技术学院,山西,太原,030006【正文语种】中文【中图分类】TP391阅读理解问答系统（QARC）是由计算机自动分析一篇给定的自然语言文章，对每个针对本篇文章的问题，自动生成一个相应答案的系统。
QARC主要是通过问题回答的形式来测试计算机对一篇文章的理解程度。
从形式上看，QARC与外语考试中的阅读理解测试题一样，只不过QARC是让计算机自动给出答案。
一般而言，QA是面向大规模的文档集，要求系统有较好的检索技术和答案生成技术；而QARC一般不需要检索，主要是侧重研究各种类型、不同难度问题答案的寻找和生成技术。
社区问答系统中潜在回答者排序算法(英文)
社区问答系统中潜在回答者排序算法(英文)韩闻文;阙喜戎;宋思奇;田野;王文东【期刊名称】《中国通信：英文版》【年(卷),期】2013()10【摘要】Community Question Answering(CQA) websites have greatly facilitated users' lives, with an increasing number of people seeking help and exchanging ideas on the Internet. This newly-emerged community features two characteristics: social relations and an ask-reply mechanism. As users' behaviours and social statuses play a more important role in CQA services than traditional answer retrieving websites, researchers' concerns have shifted from the need to passively find existing answers to actively seeking potential reply providers that may give answers in the near future. We analyse datasets derived from an online CQA system named "Quora", and observed that compared with traditional question answering services, users tend to contribute replies rather than questions for help in the CQA system. Inspired by the findings, we seek ways to evaluate the users' ability to offer prompt and reliable help, taking into account activity, authority and social reputation characteristics. We propose a hybrid method that is based on a Question-User network and social network using optimised PageRank algorithm.Experimental results show the efficiency of the proposed method for ranking potential answer-providers.【总页数】12页(P125-136)【关键词】答疑系统;供应商;社区;PageRank算法;社会关系;回复;应答机制;研究人员【作者】韩闻文;阙喜戎;宋思奇;田野;王文东【作者单位】State Key Laboratory of Networking and Switching Technology, Beijing University of Posts and Telecommunications【正文语种】中文【中图分类】TP311.1;F274【相关文献】1.社区问答系统中“问答对”的质量评价 [J], 刘高军;马砚忠;段建勇2.社区问答系统中问题推荐机制 [J], 蒋宗礼;李立新3.社区问答系统中基于当前兴趣的问题推荐研究 [J], ZHAO Yongbiao;ZHANG Qilin;GU Qiong4.社区问答系统中基于当前兴趣的问题推荐研究 [J], 赵永标; 张其林; 谷琼5.机器学习分类算法在社区问答系统中的应用 [J], 孙熙然因版权原因，仅展示原文概要，查看原文内容请购买。
智能问答系统用户指南说明书
ficial building ontology is accurate, able to objectively reflect the relationship between the concepts, in the future to consider using machine automatic build and artificial building, make up for the inadequacy of artificial building.
[3] Lopez, V., Motta, E.: Ontology Driven Question Answerin Métais, E. (eds.) NLDB 2004. LNCS, vol. 3136,pp. 89–102, 2004.
[6] De Boni, M.: TREC 9 QA track overview
[7] Litkowski, K.C.: Syntactic Clues and Lexical Resources in QuestionAnswering. In: Voorhees, E. M., Harman, D. K (eds) Information Technology: The Ninth Text REtrieval Conferenence (TREC-9), NIST Special Publication 500-249. Gaithersburg, MD: National Institute of Standards and Technologypp. 157– 166, 2001,.
5. References
[1] Mc Guinness, D.: Question Answering on the Semantic Web. IEEE Intelligent Systems 19(1),pp. 82–85, 2004
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ACL-2002 Demonstration, Philadelphia, PA, U.S.A., July 7-12, 2002.Automated Question Answering in Webclopedia – A Demonstration Ulf Hermjakob, Eduard Hovy and Chin-Yew LinUSC Information Sciences Institute4676 Admiralty WayMarina del Rey, CA 90292-6695{ulf,hovy,cyl}@Tel: 310-448-8476, 310-448-8731 and 310-448-8711In this demonstration we present Webclopedia, a semantics-based question answering system accessible via the web (Hovy et al. 2002, 2001, 2000). Through a live interface (Figure 1), users can type in their questions or select a predefined question. The system returns its top 5 candidate answers, drawn from NIST’s TREC corpus, a collection of 1 million newspaper texts.Some key points:Webclopedia integrates IR and NLP components. Both symbolic and statistical techniques are employed. For example, the CONTEX parser’s grammar, the intermediate result ranking rules, and answer matching patterns are created by machine learning; the answer pinpointer uses hand-crafted matching rules.Like almost all modern QA systems, Web-clopedia uses a taxonomy of question/answer types. The QA Typology (Hovy et al 2002), one of the most extensive used in the literature, contains over 180 types, and is based on an analysis of 17,384 questions, plus subsequent extensions.The typology is at /natural-language/projects/Webclopedia/Taxonomy/taxon omy_toplevel.html.Webclopedia took part in NIST’s TREC QA evaluations, achieving MRR (mean reciprocal rank) scores of 31% in TREC9 (tried second place) and 45% in TREC10.Recent work at ISI has focused on developing Korean and Mandarin Chinese versions of Webclopedia, allowing the user to ask English questions and receive English answers from foreign-language text sources.Instead of using the TREC corpus as source, Webclopedia is being extended to also query the web, using commercial web search engines to provide documents with likely answer candidates.The system works as follows:•Question parsing: Using BBN’s IdentiFinder (Bikel et al., 1999), the CONTEX parser (Hermjakob 1997, 2001) produces a syntactic-semantic analysis of the question and determines the QA type(s) sought.•Query formation: Single- and multi-word units (content words) are extracted from theanalysis, and WordNet synsets are used forquery expansion. A series of Boolean queriesis formed.•IR: The IR engine MG (Witten et al., 1994) returns the top-ranked N documents. •Selecting and ranking sentences: For each document, the most promising K<<Nsentences are located and scored using a formula that rewards word and phrase overlapwith the question and its expanded query words. Results are ranked.•Parsing sentences: CONTEX parses the top-ranked 300 sentences.•Pinpointing: Each candidate answer sentence parse tree is matched against the parse of thequestion, with particular attention to the QAtype(s) sought. The matching patterns werebuilt by hand; additional patterns are learnedoff the web (Ravichandran and Hovy, 2002).As a fallback the window method is used.•Ranking of answers: The candidate answers’scores are computed and the topmost 5 areoutput as final answers.Figure 1. Webclopedia web interface (answers in red, matched portions in blue). ReferencesHermjakob, U. 1997. Learning Parse and Translation Decisions from Examples with Rich Context. Ph.D. dissertation, University of Texas, Austin.file:///pub/~mooney/papers/hermjakob-dissertation- 97.ps.gz.Hermjakob, U. 2001. Parsing and Question Classification for Question Answering. Proceedings of the ACL Workshop on Question Answering. Toulouse, France.Hovy, E.H., L. Gerber, U. Hermjakob, M. Junk, and C.-Y. Lin. 2000. Question Answering in Webclopedia. Proceedings of the TREC-9 Conference. NIST, Gaithersburg, MD.Hovy, E.H., L. Gerber, U. Hermjakob, C.-Y. Lin, and D. Ravichandran. 2001. Toward Semantics-Based Answer Pinpointing. Proceedings of the DARPA Human Language Technology Conference (HLT). San Diego, CA.Hovy, E.H., U. Hermjakob, and D. Ravichandran. 2002. A Question/Answer Typology with Surface Text Patterns. Poster in Proceedings of the Human Language Technology Conference. San Diego, CA. Ravichandran, D. and E.H. Hovy. 2002. Learning Surface Text Patterns for a Question Answering System. Proceedings of the ACL Conference. Philadelphia, PA.Witten, I. H. and A. Moffat and T. Bell 1994. Managing Gigabytes. New York: Van Nostrand Reinhold.。