【综述】最新7篇数据科学/深度学习/CNN/知识图谱/文本匹配等中英文综述论文推介（附下载）

2017 年 12 月 3 日 机器学习研究会 专知内容组（编）

【导读】专知内容组整理了最近人工智能领域相关期刊的7篇最新综述文章，为大家进行介绍，欢迎查看!

▌深度文本匹配综述

作者：庞亮兰艳艳徐君郭嘉丰万圣贤程学旗

摘要：自然语言理解的许多任务,例如信息检索、自动问答、机器翻译、对话系统、复述问题等等,都可以抽象成文本匹配问题.过去研究文本匹配主要集中在人工定义特征之上的关系学习,模型的效果很依赖特征的设计.最近深度学习自动从原始数据学习特征的思想也影响着文本匹配领域,大量基于深度学习的文本匹配方法被提出,作者称这类模型为深度文本匹配模型.相比于传统方法,深度文本匹配模型能够从大量的样本中自动提取出词语之间的关系,并能结合短语匹配中的结构信息和文本匹配的层次化特性,更精细地描述文本匹配问题.根据特征提取的不同结构,深度文本匹配模型可以分为3类:基于单语义文档表达的深度学习模型、基于多语义文档表达的深度学习模型和直接建模匹配模式的深度学习模型.从文本交互的角度,这3类模型具有递进的关系,并且对于不同的应用,具有各自性能上的优缺点.该文在复述问题、自动问答和信息检索3个任务上的经典数据集上对深度文本匹配模型进行了实验,比较并详细分析了各类模型的优缺点.最后该文对深度文本模型未来发展的若干问题进行了讨论和分析。

期刊：计算机学报 2017年4月第4期

网址：

http://cjc.ict.ac.cn/online/onlinepaper/pl-201745181647.pdf

▌Deep Convolutional Neural Networks for Image Classification - A Comprehensive Review

作者：Waseem Rawat Zenghui Wang

摘要：Convolutional neural networks (CNNs) have been applied to visual tasks since the late 1980s. However, despite a few scattered applications, they were dormant until the mid-2000s when developments in computing power and the advent of large amounts of labeled data, supplemented by improved algorithms, contributed to their advancement and brought them to the forefront of a neural network renaissance that has seen rapid progression since 2012. In this review, which focuses on the application of CNNs to image classification tasks, we cover their development, from their predecessors up to recent state-of-the-art deep learning systems. Along the way, we analyze (1) their early successes, (2) their role in the deep learning renaissance, (3) selected symbolic works that have contributed to their recent popularity, and (4) several improvement attempts by reviewing contributions and challenges of over 300 publications. We also introduce some of their current trends and remaining challenges.

期刊：Neural Computation 2017年09月

网址：

http://ieeexplore.ieee.org/document/8016501/

▌Data Science: A Comprehensive Overview

作者：Longbing Cao

摘要：The 21st century has ushered in the age of big data and data economy, in which data DNA, which carries important knowledge, insights, and potential, has become an intrinsic constituent of all data-based organisms. An appropriate understanding of data DNA and its organisms relies on the new field of data science and its keystone, analytics. Although it is widely debated whether big data is only hype and buzz, and data science is still in a very early phase, significant challenges and opportunities are emerging or have been inspired by the research, innovation, business, profession, and education of data science. This article provides a comprehensive survey and tutorial of the fundamental aspects of data science: the evolution from data analysis to data science, the data science concepts, a big picture of the era of data science, the major challenges and directions in data innovation, the nature of data analytics, new industrialization and service opportunities in the data economy, the profession and competency of data education, and the future of data science. This article is the first in the field to draw a comprehensive big picture, in addition to offering rich observations, lessons, and thinking about data science and analytics.

期刊：ACM Computing Surveys (CSUR) 2017年10月

网址：https://dl.acm.org/citation.cfm?

id=3076253&CFID=1012531090&CFTOKEN=28945038

▌Knowledge Graph Embedding: A Survey of Approaches and Applications

作者：Waseem Rawat Zenghui Wang

摘要：Knowledge graph (KG) embedding is to embed components of a KG including entities and relations into continuous vector spaces, so as to simplify the manipulation while preserving the inherent structure of the KG. It can benefit a variety of downstream tasks such as KG completion and relation extraction, and hence has quickly gained massive attention. In this article, we provide a systematic review of existing techniques, including not only the state-of-the-arts but also those with latest trends. Particularly, we make the review based on the type of information used in the embedding task. Techniques that conduct embedding using only facts observed in the KG are first introduced. We describe the overall framework, specific model design, typical training procedures, as well as pros and cons of such techniques. After that, we discuss techniques that further incorporate additional information besides facts. We focus specifically on the use of entity types, relation paths, textual descriptions, and logical rules. Finally, we briefly introduce how KG embedding can be applied to and benefit a wide variety of downstream tasks such as KG completion, relation extraction, question answering, and so forth.

期刊：IEEE Transactions on Knowledge and Data Engineering ( Volume: 29, Issue: 12, Dec. 1 2017 )

网址：

http://ieeexplore.ieee.org/document/8047276/

转自：专知内容组（编）

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