CSCI 5832Natural Language Processing
自然语言处理
自然语言处理自然语言处理（Natural Language Processing，简称NLP）是计算机科学与人工智能领域中涉及人类语言与计算机之间交互的一门研究领域。
它以人类语言为研究对象，通过构建模型和算法，使计算机能够理解、分析和生成自然语言，实现人机交互、信息检索、文本挖掘、机器翻译等应用。
自然语言处理的发展历程可以追溯到20世纪50年代。
早期的研究集中在词法分析和句法分析等基本层面的处理，随着计算能力的提升和统计机器翻译等方法的引入，自然语言处理逐渐取得了一些突破性的成果。
近年来，深度学习的兴起为自然语言处理带来了更大的突破，其中以神经网络为基础的模型在语义理解、情感分析和问答系统等任务上取得了显著的进展。
在实际应用中，自然语言处理常常需要面临一些挑战。
首先，人类语言是多样化和复杂的，存在着各种语言现象、词义歧义、语法结构等。
其次，文本数据的规模庞大，需要处理海量的语料库来训练和评估模型。
此外，不同的语言和文化背景也会对自然语言处理带来影响，需要考虑跨语言处理的问题。
自然语言处理的核心任务包括语言理解和语言生成。
语言理解主要涉及词性标注、命名实体识别、句法分析、语义角色标注、语义理解、情感分析等。
语言生成则包括机器翻译、文本摘要、情景对话生成等。
这些任务可以使用不同的模型和算法来实现，如统计机器学习、条件随机场、深度学习等。
自然语言处理在各种应用领域中发挥了重要作用。
在信息检索中，利用自然语言处理技术可以提高搜索引擎的准确性和效率。
在文本挖掘中，自然语言处理可以用于发现和分析文本中的模式和关系。
在智能对话系统中，自然语言处理是实现人机交互的关键技术。
此外，自然语言处理还广泛应用于文本分类、情感分析、自动问答、机器翻译、语音识别等领域。
然而，自然语言处理仍然存在一些挑战和限制。
例如，语义理解仍然是一个复杂的问题，尤其是在处理歧义和语义推理时。
另外，对于低资源语言和领域特定语言的处理仍然比较困难。
融合知识图谱与注意力机制的短文本分类模型
第47卷第1期Vol.47No.1计算机工程Computer Engineering2021年1月January2021融合知识图谱与注意力机制的短文本分类模型丁辰晖1，夏鸿斌1，2，刘渊1，2（1.江南大学数字媒体学院，江苏无锡214122；2.江苏省媒体设计与软件技术重点实验室，江苏无锡214122）摘要：针对短文本缺乏上下文信息导致的语义模糊问题,构建一种融合知识图谱和注意力机制的神经网络模型。
借助现有知识库获取短文本相关的概念集,以获得短文本相关先验知识,弥补短文本缺乏上下文信息的不足。
将字符向量、词向量以及短文本的概念集作为模型的输入,运用编码器-解码器模型对短文本与概念集进行编码,利用注意力机制计算每个概念权重值,减小无关噪声概念对短文本分类的影响,在此基础上通过双向门控循环单元编码短文本输入序列,获取短文本分类特征,从而更准确地进行短文本分类。
实验结果表明,该模型在AGNews、Ohsumed 和TagMyNews短文本数据集上的准确率分别达到73.95%、40.69%和63.10%,具有较好的分类能力。
关键词：短文本分类；知识图谱；自然语言处理；注意力机制；双向门控循环单元开放科学（资源服务）标志码（OSID）：中文引用格式：丁辰晖，夏鸿斌，刘渊.融合知识图谱与注意力机制的短文本分类模型［J］.计算机工程，2021，47（1）：94-100.英文引用格式：DING Chenhui，XIA Hongbin，LIU Yuan.Short text classification model combining knowledge graph and attention mechanism［J］.Computer Engineering，2021，47（1）：94-100.Short Text Classification Model Combining Knowledge Graph and Attention MechanismDING Chenhui1，XIA Hongbin1，2，LIU Yuan1，2（1.School of Digital Media，Jiangnan University，Wuxi，Jiangsu214122，China；2.Jiangsu Key Laboratory of Media Design andSoftware Technology，Wuxi，Jiangsu214122，China）【Abstract】Concerning the semantic ambiguity caused by the lack of context information，this paper proposes a neural network model，which combines knowledge graph and attention mechanism.By using the existing knowledge base to obtain the concept set related to the short text，the prior knowledge related to the short text is obtained to address the lack of context information in the short text.The character vector，word vector，and concept set of the short text are taken as the input of the model.Then the encoder-decoder model is used to encode the short text and concept set，and the attention mechanism is used to calculate the weight value of each concept to reduce the influence of unrelated noise concepts on short text classification.On this basis，a Bi-directional-Gated Recurrent Unit（Bi-GRU）is used to encode the input sequences of the short text to obtain short text classification features，so as to perform short text classification more effectively.Experimental results show that the accuracy of the model on AGNews，Ohsumed and TagMyNews short text data sets is73.95%，40.69%and63.10%，respectively，showing a good classification ability.【Key words】short text classification；knowledge graph；Natural Language Processing（NLP）；attention mechanism；Bi-directional-Gated Recurrent Unit（Bi-GRU）DOI：10.19678/j.issn.1000-3428.00567340概述近年来，随着Twitter、微博等社交网络的出现，人们可以轻松便捷地在社交平台上发布文本、图片、视频等多样化的信息，社交网络已超越传统媒体成为新的信息聚集地，并以极快的速度影响着社会的信息传播格局［1］。
中科院计算机类SCI期刊及分区(2016年10月发布)
期刊 IEEE TRANSACTIONS ON FUZZY SYSTEMS International Journal of Neural Systems IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE IEEE TRANSACTIONS ON EVOLUTIONARY COMPUTATION INTEGRATED COMPUTER-AIDED ENGINEERING IEEE Transactions on Cybernetics IEEE Transactions on Neural Networks and Learning Systems MEDICAL IMAGE ANALYSIS Information Fusion INTERNATIONAL JOURNAL OF COMPUTER VISION IEEE TRANSACTIONS ON IMAGE PROCESSING IEEE Computational Intelligence Magazine EVOLUTIONARY COMPUTATION IEEE INTELLIGENT SYSTEMS PATTERN RECOGNITION ARTIFICIAL INTELLIGENCE KNOWLEDGE-BASED SYSTEMS NEURAL NETWORKS EXPERT SYSTEMS WITH APPLICATIONS Swarm and Evolutionary Computation APPLIED SOFT COMPUTING DATA MINING AND KNOWLEDGE DISCOVERY INTERNATIONAL JOURNAL OF APPROXIMATE REASONING SIAM Journal on Imaging Sciences DECISION SUPPORT SYSTEMS Swarm Intelligence Fuzzy Optimization and Decision Making IEEE TRANSACTIONS ON KNOWLEDGE AND DATA ENGINEERING JOURNAL OF MACHINE LEARNING RESEARCH ACM Transactions on Intelligent Systems and Technology NEUROCOMPUTING ENGINEERING APPLICATIONS OF ARTIFICIAL INTELLIGENCE CHEMOMETRICS AND INTELLIGENT LABORATORY SYSTEMS ARTIFICIAL INTELLIGENCE IN MEDICINE COMPUTER VISION AND IMAGE UNDERSTANDING JOURNAL OF AUTOMATED REASONING INTERNATIONAL JOURNAL OF INTELLIGENT SYSTEMS COMPUTATIONAL LINGUISTICS ADVANCED ENGINEERING INFORMATICS JOURNAL OF INTELLIGENT MANUFACTURING Cognitive Computation IEEE Transactions on Affective Computing JOURNAL OF CHEMOMETRICS MECHATRONICS IEEE Transactions on Human-Machine Systems Semantic Web IMAGE AND VISION COMPUTING Wiley Interdisciplinary Reviews-Data Mining and Knowledge Discovery issn 1063-6706 0129-0657 0162-8828 1089-778X 1069-2509 2168-2267 2162-237X 1361-8415 1566-2535 0920-5691 1057-7149 1556-603X 1063-6560 1541-1672 0031-3203 0004-3702 0950-7051 0893-6080 0957-4174 2210-6502 1568-4946 1384-5810 0888-613X 1936-4954 0167-9236 1935-3812 1568-4539 1041-4347 1532-4435 2157-6904 0925-2312 0952-1976 0169-7439 0933-3657 1077-3142 0168-7433 0884-8173 0891-2017 1474-0346 0956-5515 1866-9956 1949-3045 0886-9383 0957-4158 2168-2291 1570-0844 0262-8856 1942-4787
Natural Language Processing Techniques
Natural Language Processing Techniques Natural Language Processing (NLP) TechniquesNatural Language Processing (NLP) is a subfield of artificial intelligence that focuses on the interaction between computers and humans using natural language. In recent years, NLP techniques have made significant advancements in various applications such as sentiment analysis, chatbots, machine translation, and speech recognition. In this article, we will explore some of the most commonly used NLP techniques and their applications.1. Tokenization: Tokenization is the process of breaking down a text into individual words, phrases, or symbols known as tokens. This technique is essential for many NLP tasks as it helps to convert unstructured text data into a structured format that can be easily processed by machines. Tokenization can be done at different levels, such as word level, sentence level, or character level.2. Part-of-Speech (POS) tagging: POS tagging is the process of assigning a grammatical category (noun, verb, adjective, etc.) to each word in a sentence. This technique helps in understanding the syntactic structure of a sentence and is crucial for tasks like named entity recognition, sentiment analysis, and machine translation.3. Named Entity Recognition (NER): Named Entity Recognition is the task of identifying and classifying named entities (such as names of people, organizations, locations, etc.) in a text. NER is widely used in information extraction, question answering systems, and social media analysis.4. Sentiment Analysis: Sentiment analysis is the process of determining the sentiment expressed in a piece of text, whether it is positive, negative, or neutral. This technique is commonly used in social media monitoring, customer feedback analysis, and brand reputation management.5. Machine Translation: Machine translation is the task of translating text from one language to another automatically. NLP techniques such as neural machine translation have significantly improved the accuracy and fluency of machine translation systems.6. Text Classification: Text classification is the process of categorizing text data into predefined categories or classes. This technique is widely used in spam detection, topic categorization, and sentiment analysis.7. Information Extraction: Information extraction is the process of automatically extracting structured information from unstructured text data. This technique is used in various domains such as web scraping, document summarization, and question answering systems.8. Summarization: Text summarization is the task of generating a concise and coherent summary of a longer text. NLP techniques such as extractive and abstractive summarization have been widely used in news summarization, document summarization, and keyword extraction.9. Word Embeddings: Word embeddings are vector representations of words in a continuous vector space. This technique allows us to capture semantic relationships between words and is crucial for tasks like named entity recognition, sentiment analysis, and machine translation.10. Speech Recognition: Speech recognition is the task of automatically converting spoken language into text. NLP techniques such as acoustic modeling and language modeling have significantly improved the accuracy and performance of speech recognition systems.In conclusion, natural language processing techniques have revolutionized the way we interact with machines and have enabled a wide range of applications in various domains. As NLP continues to evolve and innovate, we can expect even more advanced applications and capabilities in the future.。
CCF推荐的国际学术会议和期刊目录修订版发布
CCF推荐的国际学术会议和期刊目录修订版发布CCF(China Computer Federation中国计算机学会)于2010年8月发布了第一版推荐的国际学术会议和期刊目录，一年来，经过业内专家的反馈和修订，于日前推出了修订版，现将修订版予以发布。
本次修订对上一版内容进行了充实，一些会议和期刊的分类排行进行了调整，目录包括：计算机科学理论、计算机体系结构与高性能计算、计算机图形学与多媒体、计算机网络、交叉学科、人工智能与模式识别、软件工程/系统软件/程序设计语言、数据库/数据挖掘/内容检索、网络与信息安全、综合刊物等方向的国际学术会议及期刊目录，供国内高校和科研单位作为学术评价的参考依据。
目录中，刊物和会议分为A、B、C三档。
A类表示国际上极少数的顶级刊物和会议，鼓励我国学者去突破；B类是指国际上著名和非常重要的会议、刊物，代表该领域的较高水平，鼓励国内同行投稿；C类指国际上重要、为国际学术界所认可的会议和刊物。
这些分类目录每年将学术界的反馈和意见，进行修订，并逐步增加研究方向。
中国计算机学会推荐国际学术刊物（网络/信息安全）一、 A类序号刊物简称刊物全称出版社网址1. TIFS IEEE Transactions on Information Forensics andSecurity IEEE /organizations/society/sp/tifs.html2. TDSC IEEE Transactions on Dependable and Secure ComputingIEEE /tdsc/3. TISSEC ACM Transactions on Information and SystemSecurity ACM /二、 B类序号刊物简称刊物全称出版社网址1. Journal of Cryptology Springer /jofc/jofc.html2. Journal of Computer SecurityIOS Press /jcs/3. IEEE Security & Privacy IEEE/security/4. Computers &Security Elsevier http://www.elsevier.nl/inca/publications/store/4/0/5/8/7/7/5. JISecJournal of Internet Security NahumGoldmann. /JiSec/index.asp6. Designs, Codes andCryptography Springer /east/home/math/numbers?SGWID=5 -10048-70-35730330-07. IET Information Security IET /IET-IFS8. EURASIP Journal on InformationSecurity Hindawi /journals/is三、C类序号刊物简称刊物全称出版社网址1. CISDA Computational Intelligence for Security and DefenseApplications IEEE /2. CLSR Computer Law and SecurityReports Elsevier /science/journal/026736493. Information Management & Computer Security MCB UniversityPress /info/journals/imcs/imcs.jsp4. Information Security TechnicalReport Elsevier /locate/istr中国计算机学会推荐国际学术会议（网络/信息安全方向）一、A类序号会议简称会议全称出版社网址1. S&PIEEE Symposium on Security and Privacy IEEE /TC/SP-Index.html2. CCSACM Conference on Computer and Communications Security ACM /sigs/sigsac/ccs/3. CRYPTO International Cryptology Conference Springer-Verlag /conferences/二、B类序号会议简称会议全称出版社网址1. SecurityUSENIX Security Symposium USENIX /events/2. NDSSISOC Network and Distributed System Security Symposium Internet Society /isoc/conferences/ndss/3. EurocryptAnnual International Conference on the Theory and Applications of Cryptographic Techniques Springer /conferences/eurocrypt2009/4. IH Workshop on Information Hiding Springer-Verlag /~rja14/ihws.html5. ESORICSEuropean Symposium on Research in Computer Security Springer-Verlag as.fr/%7Eesorics/6. RAIDInternational Symposium on Recent Advances in Intrusion Detection Springer-Verlag /7. ACSACAnnual Computer Security Applications ConferenceIEEE /8. DSNThe International Conference on Dependable Systems and Networks IEEE/IFIP /9. CSFWIEEE Computer Security Foundations Workshop /CSFWweb/10. TCC Theory of Cryptography Conference Springer-Verlag /~tcc08/11. ASIACRYPT Annual International Conference on the Theory and Application of Cryptology and Information Security Springer-Verlag /conferences/ 12. PKC International Workshop on Practice and Theory in Public Key Cryptography Springer-Verlag /workshops/pkc2008/三、 C类序号会议简称会议全称出版社网址1. SecureCommInternational Conference on Security and Privacy in Communication Networks ACM /2. ASIACCSACM Symposium on Information, Computer and Communications Security ACM .tw/asiaccs/3. ACNSApplied Cryptography and Network Security Springer-Verlag /acns_home/4. NSPWNew Security Paradigms Workshop ACM /current/5. FC Financial Cryptography Springer-Verlag http://fc08.ifca.ai/6. SACACM Symposium on Applied Computing ACM /conferences/sac/ 7. ICICS International Conference on Information and Communications Security Springer /ICICS06/8. ISC Information Security Conference Springer /9. ICISCInternational Conference on Information Security and Cryptology Springer /10. FSE Fast Software Encryption Springer http://fse2008.epfl.ch/11. WiSe ACM Workshop on Wireless Security ACM /~adrian/wise2004/12. SASN ACM Workshop on Security of Ad-Hoc and Sensor Networks ACM /~szhu/SASN2006/13. WORM ACM Workshop on Rapid Malcode ACM /~farnam/worm2006.html14. DRM ACM Workshop on Digital Rights Management ACM /~drm2007/15. SEC IFIP International Information Security Conference Springer http://sec2008.dti.unimi.it/16. IWIAIEEE International Information Assurance Workshop IEEE /17. IAWIEEE SMC Information Assurance Workshop IEEE /workshop18. SACMATACM Symposium on Access Control Models and Technologies ACM /19. CHESWorkshop on Cryptographic Hardware and Embedded Systems Springer /20. CT-RSA RSA Conference, Cryptographers' Track Springer /21. DIMVA SIG SIDAR Conference on Detection of Intrusions and Malware and Vulnerability Assessment IEEE /dimva200622. SRUTI Steps to Reducing Unwanted Traffic on the Internet USENIX /events/23. HotSecUSENIX Workshop on Hot Topics in Security USENIX /events/ 24. HotBots USENIX Workshop on Hot Topics in Understanding Botnets USENIX /event/hotbots07/tech/25. ACM MM&SEC ACM Multimedia and Security Workshop ACM。
Natural Language Processing
Testing against natural language RequirementsHarry M. SneedAnecon GmbH, Vienna, AustriaEmail: Harry.Sneed@t-online.atAbstract:Testing against natural language requirements is the standard approach for system and acceptance testing. This test is often performed by an independent test organization unfamiliar with the application area. The only things the testers have to go by are the written requirements. So it is essential to be able to analyze those requirements and to extract test cases from them. In this paper an automated approach to requirements based testing is presented and illustrated on an industrial application.Keywords: Acceptance Testing, System Testing, Requirements Analysis, Test Case Generation, Natural Language ProcessingA test is always a test against something. According to the current test literature this something can be the code itself, the design documentation, the data interfaces, the requirements or the unwritten expectations of the users [1]. In the first case, one is speaking of code based testing where the test cases are actually extracted from an analysis of the code. In the second case, one is speaking of design based testing where test cases are taken from the design documents, e.g. the UML diagrams. In the third case, we speak of data based testing, where test cases are generated from the data structures, e.g. the SQL schema or the XML schema. In the fourth case, we speak of requirements based testing, where we extract the test cases from the requirements documents. This is also known as functional testing. In the fifth and final case, we speak of user based testing, in which a representative user invents test cases as he goes along. This is also referred to as creative testing [2].Another form of testing is regression testing in which a new version of a previous system is tested against the older version. Here the test cases are taken from the old data or from the behavior of the old system. In both cases one is comparing the new with the old, either entirely or selectively [3].1. Functional testingIn this paper the method of requirements based testing is being described, i.e. testing against the functional and non functional requirements as defined in an official document. This type of testing is used primarily as a final system test or as an acceptance test. Bill Howden referred to this as functional testing [4]. It assumes that other kinds of testing, such as code based unit testing and/or design based integration testing have already taken place so that the software is executable and fairly reliable. It is then the task of the requirements based test to demonstrate that the system does what it should do according to the written agreement between the user organization and the developing organization. Very often this test is performed by an independent test organization so as to eliminate any bias. The test organization is called upon not only to test, but also to interpret the meaning of the requirements. In this respect, the requirements are similar to laws and the testers are performing the roles of a judge, whose job it is to interpret the laws to apply to a particular case [5].What laws and requirements have in common is that they are both written in natural language and they are both fuzzy. Thus, they are subject to multiple interpretations. Judges are trained to interpret laws. Testers are not always prepared to interpret requirements. However, in practice this is the essence of their job. Having an automated tool to dissect the requirement texts and to distinguish between different types of requirement statements is a first step in the direction of automated requirements testing. The Text Analyzer is intended to be such a tool.2. Nature of natural language requirementsBefore examining the functions of a requirement analysis tool, it is first necessary to investigate the nature of requirement documents. There may be certain application areas where requirements are written in a formal notation. There are languages for this, such as VDM, SET and Z, and more recently OCL the object Constraint Language propagated by the OMG [6]. However, in the field of information technology such formal methods havenever really been accepted. There, the bulk of the requirements are still written in prose text.Christof Ebert distinguishes between unstructured text, structured text and semi formal text [7]. In a structured text the requirements are broken down into prescribed chapters and sections with specific meanings. A good example of this is the ANSI/IEEE-830: Guide to Requirements Specification. It prescribes a nested hierarchy of topics including the distinction between functional and non functional requirements [8]. Functional Requirements are defined in terms of their purpose, their sequence, their preconditions and post conditions as well as their inputs and outputs. Inputs are lists of individual arguments and outputs lists of results. Arguments and results may be even defined in respect to their value ranges. This brings the functional specification very close to a functional test case.Non functional requirements are to be defined in terms of their pass and fail criteria. Rather than depicting what flows in and out of a function, a measurable goal is set such as a response time of less than 3 seconds. Each non functional requirement may have one or more criteria which have to be fulfilled in order for the requirement to be fulfilled. In addition to the functional and non functional requirements of the product, the ANSI/IEEE standard also stipulates that constraints, risks and other properties of the projects be defined. The end result is a highly structured document with 7 sections. Provided that standard titles or standard numbering is used, a text analysis tool should easily recognize what is being described even if the description itself is not interpretable. By having such a structured document a tester has an easier job of extracting test cases. The job becomes even easier if the structured requirements are supplemented by acceptance criteria as proposed by Christiana Rupp and others [9]. After every functional and non functional requirement a rule is defined for determining whether the requirement is fulfilled or not. Such a rule could be that in case of a withdrawal from an account, the balance has to be less than the previous balance by the withdrawal amount. Account = Account@pre Withdrawal;An acceptance criterion is equivalent to a post condition assertion so that it can be readily copied into a test case definition.Semi formal requirements go one step further. They have their text content placed in a specific format, such as the use case format. Use cases are typical semi formal descriptions. They have standardized attributes which the requirement writer must fill out, attributes like trigger, rule, precondition, post condition, paths, steps and relations to other use cases. In the text these attributes will always have the same name so that a tool can readily recognize them. Most of the use cases are defined in standard frameworks or boxes which make it even easier to process them [10].A good semi formal requirements document will also have links between the use cases and the functional requirements. Each requirement will consist of a few sentences and will have some kind of number or mnemonic identifier to identify it. This identifier will then be referred to by the use case. One use case can fulfill one or more functional requirements. One attribute of the use case will be a list of such pointers to the requirements it fulfills [11].At the upper end of a semiformal requirement specification arithmetic expressions or logical conditions may be formulated. Within an informal document there can be scattered formal elements. These should be recognizable to an analysis tool.In the current world of information technology, the requirement documents range from structured to semi formal. Even the most backward users will have some form of structured requirements document in which it is possible to distinguish between individual functional requirements as well as between constraints and non functional requirements. More advanced users will have structured, semi formal documents in which individual requirements are numbered, use cases are specified with standardized attributes, and processing rules are defined in tables. Really sophisticated requirement documents such as can be found in requirements engineering tools like Doors and Rational Requisite Pro will also have links between requirements, rules, objects and processes, i.e. use cases [12].3. The Testing StrategyA software system tester in industry is responsible for demonstrating that a system does what it is supposed to do. To accomplish this, he must have an oracle to refer to. The concept of an automated oracle for functional testing was introduced by Howden in 1980 [13]. As foreseen by Howden then,the test oracle was to be a formal specification in terms of pre and post conditions. However the oracle could also be a natural language text provided the text is structured and has some degree of formality. In regression testing the oracle is the input and output data of the previous version. In unit testing it is the pre and post conditions of the methods and the invariant states of the objects. In integration testing it is the specification of the interfaces and in system testing it is the requirements document [14]. Thus, it is the task of the system tester to extract test cases from the functional and non functional requirements. Using this as a starting point, he then proceeds to carry out seven steps on the way to achieving confidence in the functionality of a software system. These seven steps are:identifying the test casescreating a test designspecifying the test casesgenerating the test casessetting up the test environmentexecuting the testevaluating the test.3.1 Identifying the test casesHaving established what it is to be tested against, i.e. the test oracle, it is first up to the tester to analyze that object and to identify the potential test cases. This is done by scanning through the document and selecting all statements about the behavior of the target system which need to be confirmed. These statements can imply actions or states, or they define conditions which have to be fulfilled if an action is to take place or a state is to hold [15].Producing a customer reminder is an action of the system. The fact that the customer account is overdrawn is a state. The rule that when a customer account is overdrawn the system should produce a customer reminder is a condition. All three are candidates for a test case. Testing whether the system produces a customer reminder is one test case. Testing if the customer account can be overdrawn is another test case, and testing whether the system produces a customer reminder when the customer account is overdrawn is a test case which combines the other two.In scanning the requirements document the tester must make sure to recognize each action to be performed, each state which may occur and each condition under which an action is performed or a state occurs. From these statements the functional test cases are extracted. But not only the functional test cases. Statements like the response time must be under 3 seconds and the system must recognize any erroneous input data are non functional requirements which must be tested. Every statement about the system, whether functional or non functional is a potential test case. The tester must recognize and record them [16].3.2. Creating a test designOf course, this is only the beginning of a system test. Once the test cases have been defined they must be ordered by time and place and grouped by test session. A test session encompasses a series of test cases performed within one dialog session or one batch process. In one session several requirements and several related use cases are executed. The test cases can be run sequentially or in parallel. The result of this test case ordering by execution sequence is part of the test design.3.3 Specifying the test casesFollowing the test design is the test case specification. This is where the attributes of the test cases are filled out in detail down to the level of the input and output data ranges. Each test case will already have an identifier, a purpose, a link to the requirements, objects and use cases it tests, as well as a source, a type and a status. It may even have a pre and post condition depending on how exact the requirements are. Now it is up to the tester to design the predecessor test cases, the physical interface or database being tested and to assign data values. Normally the general test case description will be in a master table whereas the input and output values will be in sub tables one for the test inputs and one for the expected outputs. In assigning the data, the tester will employ such techniques as equivalence classes, representative values, boundary values and progression or degression intervals. Which technique is used, depends on the type of data. In the end there will be for each test case a set of arguments and results [17].3.4 Generating the test dataProvided the test data definitions are made with a formal syntax, the test data itself can then be automatically generated. The tester may only have to oversee and guide the test data generation process. The basis for the test data generation will be the interface descriptions such as HTML forms, XML schemas, WSDL specifications and SQL database schemas. The values extracted from the test case, specifications are united with the structuralinformation provided by the data definition formats to create test objects, i.e. GUI instances, maps, records, database tables and other forms of test data [18].3.5 Setting up the test environmentIn the 5th step the test environment is prepared. Test databases must be allocated and filled with the generated data. Test work stations are loaded with the client software and the input test objects. The network is activated. The server software is initialized. The source code may be instrumented for tracing execution and test coverage.3.6 Execution the testNow the actual test can be started, one session at a time or several sessions in parallel depending on the type of system under test. The system tester will be either submitting the input data manually or operating a tool for submitting the data automatically. The latter approach is preferable since it is not only much faster, but also more reliable and above all repeatable. While the test is running the execution paths are being monitored and the test coverage of the code is being measured.3.7 Evaluating the testAfter each test session or test run the tester should perform an analysis of the test results. This entails several sub tasks. One sub task will be to report any incidents which may have occurred during the test session. Another task will be to record and document the functional test coverage. A third and vital task is to confirm the correctness of the data results, i.e. the post conditions. This can and should be done automatically by comparing the actual results with the expected results as specified in the test cases. Any deviations between the actual and the specified data results should be reported. Finally the tester will want to record various test metrics such as the number of test cases executed, the number of requirements tested, the number of data validated, the number of errors recorded and the degree of test coverage achieved [19].4. Automating the requirement analysisAs can be gathered from this summary of the system tester s tasks, there are many tasks which lend themselves to automation. Both test data generation and test data validation can be automated. Automated test execution has been going on for years and there are several tools for performing this. What are weakly automated are the test case specification and the test design. Not automated at all are the activities setting up the test environment and identifying the test cases [20].The focus of this paper is on the latter activity, i.e., identifying and extracting test cases. It is the first and most important task in functional system testing. Since the test we are discussing here is a requirements based test, the test cases must be identified in and extracted from the requirements document.The tool for doing that is the text analyzer developed by the author. The same tool goes on to create a test design, thus covering the first two steps of the system testing process. The Text Analyzer was conceived to do what a tester should do when he begins a requirements based system test. It scans through the requirements text to pick out potential test cases.4.1 Recognizing and selecting essential objects The key to requirements analysis is to have a natural language processor which extracts information from the text based on key words and sentence structure. This is referred to as text mining, a technique used by search engines on the internet. [21] The original purpose of text mining was to automatically index documents for classification and retrieval. The purpose here is to extract test cases from natural language text.Test cases relate to the objects of a system. Objects in a requirement document are either acted upon or their state is checked. Therefore, the first step of the text analysis is to identify the pertinent objects. For this all of the nouns must be identified. This is not an easy task, especially in the English language, since nouns can often be verbs or compound words such as master record. In this respect other languages such as German and Hungarian are more precise. In German nouns begin with a capital letter which makes the object recognition even easier.A pre scanner can examine the text to identify and record all nouns. However, only the human analyst can determine which nouns are potential objects based on the context in which they are used. To this end all of the nouns are displayed in a check box and the user can uncheck all nouns which he perceives to be irrelevant. The result is a list of pertinent nouns which can be recorded as the essential objects. Depending on the scope of the requirementsdocument their number can be anywhere from 100 to 1000.Besides that, object selection is apt to trigger a lengthy and tedious discussion among the potential users about which objects are relevant and which are not. In presenting the list of potential objects it becomes obvious, how arbitrary software systems are. In order to come up with an oracle to test against, the users must first come to a consensus on what the behavior of the system should be. Confronting them with the contradictions in their views helps to establish that consensus. [22]4.2 Defining key words in contextAs a prerequisite for the further text analysis, the user must identify the key words used in the requirement text. These key words can be any string of characters, but they must be assigned a predefined meaning. This is done through a key word table. There are currently some 20 predefined notions which can be assigned to a key word in the text. These are:SKIP= ignore lines beginning with this word REQU= this word indicates a requirementMASK= this word indicates a user interfaceINFA= this word indicates a system interface REPO = this word indicates a reportINPT = this word indicates a system inputOUTP = this word indicates a system outputSERV = this word indicates a web serviceDATA = this word indicates a data storeACT= this word indicates a system actorTRIG= this word indicates a triggerPRE= this word indicates a preconditionPOST= this word indicates a post condition PATH= this word indicates a logical path or sequence of stepsEXCP= this word indicates an exception condition ATTR = this word indicates any user assigned text attributeRULE= this word indicates a business rule PROC = this word indicates a business process GOAL = this word indicates a business goalOBJT= this word is the name of an object.By means of the key words, the analyzer is able to recognize certain requirement elements embedded in the prose text.4.3 Recognizing and extracting potential test cases The next step is for the tool to make a second scan of the document. This time only sentences in which an essential object occurs are processed, the others are skipped over. Each sentence selected is examined whether it is an action, a state query, or a condition. The sentence is an action when the object is the target of a verb. The sentences The customer account is updated daily and The system updates the customer account are both actions. The account is the object and updates is the action. The test case will be to test whether the system really updates the account.The sentence The account is overdrawn when the balance exceeds the credit limit is a state which needs to be tested and the sentence If an account is overdrawn, it should be frozen until a payment comes in is a condition combining an object state with an object action. The object is the account. The state is overdrawn. The action is should be frozen. There are actually two tests here. One is to confirm that an account can become overdrawn. The other is to confirm that an account is frozen when it is overdrawn.To qualify as a statement to be tested, a sentence must contain at least one relevant object. In the sentence If his credit rating is adequate, the customer can order a book. there are three relevant objects - credit, customer and book - so this qualifies the sentence to be processed further. The clause if his credit rating is adequate indicates that this is a condition which needs to be tested. There are many words which can be used to identify a condition. Besides the word if there are other words like should, provided, when, etc. there are also word patterns like in case of and as long as. When they occur the statement is assumed to be a condition.If the sentence is not a condition it may be a state declaration. A state declaration is when a relevant object is declared to be in a given state, i.e.The customer must be registered.The word customer is a selected object and the word pattern be registered indicates a state that the object is in. Predicate verbs such as be, is, are, were, etc denote that this is a state declaration.If the sentence is neither a condition nor a state, it may be an action. An action is indicated by a verb which acts upon a selected object e.g.The system checks the customer order.Here the order is a relevant object and checks is a verb which acts upon it. Normally these verbs will end with an s if they are in present tense and with ed if they are in past tense. So this makes it easier to recognize them. The advantage of requirement texts as opposed to texts in general is that they are almost always written in the third person, thus reducing the number of verb patterns to be checked. Sentences which qualify as a statement to be tested are extracted from the text and stored in the test case table. Assuming that all sentences are embedded in the text of a section,, a requirement or a use case, it is possible to assign test cases to individual requirements, use cases or simply to titled sections. If a test case originates from a requirement it receives the number or title of that requirement. If the test cases are created from a use case, then they bear the title of that use case. If these structural elements are missing the test case is simply assigned to the last text title. Relations are also established between test cases and objects. Test cases extracted from a particular sentence will have a reference to the objects referred to in that sentence.A generated test case will have an id, a purpose, a trigger, a pre-condition and a post-condition. The id of the test case is generated from the system name and a running number. The condition if the customer s credit rating is adequate, he can order a book implies two pre conditions1.the customer s credit rating is adequate2.the customer s credit rating is not adequate There are also two post conditions1.the customer has ordered a book2.the customer has not ordered a bookThis shows that for every conditional clause there should be two test casesone which fulfils the condition, andanother which does not fulfil the condition. They both have the same trigger, namely the customer orders a book.These are samples of functional test cases. Non functional test cases are all either states or conditions. The sentence The system should be able to process at least 2000 transactions per hour is a state denoted by the verb should be. The sentence In case of a system crash, the system has to be restarted within 2 minutes is a condition determined by the predicate In case of, followed by an action restarted. Both requirements must be tested. The tool itself can only distinguish between functional and non functional test cases based on the objects acted on or whose state is checked. Here again the user must interact by marking those objects such as system which are not part of the actual application.4.4 Storing the potential test casesThe result of the text analysis is a table of potential system test cases. If the requirements document is structured so that the individual requirements are recognizable, the test cases will be ordered by requirement. If there are use case definitions, the test cases extracted from a particular use case will be associated with that use case. Otherwise, the test cases will be grouped by subtitles.In the end every test case, whether functional or non-functional will have at least the following attributes:a test case Ida test case purpose = the sentence fromwhich the case was takena test case type = {action | state | condition }a preconditiona post conditiona triggera reference to the objects involveda reference to the requirements being testeda reference to the use case being tested5. Generating a test designIt is not enough to extract potential test cases. The test cases also need to be assigned to an overall test framework. The test framework is derived from the structure of the requirements document. Requirements should be enhanced by events. An event is something which occurs at one place at one time. Use cases are such events. An account withdrawal is an example of a use case event. A money transfer is another event. Printing out an account statement is yet another event. Events are triggered by a user, by the system itself or by some other system.In system testing it is essential to test every event, first independently of the other events and then in conjunction with them. An event will have at least two test cases - a positive and a negative outcome, but it may have many. In the case of an account withdrawal, the user may give in a bad PIN number, he may have an invalid card, the amount to be withdrawn may exceed the daily limit or his account may be frozen. There are usually 4 to 20 test cases for each use case.In generating a test design the text analyzer tool orders the test cases by event. The event is the focus of a testing session. Requirements and essential objects are assigned to an event so that it becomes clear which functions and which objects are tested within a session. If recognizable in the requirements text, the user or system interface for an event is also assigned. This grouping of all relevant information pertaining to an event is then presented in an XML document for viewing and editing by the tester. In so doing, the text analyzer has not only extracted the potential test cases from the requirements, it has also generated a test design based on the events specified.6. Experience with automated requirements analysisThe German language version of the text analyzer was first employed in a web application for the state of Saxony at the beginning of 2005. The requirements of that application were split up among 4 separate documents with 4556 lines of text. Some 677 essential objects were identified. Specified with these objects were 644 actions, 103 states and 114 rules. This led to 1103 potential test cases in 127 use cases. The generated test case table served as a basis for the test case specification. As might be expected, several test cases were added, so in the end there were 1495 test cases to be tested. These test cases revealed 452 errors in the system under test as opposed to the 96 errors discovered in production giving an error discovery rate of 89%. This demonstrated that the automatic extraction of test cases from requirements documents, complemented by manual test case enhancement is a much cheaper and more efficient way of exposing errors than a pure manual test case selection process [23]. Besides that it achieves higher functional test coverage. In this project over 95% of the potential functions were covered.Since this first trial in the Saxon e-Government project the German language version has been employed in no less than 12 projects to generate test cases from the requirements text including a project to automate the administration of the Austrian Game Commission, a project to introduce a standard software package for administering the German water ways, and a project to develop a university web site for employment opportunities.The English language version has only recently been completed, but has already been used in 3 projects once for analyzing the use cases of a mobile phone billing system, secondly for analyzing the requirements of an online betting system, and thirdly to generate test cases for a Coca Cola bottling and distribution system. In the case of the mobile billing system, a subsystem with 7 use cases was analyzed in which there were 78 actions and 71 rules for 68 objects rendering 185 test cases. The online betting system had 111 requirements of which 89 were functional and 22 were non-functional. There were 69 states, 126 actions and 112 rules for 116 specified objects from which 304 test cases were extracted. The specification of the Coca Cola distribution system is particularly interesting because it used neither a list of requirements nor a set of use cases, but instead a table of outputs to a relational database. In the first column of the table was the name of the output data, in the second the data description, in the third the algorithm for creating the data and in the fourth the condition for triggering the algorithm. A typical output specification is depicted in Table 1. Name Definition Source ConditionA400TotalNumber ofBottlesXX20QuantityfromMobileDeviceTranstype<5(Sampling)Transtype >7(Breakage)ARTIDF =1Aor1RTable 1: Output SpecificationFor this one output 6 test cases were generated Transtype <5 & ARTIDF = 1ATranstype <5 & ARTIDF = 1RTranstype <5 & ARTIDF ! = 1A & ARTIDF ! = 1RTranstype > 7 & ARTIDF = 1ATranstype > 7 & ARTIDF = 1ATranstype > 7 & ARTIDF = 1RTranstype > 7 & ARTIDF ! = 1A & ARTIDF ! = 1R。
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Comput Math*J Comput Sci Technol*J Environ Sci*J Forest Res*J Geoographical Sci*J Syst Eng Electronics*J Syst Sci Complexity*机器人机械传动*机械工程材料*机械工程学报*机械科学与技术机械强度机械设计机械设计与研究*基础医学与临床*激光技术激光生物学报激光与光电子学进展激光与红外*激光杂志*吉林大学学报.地球科学版吉林大学学报.工学版*吉林大学学报.理学版*吉林大学学报.医学版*吉林农业大学学报吉林农业科学*极地研究*计量学报计算机仿真*计算机辅助设计与图形学学报*环境化学*环境科学*环境科学学报*环境科学研究环境科学与技术环境污染与防治*环境污染治理技术与设备环境与健康杂志黄渤海海洋*会计研究火工品火灾科学火炸药学报*计算机科学计算机系统应用*计算机学报*计算机研究与发展*计算机应用*计算机应用研究*计算机与应用化学计算机自动测量与控制*计算力学学报*计算数学*计算物理*暨南大学学报.自然科学与医学版建筑材料学报*建筑结构*建筑结构学报江汉石油学院学报*江苏农业学报江苏医药江西农业大学学报江西医学院学报交通运输工程学报*结构化学解放军理工大学学报.自然科学版*解放军医学杂志*解剖学报*解剖学杂志*金融研究*金属热处理*金属学报经济数学*精细化工*计算机工程计算机工程与科学*计算机工程与应用*计算机集成制造系统K科技通报*科学通报*科学学研究*科研管理*空间科学学报空军工程大学学报.自然科学版*空气动力学学报*控制理论与应用*控制与决策L莱阳农学院学报*兰州大学学报.自然科学版*离子交换与吸附理化检验.化学分册*力学季刊*力学进展*力学学报力学与实践*量子电子学报量子光学学报辽宁农业科学林产工业M麦类作物学报*煤炭学报*煤炭转化煤田地质与勘探*棉花学报N*Nucl Sci Tech*内蒙古大学学报.自然科学版内蒙古农业大学学报.自然科学版内燃机工程*内燃机学报南昌大学学报.理科版*南京大学学报.自然科学版南京大学学报.数学半年刊*南京航空航天大学学报南京化工大学学报南京理工大学学报.自然科学版*南京林业大学学报*南京农业大学学报南京气象学院学报精细石油化工*军事医学科学院院刊*菌物系统*矿床地质*矿物学报*矿物岩石矿物岩石地球化学通报矿冶工程*昆虫分类学报昆虫天敌*昆虫学报*昆虫知识林产化学与工业*林业科学*林业科学研究临床耳鼻咽喉科杂志*临床放射学杂志*临床检验杂志临床麻醉学杂志临床皮肤科杂志*临床心血管病杂志临床与实验病理学杂志流体机械*流体力学实验与测量*免疫学杂志*模糊系统与数学*模式识别与人工智能*膜科学与技术*摩擦学学报南京医科大学学报南京中医药大学学报.自然科学版*南开大学学报.自然科学版*南开管理评论*泥沙研究宁夏大学学报.自然科学版*农村生态环境农药学学报*农业工程学报*农业环境科学学报*农业机械学报*农业经济问题*农业生物技术学报*农业系统科学与综合研究南京师范大学学报.自然科学版P*PedosphereQ*气候与环境研究*气象气象科学*气象学报汽车工程前寒武纪研究进展R*Rare Metals*燃料化学学报燃烧科学与技术*热带海洋学报*热带气象学报*热带亚热带植物学报*热带作物学报热加工工艺S*Semicond Photonics Technol*色谱*山地学报*山东大学学报.理学版山东工业大学学报*山东农业大学学报.自然科学版山东医科大学学报山西大学学报.自然科学版山西农业科学*陕西师范大学学报.自然科学版*陕西天文台台刊上海大学学报.自然科学版*上海第二医科大学学报*上海环境科学*上海交通大学学报*上海免疫学杂志上海农业学报上海水产大学学报*上海天文台年刊*上海医学上海医学检验杂志深圳大学学报.理工版神经解剖学杂志*沈阳农业大学学报沈阳药科大学学报*肾脏病与透析肾移植杂志*生理科学进展*生理学报农业现代化研究*Plasma Sci Technol*强激光与粒子束*青岛海洋大学学报.自然科学版*清华大学学报.自然科学版情报科学*情报学报热科学与技术热能动力工程*人工晶体学报*人类工效学*人类学学报日用化学工业*软件学报*石油学报石油学报.石油加工*石油与天然气地质石油与天然气化工石油钻采工艺*实验力学*实验生物学报实用放射学杂志实用妇产科杂志实用口腔医学杂志实用肿瘤杂志食品工业科技*食品科学食品与发酵工业世界地质世界科技研究与发展世界林业研究*兽类学报数据采集与处理*数理统计与管理*数量经济技术经济研究*数学季刊*数学进展*数学年刊.A*数学物理学报*数学学报数学研究*数学研究与评论*生命的化学生命科学生命科学研究生态科学*生态学报*生态学杂志*生物多样性*生物工程学报*生物化学与生物物理进展*生物化学与生物物理学报生物技术通报*生物数学学报*生物物理学报*生物医学工程学杂志*生殖与避孕*声学学报*石油大学学报.自然科学版*石油地球物理勘探*石油化工石油化工高等学校学报*石油勘探与开发*石油实验地质石油物探T*Trans Nonferrous Met Soc 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自然语言处理NaturalLanguageProcessing(NLP)精选版演示课件.ppt
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国际中文教师证书 外语要求-概述说明以及解释
国际中文教师证书外语要求-概述说明以及解释1.引言1.1 概述国际中文教师证书是一项具有重要意义的资格认证，旨在培养优秀的中文教师和促进全球中文教育的发展。
随着全球对中文学习需求的增加，国际中文教师证书的重要性日益凸显。
然而，要成为一名合格的中文教师，不仅需要具备了解中文语言文化的专业知识，还需要具备扎实的外语能力。
外语要求作为国际中文教师证书的一项必备条件，对于提升教师的教学能力和全球交流能力至关重要。
本文将深入探讨国际中文教师证书的外语要求，分析其必要性，探讨目前存在的挑战和问题，并提出建议和展望。
通过对外语要求的研究，希望可以为完善国际中文教师证书体系，提高中文教学水平和促进国际中文教育做出贡献。
1.2文章结构1.2 文章结构：本文将分为两个主要部分，分别是国际中文教师证书的重要性和外语要求的必要性。
在第二部分中，将分析目前在国际中文教师证书中关于外语要求的情况，探讨外语能力对于中文教师的重要性。
同时，文章还将讨论目前存在的挑战和问题，并提出建议和展望。
通过对这些内容的深入探讨，我们将更好地理解国际中文教师证书和外语要求的关系，为提高中文教学水平和国际传播中文文化做出贡献。
1.3 目的本文的主要目的是探讨国际中文教师证书中外语要求的重要性和必要性。
通过分析现有情况，我们将探讨外语要求对于提升中文教师教学质量、促进国际汉语教学交流以及适应全球化教育趋势的重要性。
同时，我们将讨论当前存在的挑战和问题，并提出建议和展望，以便在未来完善国际中文教师证书的外语要求，推动中文教育的发展和提升。
2.正文2.1 国际中文教师证书的重要性国际中文教师证书是一种专门针对中文教师的职业资格认证，具有广泛的国际认可度和权威性。
获得国际中文教师证书的教师不仅具备了丰富的中文教学经验和知识，还对教学方法、教学理论、课程设计等方面有着深入的了解和应用能力。
首先，国际中文教师证书的重要性体现在其对教师的专业素养和职业发展的促进作用。
USC CS 课程表
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Computer Science · USC Schedule of Classes
12/23/14, 10:43
CSCI 101L: Fundamentals of Computer Programming (3.0 units) CSCI 103L: Introduction to Programming (3.0 units) CSCI 104L: Data Structures and Object Oriented Design (4.0 units) CSCI 109: Introduction to Computing (3.0 units) CSCI 110: Introduction to Digital Logic (3.0 units) CSCI 170: Discrete Methods in Computer Science (4.0 units) CSCI 201: Principles of Software Development (4.0 units) CSCI 270: Introduction to Algorithms and Theory of Computing (4.0 units) CSCI 280: Video Game Production (4.0 units) CSCI 281: Pipelines for Games and Interactives (3.0 units) CSCI 351: Programming and Multimedia on the World Wide Web (3.0 units) CSCI 352L: Computer Organization and Architecture (3.0 units) CSCI 357: Basic Organization of Computer Systems (3.0 units) CSCI 377: Introduction to Software Engineering (3.0 units)
simplification 自然语言处理
【标题】：深入理解自然语言处理中的简化技术【引言】在当今信息爆炸的时代，我们无时不刻地面对着大量的文字信息。
然而，如何高效地处理和理解这些信息成了一个亟待解决的问题。
在这样的背景下，自然语言处理（Natural Language Processing，NLP）技术应运而生，它为我们提供了一种更加便捷和智能的处理文字信息的方式。
而其中的一个重要技术就是简化技术，它能帮助我们更好地理解和应用文字信息。
在本文中，我们将深入探讨自然语言处理中的简化技术，探讨其核心原理、应用场景和未来发展趋势。
【探讨主题】1. 简化技术在自然语言处理中的重要性2. 简化技术的核心原理及应用3. 简化技术在不同领域的应用案例4. 未来简化技术的发展趋势和展望【简化技术在自然语言处理中的重要性】在NLP领域，简化技术起到了至关重要的作用。
在面对海量的文字信息时，简化技术能够帮助我们更快速地理解和应用这些信息。
对于语言能力不强或专业领域外的人士来说，简化技术能够让他们更容易地理解和应用专业知识。
【简化技术的核心原理及应用】简化技术的核心原理主要包括语句简化、词语简化和句子结构简化等方面。
语句简化主要通过简化复杂句子、去除冗余信息来实现；词语简化则是通过替换或删除部分词语来提高语言可懂度；句子结构简化则是通过调整句子结构来增强语句逻辑性。
【简化技术在不同领域的应用案例】在新闻报道、教育教学、法律文件等领域，简化技术都有着广泛的应用。
比如在新闻报道中，简化技术能够帮助读者更轻松地理解复杂的新闻事件，提高阅读体验。
在教育教学中，简化技术可以帮助学生更好地理解教科书上的知识，提高学习效率。
【未来简化技术的发展趋势和展望】随着人工智能技术的不断发展，简化技术也将迎来更大的发展空间。
未来，我们预计简化技术将在智能客服、智能写作等领域发挥更大的作用，为人们的生活和工作带来更多便利。
【个人观点】对于我个人来说，简化技术在NLP领域的发展带来了很多便利。
第二届CCF自然语言处理与中文计算会议（NLPAMPCC2013）TUTORIALS及...
中国计算机学会《学科前沿讲习班》第四十六期面向大数据的自然语言处理与机器学习2013年11月15日-17日重庆简介自然语言处理(Natural Language Processing, NLP)与机器学习(Machine Learning，ML)一直是计算机科学，尤其是人工智能研究方向的两个核心问题。
近几年来，互联网、社交媒体与移动平台的迅猛发展为传统自然语言处理与机器学习技术带来前所未有的挑战和机遇，使其成为学术界和工业界的研究热点。
一方面，传统的自然语言处理与机器学习在基础研究方面取得了长足进展，为工业应用提供了有效的技术支持；另一方面，不断涌现的真实问题和大规模数据又呼唤更加有效的自然语言处理与机器学习技术。
本期CCF学科前沿讲习班《面向大数据的自然语言处理与机器学习》将邀请学术界和工业界的著名专家、学者对自然语言处理基础理论、方法和应用，面向自然语言的机器学习技术，以及当前面向大数据时代的热点问题进行深入浅出的讲解。
目的是为青年学者和学生提供一个三天的学习、交流机会，快速了解本领域的基本概念、研究内容、方法和发展趋势。
本讲习班同时作为第二届CCF自然语言处理与中文计算会议（NLP&CC 2013）的Tutorials，将围绕大会主题——“数据智能、知识智能与社会智能”，重点讲解自然语言处理基础、社会媒体和语言计算、ＮＬＰ工业应用和典型案例、面向ＮＬＰ的机器学习、Deep Learning以及深层神经网络计算等内容。
学术主任张民，苏州大学教授李沐，微软亚洲研究院研究员协办单位重庆大学苏州大学微软亚洲研究院日程安排================2013年11月15日：自然语言处理和机器翻译8:30-9:00 开班仪式、合影第一讲自然语言处理：基础技术与互联网创新万小军北京大学副教授第一课 09:00-10:20 自然语言处理基础第二课 10:40-11:40 语义计算和篇章分析第三课 11:40-12:00 互动问答第二讲大数据时代的机器翻译刘洋清华大学副教授第一课 13:30-15:30 机器翻译概况、基于词和短语的方法第二课 16:00-17:30 基于句法的方法和未来发展趋势第三课 17:30-17:50 互动问答==============2013年11月16日：社会计算与自然语言处理应用第三讲社会网络计算及社会影响力分析唐杰清华大学副教授第一课 09:00-10:20 社会网络计算基础第二课 10:40-11:40 社会网络计算之社会影响力分析第三课 11:40-12:00 互动问答第四讲自然语言处理工业应用/开发实践第一课 13:20-15:20 大数据时代的智能问答和搜索张阔搜狗研究员第二课 15:40-16:40 大数据时代的搜索广告查询分析胡云华阿里研究员第三课 16:40-17:40 大数据下的广告排序技术及实践蒋龙阿里研究员第四课 17:40-18:00 互动问答==============2013年11月17日：机器学习第五讲 Statistical Machine Learning for NLP （统计机器学习与自然语言处理）朱小槿 University of Wisconsin-Madison 副教授第一课 09:00-10:00 Basics of Statistical Learning （统计机器学习基础）第二课 10:20-11:10 Graphical Models （图模型）第三课 11:10-12:00 Bayesian Non-Parametric Models （贝叶斯非参数方法）第四课 12:00-12:20 互动问答第六讲 Deep Learning - What, Why, and How 俞栋 MSR 研究员第一课 13:40-14:40 Deep Learning: Premise, Philosophy, and ItsRelation to Other Techniques 第二课 14:40-15:40 Basic Deep Learning Models第三课 16:00-17:15 Deep Neural Network and Its Applicationin Speech Recognition 第四课 17:15-17:35 互动问答17:35-18:00 结业式讲者介绍========================万小军北京大学副教授报告题目：自然语言处理：基础技术与互联网创新摘要：随着互联网上文本数据的爆炸性增长，如何对这些数据进行智能分析、语义挖掘与深度利用是学术界和工业界所共同面临的重大挑战。
基于国际汉语教材语料库的中华文化项目表开发
基于国际汉语教材语料库的中华文化项目表开发一、概述中华文化源远流长，博大精深，涵盖了语言、文学、历史、哲学、艺术等多个领域。
在国际汉语教育中，有效地传播和教授中华文化，不仅有助于提升学习者的汉语水平，更能增进他们对中国的理解和认同。
目前市场上的国际汉语教材在中华文化内容的呈现上，往往存在内容零散、缺乏系统性、与语言教学脱节等问题。
开发一套基于国际汉语教材语料库的中华文化项目表，对于优化国际汉语教材中的文化内容，提高教学效果，具有重要意义。
本文旨在探讨基于国际汉语教材语料库的中华文化项目表的开发过程及应用价值。
我们将分析现有国际汉语教材中文化内容的现状及其存在的问题，明确开发中华文化项目表的必要性和紧迫性。
我们将介绍语料库的构建过程，包括语料来源的选择、语料处理的方法以及语料库的结构设计等。
在此基础上，我们将详细阐述中华文化项目表的开发过程，包括项目分类、内容筛选、呈现方式等方面的内容。
我们将通过实际教学案例，展示中华文化项目表在国际汉语教学中的应用效果，并探讨其对于提升学习者汉语水平和文化认知的积极作用。
1. 国际汉语教育的重要性在全球化日益加剧的今天，国际汉语教育的重要性愈发凸显。
汉语作为中华文化的载体，不仅是沟通中国与世界的重要桥梁，更是传播中华文明、促进文化多样性的关键途径。
通过汉语教育，我们可以向世界展示中华文化的独特魅力，增进不同文化间的理解与交流。
国际汉语教育有助于提升国家软实力。
语言是文化的载体，汉语教育在推广中华文化、增强国家文化影响力方面发挥着重要作用。
随着中国的崛起和国际地位的提升，越来越多的外国人开始关注并学习汉语，希望通过了解汉语来深入了解中国。
加强国际汉语教育，对于提升国家文化软实力、树立良好国际形象具有重要意义。
国际汉语教育有助于促进国际交流与合作。
在全球化背景下，国际间的交流与合作日益频繁。
掌握汉语不仅有助于个人在国际舞台上更好地展示自己的才能和优势，更有利于国家间开展深入的合作与交流。
基于国际汉语教材语料库的中华文化项目表开发
基于国际汉语教材语料库的中华文化项目表开发一、本文概述随着全球汉语热度的不断升温，国际汉语教学逐渐成为语言教育领域的重要分支。
在这一过程中，汉语教材作为传播中华文化、培养学习者语言技能的重要载体，其质量和内容的丰富性显得尤为重要。
本文旨在探讨基于国际汉语教材语料库的中华文化项目表的开发，以期通过系统整理和分析教材语料库，挖掘和提炼中华文化的核心元素，为国际汉语教学提供更加精准、丰富的教学资源。
本文首先将对国际汉语教材语料库的建设现状进行概述，分析语料库在汉语教学和中华文化传播方面的重要作用。
随后，将详细介绍中华文化项目表的开发流程，包括项目表的构建原则、筛选标准、分类方法以及项目表的呈现形式等。
在此基础上，文章将深入探讨如何有效利用国际汉语教材语料库，提取与中华文化相关的关键词汇、短语和表达方式，进而构建具有针对性的中华文化项目表。
通过本文的研究，期望能够为国际汉语教材的编写者、汉语教师以及学习者提供更加系统、全面的中华文化教学资源，推动国际汉语教学的深入发展，进一步增进世界各国人民对中华文化的了解和认同。
二、国际汉语教材语料库概述国际汉语教材语料库是一个集成了大量汉语教材资源的综合性数据库，旨在为汉语教学与研究提供全面、系统的语料支持。
该语料库涵盖了自上世纪至今的各类汉语教材，包括教材文本、练习、注释等多元化内容。
其特色在于其庞大的规模、丰富的资源和精确的标注，为研究者提供了便捷的数据查询和分析工具。
国际汉语教材语料库的建立，不仅有助于汉语学习者更好地理解和学习汉语，也为汉语教师提供了丰富的教学资源和参考。
通过对语料库中的教材进行分析，可以深入了解汉语教材的发展历程、教学内容的变化以及教学方法的改进。
该语料库还为跨文化交流研究提供了宝贵的资料，有助于推动中华文化的国际传播。
在国际汉语教学领域，国际汉语教材语料库的应用前景广阔。
随着全球汉语学习热潮的持续升温，该语料库将为全球范围内的汉语学习者、教师和研究者提供更加便捷、高效的服务。
计算语言学及其近义术语详解
一、计算语言学的起源及其发展从世界上第一台电子计算机诞生至今，计算机的功能已经远远超出了最初的数值计算范围，进入到了更广泛的非数值领域，例如语言处理领域。
而在计算机出现之前，对语言的研究大都是由语言学家来完成的。
利用计算机这一现代计算工具来研究语言，仿佛给计算机赋予了更多的智能化色彩，而“计算语言学”(Computational Linguis－tics,CL)这一语言学和计算机科学的交叉学科此时则应运而生。
当然，在计算语言学的研究过程中，还涉及到数学、认知科学、逻辑学、心理学等许多其他学科。
实际上，“计算语言学”这一术语是伴随着“机器翻译”这一应用而出现的。
传说中，上帝为阻止人类建造通天塔的壮举，故意让不同种族的人讲不同的语言，使人类不能自由交流，无法齐心协力。
为了跨越语言的障碍，远在古希腊时代，就有人提出要用机器来代替人进行不同语言之间的翻译。
1933年，前苏联发明家特罗扬斯基设计了一种用于翻译的机器，但是并没有成功。
事实上，真正的机器翻译研究是在计算机发明之后开始的，1954年，美国Georgetown大学与IBM公司合作开发了世界上第一个机器翻译的原型系统，当时的目的主要是将其用于美俄之间军事情报的翻译工作，该系统首次通过机器将俄语翻译为英文并取得了初步的成功。
这项工作使学者们备受鼓舞，也吸引了政府大量资金的注入，计算语言学的研究也开始了其萌芽时期。
初期的机器翻译系统大都是以词典驱动，直接采用词对词的模式匹配的翻译方式，由于不同的语言之间词法、句法都存在很大差异，显然，这样的翻译结果不会令人满意。
1966年，ALPAC报告中指出，机器翻译的研究在当时的条件下并不具备很好的前景，不宜给予大力支持。
另外，后来有学者认为，虽然“计算语言学”一词之前早已出现，但作为术语第一次正式提出“计算语言学”及其近义术语详解*◇邵艳秋（北京大学）摘要：本文介绍了计算语言学的起源及其发展历史，对该领域的一些相近术语概念及其各概念之间的关系进行了详细的解释，包括计算语言学、自然语言处理、自然语言理解、人类语言技术、语言信息处理、中文信息处理等等。
自然语言处理
自然语言处理自然语言处理（Natural Language Processing，NLP）是一门涉及人类与计算机之间有效交互的技术。
它涉及如何使机器能够理解、处理和生成自然语言的能力。
随着人工智能技术的快速发展，自然语言处理在各个领域都得到了广泛的应用，并取得了重要的进展。
一、自然语言处理的定义和意义自然语言处理是一门交叉学科，结合了计算机科学、人工智能以及语言学等领域的知识。
它的目标是使计算机能够理解和处理人类语言，并能够与人类进行自然、流畅的对话。
通过自然语言处理技术，我们可以让计算机阅读和理解文本、识别和生成语音、进行机器翻译、完成信息检索等任务。
自然语言处理的意义在于解决人机交互中的语言障碍问题。
人类的语言是复杂而多变的，对于计算机而言，理解和处理自然语言是一项艰巨的任务。
然而，如果我们能够使计算机具备自然语言处理的能力，就能够极大地提高人机交互的效率和便利性，推动人工智能技术的发展。
二、自然语言处理的关键技术1. 语言理解：语言理解是自然语言处理的核心任务之一。
它涉及到词法分析、句法分析、语义分析等技术，旨在使计算机能够理解人类的语言。
通过语言理解技术，计算机可以分析句子的结构和意义，提取出其中的信息。
2. 机器翻译：机器翻译是自然语言处理的重要应用之一。
它涉及将一种语言的文本自动翻译成另一种语言的文本。
机器翻译技术可以极大地降低翻译的时间和成本，并在跨语言交流和文化交流中发挥着重要的作用。
3. 信息检索：信息检索是指根据用户的需求从大量的文档或数据库中检索出相关的信息。
自然语言处理技术可以应用于信息检索中，使得计算机能够根据用户的自然语言查询，准确地检索出相关的文本信息。
4. 语音识别和语音合成：语音识别是指将人类语音转换为文本的技术，而语音合成则是将文本转换为人类可听的语音。
自然语言处理技术可以应用于语音识别和语音合成中，使得计算机能够处理和生成自然、流畅的语音。
三、自然语言处理的应用领域自然语言处理技术在各个领域都有广泛的应用。
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2003
Egyptair Has Tomorrow to Resume Its Flights to Libya Cairo 4-6 (AFP) - Said an official at the Egyptian Aviation Company today that the company egyptair may resume as of tomorrow, Wednesday its flights to Libya after the International Security Council resolution to the suspension of the embargo imposed on Libya.
6/8/2011
farok crrrok hihok yorok clok kantok ok-yurp
7a. lalok farok ororok lalok sprok izok enemok . 7b. wat jjat bichat wat dat vat eneat . 8a. lalok brok anok plok nok . 8b. iat lat pippat rrat nnat . 9a. wiwok nok izok kantok ok-yurp . 9b. totat nnat quat oloat at-yurp . 10a. lalok mok nok yorok ghirok clok . 10b. wat nnat gat mat bat hilat . 11a. lalok nok crrrok hihok yorok zanzanok . 11b. wat nnat arrat mat zanzanat . 12a. lalok rarok nok izok hihok mok . 12b. wat nnat forat arrat vat gat .
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Centauri/Arcturan [Knight, 1997]
Your assignment, translate this to Arcturan:
1a. ok-voon ororok sprok . 1b. at-voon bichat dat . 2a. ok-drubel ok-voon anok plok sprok . 2b. at-drubel at-voon pippat rrat dat . 3a. erok sprok izok hihok ghirok . 3b. totat dat arrat vat hilat . 4a. ok-voon anok drok brok jok . 4b. at-voon krat pippat sat lat . 5a. wiwok farok izok stok . 5b. totat jjat quat cat . 6a. lalok sprok izok jok stok . 6b. wat dat krat quat cat .
5
Commercial Applications
news broadcast foreign language speech recognition
English translation searchable archive
6 6/8/2011
Commercial Applications
QuickTime?and a TIFF (LZW) decompressor are needed to see this picture.
CSCI 5832 Natural Language Processing
Jim Martin Lecture 23
6/8/2011
1
Change in plans
• Going straight to Chapter 25 (translation) • I’ll come back to 23 (q/a, summarization)
13
Centauri/Arcturan [Knight, 1997]
Your assignment, translate this to Arcturan:
1a. ok-voon ororok sprok . 1b. at-voon bichat dat . 2a. ok-drubel ok-voon anok plok sprok . 2b. at-drubel at-voon pippat rrat dat . 3a. erok sprok izok hihok ghirok . 3b. totat dat arrat vat hilat . 4a. ok-voon anok drok brok jok . 4b. at-voon krat pippat sat lat . 5a. wiwok farok izok stok . 5b. totat jjat quat cat . 6a. lalok sprok izok jok stok . 6b. wat dat krat quat cat .
2 6/8/2011
Machine Translation
Slides mostly stolen from Kevin Knight (USC/ISI)
6/8/2011
3
Today 4/22
• • • • Machine translation framework State of the art results Evaluation methods Word-based models
7 6/8/2011
Statistical Machine Translation
Hmm, every time he sees “banco”, he either types “bank” or “bench” … but if he sees “banco de…”, he always types “bank”, never “bench”…
11 6/8/2011
Warren Weaver (1947)
When I look at an article in Russian, I say to myself: This is really written in English, but it has been coded in some strange symbols. I will now proceed to decode.
6/8/2011
farok crrrok hihok yorok clok kantok ok-yurp
7a. lalok farok ororok lalok sprok izok enemok . 7b. wat jjat bichat wat dat vat eneat . 8a. lalok brok anok plok nok . 8b. iat lat pippat rrat nnat . 9a. wiwok nok izok kantok ok-yurp . 9b. totat nnat quat oloat at-yurp . 10a. lalok mok nok yorok ghirok clok . 10b. wat nnat gat mat bat hilat . 11a. lalok nok crrrok hihok yorok zanzanok . 11b. wat nnat arrat mat zanzanat . 12a. lalok rarok nok izok hihok mok . 12b. wat nnat forat arrat vat gat .
Man, this is so boring.
Translated documents
8 6/8/2011
Things are Consistently Improving
Annual evaluation of Arabic-to-English MT systems
Translation quality
14
Centauri/Arcturan [Knight, 1997]
Your assignment, translate this to Arcturan:
1a. ok-voon ororok sprok . 1b. at-voon bichat dat . 2a. ok-drubel ok-voon anok plok sprok . 2b. at-drubel at-voon pippat rrat dat . 3a. erok sprok izok hihok ghirok . 3b. totat dat arrat vat hilat . 4a. ok-voon anok drok brok jok . 4b. at-voon krat pippat sat lat . 5a. wiwok farok izok stok . 5b. totat jjat quat cat . 6a. lalok sprok izok jok stok . 6b. wat dat krat quat cat .
4 6/8/2011
Progress
2002
insistent Wednesday may recurred her trips to Libya tomorrow for flying Cairo 6-4 ( AFP ) - An official announced today in the Egyptian lines company for flying Tuesday is a company "insistent for flying" may resumed a consideration of a day Wednesday tomorrow her trips to Libya of Security Council decision trace international the imposed ban comment.