P-1.1.9.ppt
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This document discusses the concepts of artificial intelligence and machine learning. It provides an overview of the course outcomes, objectives, and applications of natural language processing (NLP). The five phases of NLP are also outlined, including lexical analysis, syntactic analysis, semantic analysis, discourse integration, and pragmatic analysis. Several example applications of NLP are discussed such as question answering, text classification, language generation, and language interaction. References including textbooks, reference materials, web links, and video links are also provided.
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INTRODUCTION TO Natural language processing
Natural language processing (NLP) is a machine learning technology that gives computers the ability to
interpret, manipulate, and comprehend human language.
•Ex: Amazon’s Alexa and Apple’s Siri utilize NLP to listen to user queries and find answers
• We have large volumes of voice and text data from various communication channels like emails, text
messages, social media newsfeeds, video, audio, and more.
• They use NLP software to automatically process this data, analyze the intent or sentiment in the
message, and respond in real time to human communication
• When text mining and machine learning are combined, automated text analysis becomes possible
PREPROCESSING STEPS IN NLP
• Data preprocessing involves preparing and cleaning text data so that machines can analyze it. This
can be done in following:
• Tokenization. It substitutes sensitive information with nonsensitive information, or a token.
Tokenization is often used in payment transactions to protect credit card data.
• Stop word removal. Common words are removed from the text, so unique words that offer the most
information about the text remain.
• Lemmatization and stemming. Lemmatization groups together different inflected versions of the
same word. For example, the word "walking" would be reduced to its root form, or stem, "walk" to
process.
• Part-of-speech tagging. Words are tagged based on which part of speech they correspond to -- such
as nouns, verbs or adjectives
A Review Of Text Mining Techniques And Applications
This document provides a review of various text mining techniques and applications. It discusses techniques used for text classification and summarization, including Naive Bayes classification, backpropagation neural networks, keyword matching, and information extraction. It also covers applications of text mining in areas like sentiment analysis of social media posts and hotel reviews. Finally, it discusses the need for organizational text mining to extract useful information and insights from large amounts of unstructured text data.
Computer Educational Theories Technology .pptx
Theory of computation, complexity theory, programming language theory, and database theory are branches of computer science that study foundational aspects of computation and data. Theory of computation classifies problems and analyzes their difficulty and solvability. Complexity theory relates problems into complexity classes based on their computational resources. Programming language theory analyzes the design, implementation, and properties of programming languages. Database theory provides principles for querying, designing, and implementing databases and ensuring their consistency. These theoretical fields provide frameworks for understanding computation and managing data and problems across disciplines.
A template based algorithm for automatic summarization and dialogue managemen...
Abstract This paper describes an automated approach for extracting significant and useful events from unstructured text. The goal of research is to come out with a methodology which helps in extracting important events such as dates, places, and subjects of interest. It would be also convenient if the methodology helps in presenting the users with a shorter version of the text which contain all non-trivial information. We also discuss implementation of algorithms which exactly does this task, developed by us. Key Words: Cosine Similarity, Information, Natural Language, Summarization, Text Mining
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This document discusses natural language processing (NLP) and its role in augmentative and alternative communication. It begins with introducing NLP as a field of artificial intelligence that aims to allow computers to understand and process human language. It then describes augmentative and alternative communication as methods of non-verbal communication that are useful for those with speech impairments. The document goes on to discuss the different levels of NLP processing from phonology to pragmatics. It also outlines common approaches to NLP, including symbolic and statistical methods. The role of NLP is seen as enabling computerized communication to support augmentative communication.
An In-Depth Exploration of Natural Language Processing: Evolution, Applicatio...
Natural language processing (NLP) has
recently garnered significant interest for the
computational representation and analysis of human
language. Its applications span multiple domains such
as machine translation, email spam detection,
information extraction, summarization, healthcare,
and question answering. This paper first delineates
four phases by examining various levels of NLP and
components of Natural Language Generation,
followed by a review of the history and progression of
NLP. Subsequently, we delve into the current state of
the art by presenting diverse NLP applications,
contemporary trends, and challenges. Finally, we
discuss some available datasets, models, and
evaluation metrics in NLP.
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INTRODUCTION TO Natural language processing
Natural language processing (NLP) is a machine learning technology that gives computers the ability to
interpret, manipulate, and comprehend human language.
•Ex: Amazon’s Alexa and Apple’s Siri utilize NLP to listen to user queries and find answers
• We have large volumes of voice and text data from various communication channels like emails, text
messages, social media newsfeeds, video, audio, and more.
• They use NLP software to automatically process this data, analyze the intent or sentiment in the
message, and respond in real time to human communication
• When text mining and machine learning are combined, automated text analysis becomes possible
PREPROCESSING STEPS IN NLP
• Data preprocessing involves preparing and cleaning text data so that machines can analyze it. This
can be done in following:
• Tokenization. It substitutes sensitive information with nonsensitive information, or a token.
Tokenization is often used in payment transactions to protect credit card data.
• Stop word removal. Common words are removed from the text, so unique words that offer the most
information about the text remain.
• Lemmatization and stemming. Lemmatization groups together different inflected versions of the
same word. For example, the word "walking" would be reduced to its root form, or stem, "walk" to
process.
• Part-of-speech tagging. Words are tagged based on which part of speech they correspond to -- such
as nouns, verbs or adjectives
A Review Of Text Mining Techniques And Applications
This document provides a review of various text mining techniques and applications. It discusses techniques used for text classification and summarization, including Naive Bayes classification, backpropagation neural networks, keyword matching, and information extraction. It also covers applications of text mining in areas like sentiment analysis of social media posts and hotel reviews. Finally, it discusses the need for organizational text mining to extract useful information and insights from large amounts of unstructured text data.
Computer Educational Theories Technology .pptx
Theory of computation, complexity theory, programming language theory, and database theory are branches of computer science that study foundational aspects of computation and data. Theory of computation classifies problems and analyzes their difficulty and solvability. Complexity theory relates problems into complexity classes based on their computational resources. Programming language theory analyzes the design, implementation, and properties of programming languages. Database theory provides principles for querying, designing, and implementing databases and ensuring their consistency. These theoretical fields provide frameworks for understanding computation and managing data and problems across disciplines.
A template based algorithm for automatic summarization and dialogue managemen...
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This document discusses natural language processing (NLP) and its role in augmentative and alternative communication. It begins with introducing NLP as a field of artificial intelligence that aims to allow computers to understand and process human language. It then describes augmentative and alternative communication as methods of non-verbal communication that are useful for those with speech impairments. The document goes on to discuss the different levels of NLP processing from phonology to pragmatics. It also outlines common approaches to NLP, including symbolic and statistical methods. The role of NLP is seen as enabling computerized communication to support augmentative communication.
An In-Depth Exploration of Natural Language Processing: Evolution, Applicatio...
Natural language processing (NLP) has
recently garnered significant interest for the
computational representation and analysis of human
language. Its applications span multiple domains such
as machine translation, email spam detection,
information extraction, summarization, healthcare,
and question answering. This paper first delineates
four phases by examining various levels of NLP and
components of Natural Language Generation,
followed by a review of the history and progression of
NLP. Subsequently, we delve into the current state of
the art by presenting diverse NLP applications,
contemporary trends, and challenges. Finally, we
discuss some available datasets, models, and
evaluation metrics in NLP.
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The document discusses natural language processing (NLP), which uses technology to help computers understand human language through tasks like audio to text conversion, text processing, and responding to humans in their own language. It describes the key components of NLP as natural language understanding to analyze language and natural language generation to convert data into language. The document also outlines how to build an NLP pipeline with steps like sentence segmentation, tokenization, stemming, and named entity recognition.
NLP Bootcamp 2018 : Representation Learning of text for NLP
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Natural Language Processing (NLP) is a field of artificial intelligence that allows computers to understand, process, and derive meaning from human language. NLP incorporates machine learning, statistics, and computational linguistics to analyze large amounts of natural language data and emulate human language understanding. Key applications of NLP include machine translation, conversational agents, information extraction, and natural language generation. While NLP has advanced capabilities, fully simulating human language comprehension remains a challenge for artificial intelligence.
Natural Language Processing for development
Natural Language Processing (NLP) is a field of artificial intelligence that allows computers to understand, process, and derive meaning from human language. NLP incorporates machine learning, statistics, and computational linguistics to analyze large amounts of natural language data and emulate human language understanding. Key applications of NLP include machine translation, conversational agents, information extraction, and natural language generation. While NLP has advanced capabilities, fully simulating human language comprehension remains a challenge for artificial intelligence.
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The document provides information about natural language processing (NLP) including:
1. NLP stands for natural language processing and involves using machines to understand, analyze, and interpret human language.
2. The history of NLP began in the 1940s and modern NLP consists of applications like speech recognition and machine translation.
3. The two main components of NLP are natural language understanding, which helps machines understand language, and natural language generation, which converts computer data into natural language.
Natural Language Processing
Natural language processing (NLP) is a way for computers to analyze, understand, and derive meaning from human language. NLP utilizes machine learning to automatically learn rules by analyzing large datasets rather than requiring hand-coding of rules. Common NLP tasks include summarization, translation, named entity recognition, sentiment analysis, and speech recognition. NLP works by applying algorithms to identify and extract natural language rules to convert unstructured language into a form computers can understand. Main techniques used in NLP are syntactic analysis to assess language alignment with grammar rules and semantic analysis to understand meaning and interpretation of words.
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Natural Language Processing (NLP) techniques are one of the most used techniques in the field of computer applications. It has become one of the vast and advanced techniques. Language is the means of communication or interaction among humans and in present scenario when everything is dependent on machine or everything is computerized, communication between computer and human has become a necessity. To fulfill this necessity NLP has been emerged as the means of interaction which narrows the gap between machines (computers) and humans. It was evolved from the study of linguistics which was passed through the Turing test to check the similarity between data but it was limited to small set of data. Later on various algorithms were developed along with the concept of AI (Artificial Intelligence) for the successful execution of NLP. In this paper, the main emphasis is on the different techniques of NLP which have been developed till now, their applications and the comparison of all those techniques on different parameters.
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From many years we have been using Chomsky‟s generative system of grammars, particularly context-free grammars (CFGs) and regular expressions (REs), to express the syntax of programming languages and protocols. Syntactic parsing mainly works with syntactic structure of a sentence. The 'syntax' refers to the grammatical and syntactical arrangement of words in a sentence and their relationship with other words. The main focus of syntactic analysis is important to find syntactic structure of a sentence which usually is represented as a tree structure. To identify the syntactic structure is useful in determining the meaning of a sentence Natural language processing processes the data through lexical analysis, Syntax analysis, Semantic analysis, and Discourse processing, Pragmatic analysis. This paper gives various parsing methods. The algorithm in this paper splits the English sentences into parts using POS (Parts Of Speech) tagger, It identifies the type of sentence (Simple, Complex, Interrogate, Facts, active, passive etc.) and then parses these sentences using grammar rules of Natural language. As natural language processing becomes an increasingly relevant, there is a need for tree banks catered to the specific needs of more individualized systems. Here, we present the open source technique to check and correct the grammar. The methodology will give appropriate grammatical suggestions.
Error Analysis of Rule-based Machine Translation Outputs
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Natural Language Processing (NLP) is a subfield of artificial intelligence (AI) and computational linguistics that focuses on enabling computers to understand and interact with human language. It combines techniques from computer science, linguistics, and statistics to bridge the gap between human language and machine understanding. NLP has gained significant attention in recent years due to advancements in AI and the increasing need for machines to process and interpret vast amounts of textual data.
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一比一原版(uoft毕业证书)加拿大多伦多大学毕业证如何办理
原版一模一样【微信：741003700 】【(uoft毕业证书)加拿大多伦多大学毕业证成绩单】【微信：741003700 】学位证，留信认证（真实可查，永久存档）原件一模一样纸张工艺/offer、雅思、外壳等材料/诚信可靠,可直接看成品样本，帮您解决无法毕业带来的各种难题！外壳，原版制作，诚信可靠，可直接看成品样本。行业标杆！精益求精，诚心合作，真诚制作！多年品质 ,按需精细制作，24小时接单,全套进口原装设备。十五年致力于帮助留学生解决难题，包您满意。
本公司拥有海外各大学样板无数，能完美还原。
1:1完美还原海外各大学毕业材料上的工艺：水印，阴影底纹，钢印LOGO烫金烫银，LOGO烫金烫银复合重叠。文字图案浮雕、激光镭射、紫外荧光、温感、复印防伪等防伪工艺。材料咨询办理、认证咨询办理请加学历顾问Q/微741003700
【主营项目】
一.毕业证【q微741003700】成绩单、使馆认证、教育部认证、雅思托福成绩单、学生卡等！
二.真实使馆公证(即留学回国人员证明,不成功不收费)
三.真实教育部学历学位认证（教育部存档！教育部留服网站永久可查）
四.办理各国各大学文凭(一对一专业服务,可全程监控跟踪进度)
如果您处于以下几种情况：
◇在校期间，因各种原因未能顺利毕业……拿不到官方毕业证【q/微741003700】
◇面对父母的压力，希望尽快拿到；
◇不清楚认证流程以及材料该如何准备；
◇回国时间很长，忘记办理；
◇回国马上就要找工作，办给用人单位看；
◇企事业单位必须要求办理的
◇需要报考公务员、购买免税车、落转户口
◇申请留学生创业基金
留信网认证的作用:
1:该专业认证可证明留学生真实身份
2:同时对留学生所学专业登记给予评定
3:国家专业人才认证中心颁发入库证书
4:这个认证书并且可以归档倒地方
5:凡事获得留信网入网的信息将会逐步更新到个人身份内，将在公安局网内查询个人身份证信息后，同步读取人才网入库信息
6:个人职称评审加20分
7:个人信誉贷款加10分
8:在国家人才网主办的国家网络招聘大会中纳入资料，供国家高端企业选择人才
办理(uoft毕业证书)加拿大多伦多大学毕业证【微信：741003700 】外观非常简单，由纸质材料制成，上面印有校徽、校名、毕业生姓名、专业等信息。
办理(uoft毕业证书)加拿大多伦多大学毕业证【微信：741003700 】格式相对统一，各专业都有相应的模板。通常包括以下部分：
校徽：象征着学校的荣誉和传承。
校名:学校英文全称
授予学位：本部分将注明获得的具体学位名称。
毕业生姓名：这是最重要的信息之一，标志着该证书是由特定人员获得的。
颁发日期：这是毕业正式生效的时间，也代表着毕业生学业的结束。
其他信息：根据不同的专业和学位，可能会有一些特定的信息或章节。
办理(uoft毕业证书)加拿大多伦多大学毕业证【微信：741003700 】价值很高，需要妥善保管。一般来说，应放置在安全、干燥、防潮的地方，避免长时间暴露在阳光下。如需使用，最好使用复印件而不是原件，以免丢失。
综上所述，办理(uoft毕业证书)加拿大多伦多大学毕业证【微信：741003700 】是证明身份和学历的高价值文件。外观简单庄重，格式统一，包括重要的个人信息和发布日期。对持有人来说，妥善保管是非常重要的。
FUNDAMENTALS OF MECHANICAL ENGINEERING.pdf
EMERSON EDUARDO RODRIGUES
EMERSON EDUARDO RODRIGUES
EMERSON EDUARDO RODRIGUES
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OOPS_Lab_Manual - programs using C++ programming language
This manual contains programs on object oriented programming concepts using C++ language.
Tools & Techniques for Commissioning and Maintaining PV Systems W-Animations ...
Join us for this solutions-based webinar on the tools and techniques for commissioning and maintaining PV Systems. In this session, we'll review the process of building and maintaining a solar array, starting with installation and commissioning, then reviewing operations and maintenance of the system. This course will review insulation resistance testing, I-V curve testing, earth-bond continuity, ground resistance testing, performance tests, visual inspections, ground and arc fault testing procedures, and power quality analysis.
Fluke Solar Application Specialist Will White is presenting on this engaging topic:
Will has worked in the renewable energy industry since 2005, first as an installer for a small east coast solar integrator before adding sales, design, and project management to his skillset. In 2022, Will joined Fluke as a solar application specialist, where he supports their renewable energy testing equipment like IV-curve tracers, electrical meters, and thermal imaging cameras. Experienced in wind power, solar thermal, energy storage, and all scales of PV, Will has primarily focused on residential and small commercial systems. He is passionate about implementing high-quality, code-compliant installation techniques.
Accident detection system project report.pdf
The Rapid growth of technology and infrastructure has made our lives easier. The
advent of technology has also increased the traffic hazards and the road accidents take place
frequently which causes huge loss of life and property because of the poor emergency facilities.
Many lives could have been saved if emergency service could get accident information and
reach in time. Our project will provide an optimum solution to this draw back. A piezo electric
sensor can be used as a crash or rollover detector of the vehicle during and after a crash. With
signals from a piezo electric sensor, a severe accident can be recognized. According to this
project when a vehicle meets with an accident immediately piezo electric sensor will detect the
signal or if a car rolls over. Then with the help of GSM module and GPS module, the location
will be sent to the emergency contact. Then after conforming the location necessary action will
be taken. If the person meets with a small accident or if there is no serious threat to anyone’s
life, then the alert message can be terminated by the driver by a switch provided in order to
avoid wasting the valuable time of the medical rescue team.
Determination of Equivalent Circuit parameters and performance characteristic...
Includes the testing of induction motor to draw the circle diagram of induction motor with step wise procedure and calculation for the same. Also explains the working and application of Induction generator
Blood finder application project report (1).pdf
Blood Finder is an emergency time app where a user can search for the blood banks as
well as the registered blood donors around Mumbai. This application also provide an
opportunity for the user of this application to become a registered donor for this user have
to enroll for the donor request from the application itself. If the admin wish to make user
a registered donor, with some of the formalities with the organization it can be done.
Specialization of this application is that the user will not have to register on sign-in for
searching the blood banks and blood donors it can be just done by installing the
application to the mobile.
The purpose of making this application is to save the user’s time for searching blood of
needed blood group during the time of the emergency.
This is an android application developed in Java and XML with the connectivity of
SQLite database. This application will provide most of basic functionality required for an
emergency time application. All the details of Blood banks and Blood donors are stored
in the database i.e. SQLite.
This application allowed the user to get all the information regarding blood banks and
blood donors such as Name, Number, Address, Blood Group, rather than searching it on
the different websites and wasting the precious time. This application is effective and
user friendly.
Object Oriented Analysis and Design - OOAD
This ppt gives detailed description of Object Oriented Analysis and design.
Digital Twins Computer Networking Paper Presentation.pptx
A Digital Twin in computer networking is a virtual representation of a physical network, used to simulate, analyze, and optimize network performance and reliability. It leverages real-time data to enhance network management, predict issues, and improve decision-making processes.
一比一原版(osu毕业证书)美国俄勒冈州立大学毕业证如何办理
原版一模一样【微信：741003700 】【(osu毕业证书)美国俄勒冈州立大学毕业证成绩单】【微信：741003700 】学位证，留信认证（真实可查，永久存档）原件一模一样纸张工艺/offer、雅思、外壳等材料/诚信可靠,可直接看成品样本，帮您解决无法毕业带来的各种难题！外壳，原版制作，诚信可靠，可直接看成品样本。行业标杆！精益求精，诚心合作，真诚制作！多年品质 ,按需精细制作，24小时接单,全套进口原装设备。十五年致力于帮助留学生解决难题，包您满意。
本公司拥有海外各大学样板无数，能完美还原。
1:1完美还原海外各大学毕业材料上的工艺：水印，阴影底纹，钢印LOGO烫金烫银，LOGO烫金烫银复合重叠。文字图案浮雕、激光镭射、紫外荧光、温感、复印防伪等防伪工艺。材料咨询办理、认证咨询办理请加学历顾问Q/微741003700
【主营项目】
一.毕业证【q微741003700】成绩单、使馆认证、教育部认证、雅思托福成绩单、学生卡等！
二.真实使馆公证(即留学回国人员证明,不成功不收费)
三.真实教育部学历学位认证（教育部存档！教育部留服网站永久可查）
四.办理各国各大学文凭(一对一专业服务,可全程监控跟踪进度)
如果您处于以下几种情况：
◇在校期间，因各种原因未能顺利毕业……拿不到官方毕业证【q/微741003700】
◇面对父母的压力，希望尽快拿到；
◇不清楚认证流程以及材料该如何准备；
◇回国时间很长，忘记办理；
◇回国马上就要找工作，办给用人单位看；
◇企事业单位必须要求办理的
◇需要报考公务员、购买免税车、落转户口
◇申请留学生创业基金
留信网认证的作用:
1:该专业认证可证明留学生真实身份
2:同时对留学生所学专业登记给予评定
3:国家专业人才认证中心颁发入库证书
4:这个认证书并且可以归档倒地方
5:凡事获得留信网入网的信息将会逐步更新到个人身份内，将在公安局网内查询个人身份证信息后，同步读取人才网入库信息
6:个人职称评审加20分
7:个人信誉贷款加10分
8:在国家人才网主办的国家网络招聘大会中纳入资料，供国家高端企业选择人才
办理(osu毕业证书)美国俄勒冈州立大学毕业证【微信：741003700 】外观非常简单，由纸质材料制成，上面印有校徽、校名、毕业生姓名、专业等信息。
办理(osu毕业证书)美国俄勒冈州立大学毕业证【微信：741003700 】格式相对统一，各专业都有相应的模板。通常包括以下部分：
校徽：象征着学校的荣誉和传承。
校名:学校英文全称
授予学位：本部分将注明获得的具体学位名称。
毕业生姓名：这是最重要的信息之一，标志着该证书是由特定人员获得的。
颁发日期：这是毕业正式生效的时间，也代表着毕业生学业的结束。
其他信息：根据不同的专业和学位，可能会有一些特定的信息或章节。
办理(osu毕业证书)美国俄勒冈州立大学毕业证【微信：741003700 】价值很高，需要妥善保管。一般来说，应放置在安全、干燥、防潮的地方，避免长时间暴露在阳光下。如需使用，最好使用复印件而不是原件，以免丢失。
综上所述，办理(osu毕业证书)美国俄勒冈州立大学毕业证【微信：741003700 】是证明身份和学历的高价值文件。外观简单庄重，格式统一，包括重要的个人信息和发布日期。对持有人来说，妥善保管是非常重要的。
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P-1.1.9.ppt
- 1. DISCOVER . LEARN . EMPOWER UNIVERSITY INSTITUTE OF ENGINEERING DEPARTMENT OF COMPUTER SCIENCE AND ENGG. Bachelor of Engineering (Computer Science & Engineering) Artificial Intelligence and Machine Learning(21CSH-316) Prepared by: Sitaram patel(E13285) 1 www.cuchd.in Computer Science and Engineering Department
- 2. Course Outcomes 2 CO1 -Understand the fundamental concepts and techniques of artificial intelligence and machine learning.
- 3. Course Objectives 3 To understand the history and development of Machine Learning. To provide a comprehensive foundation to Machine Learning and Optimization methodology with applications t. To study learning processes: supervised and unsupervised, deterministic and statistical knowledge of Machine learners, and ensemble learning To understand modern techniques and practical trends of Machine learning.
- 4. Applications of NLP • There are the following applications of NLP - • 1. Question Answering • Question Answering focuses on building systems that automatically answer the questions asked by humans in a natural language. • Text and speech processing: This includes Speech recognition, text-&-speech processing, encoding(i.e converting speech or text to machine-readable language), etc. • Text classification: This includes Sentiment Analysis in which the machine can analyze the qualities, emotions, and sarcasm from text and also classify it accordingly. • Language generation: This includes tasks such as machine translation, summary writing, essay writing, etc. which aim to produce coherent and fluent text. • Language interaction: This includes tasks such as dialogue systems, voice assistants, and chatbots, which aim to enable natural communication between humans and computers. 4
- 5. Phases of NLP There are the following five phases of NLP: 5
- 6. Applications of NLP • 1. Lexical Analysis and Morphological • The first phase of NLP is the Lexical Analysis. This phase scans the source code as a stream of characters and converts it into meaningful lexemes. It divides the whole text into paragraphs, sentences, and words. • 2. Syntactic Analysis (Parsing) • Syntactic Analysis is used to check grammar, word arrangements, and shows the relationship among the words. • Example: Agra goes to the Poonam • In the real world, Agra goes to the Poonam, does not make any sense, so this sentence is rejected by the Syntactic analyzer. • 3. Semantic Analysis • Semantic analysis is concerned with the meaning representation. It mainly focuses on the literal meaning of words, phrases, and sentences. 6
- 7. 4. Discourse Integration Discourse Integration depends upon the sentences that proceeds it and also invokes the meaning of the sentences that follow it. 5. Pragmatic Analysis Pragmatic is the fifth and last phase of NLP. It helps you to discover the intended effect by applying a set of rules that characterize cooperative dialogues. For Example: "Open the door" is interpreted as a request instead of an order. 7
- 8. References • Text Books: • 1. Rich E., Artificial Intelligence, Tata McGrawHills. • 2. George F. Luger, Artificial Intelligence: Structures and Strategies for Complex Problem Solving, Pearson Education Asia. • Reference Material: • 1. D.W. Patterson, Introduction to AI and Expert Systems, PHI. • 2. N.J. Nilsson, Principles of Artificial Intelligence, Kaufmann,1980 • 3. Saroj Kaushik, Logic and Prolog Programming, New Age International Publications. • 4. PH.Winston, Artificial Intelligence, Addison Wesley. • WEB LINKS https://www.tutorialspoint.com/artificial_intelligence/artificial_intelligence_natural_language_process ing.htm https://www.geeksforgeeks.org/natural-language-processing-overview/ VIDEO LINKS https://www.youtube.com/watch?v=5hKxvh4RAsY&list=PLyqSpQzTE6M_EcNgdZ2qOtTZe7YI4Eedb 8
- 9. THANK YOU