VALLIAMMAI ENGINEERING COLLEGE SRM Nagar, Kattankulathur DEPARTMENT OF COMPUTER SCIENCE AND ENGINEERING QUESTION BANK VII SEMESTER

Size: px
Start display at page:

Download "VALLIAMMAI ENGINEERING COLLEGE SRM Nagar, Kattankulathur DEPARTMENT OF COMPUTER SCIENCE AND ENGINEERING QUESTION BANK VII SEMESTER"

Transcription

1 VALLIAMMAI ENGINEERING COLLEGE SRM Nagar, Kattankulathur DEPARTMENT OF COMPUTER SCIENCE AND ENGINEERING QUESTION BANK VII SEMESTER CS6007-INFORMATION RETRIEVAL Regulation 2013 Academic Year Prepared by Dr.M.Senthil Kumar, Associate Professor/CSE

2 VALLIAMMAI ENGINEERING COLLEGE SRM Nagar, Kattankulathur DEPARTMENT OF COMPUTER SCIENCE AND ENGINEERING QUESTION BANK SUBJECT : CS6007 INFORMATION RETRIEVAL SEM/YEAR: VII/IV UNIT I -INTRODUCTION Introduction -History of IR- Components of IR Issues Open source Search engine Frameworks The impact of the web on IR The role of artificial intelligence (AI) in IR IR Versus Web Search Components of a Search engine- Characterizing the web. PART-A 1 Discuss about Peer to Peer Search. 2 Identify the need of Information Retrieval 3 List and explain the components of IR block diagram. 4 List the fundamental concepts in IR. 5 Express the need of tiered indexes. 6 Interpret the role of Artificial Intelligence (AI) in IR. 7 Differentiate data retrieval and information retrieval. 8 Give the components of Search Engine and the performance measures. 9 What is an extractor? 10 Show the issues that affects IR. BTL 3 Apply 11 Give the purpose of Query Interface. BTL 6 Create 12 Summarize the queries of IR. BTL 5 Evaluate 13 Design the IR architecture diagram.. BTL 6 Create 14 State the impact of WEB on IR. 15 Showthe type of natural language technology used in information BTL 3 Apply retrieval. 16 Define Information Retrieval 17 What is search engine? 18 Compare IR vs Web Search. 19 Illustrate the function of Information Retrieval System. BTL 3 Apply 20 Summarize on text acquisition. BTL 5 Evaluate

3 PART B 1 i)summarize the history of IR.(7) ii) Explain the purpose of Information Retrieval System.(6) 2 Describe the various components of Information Retrieval System with neat diagram. (13) 3 i)define Information Retrieval system and its features.(4) ii)describe the different stages of IR system.(9) 4. i) Identify the various issues in IR system.(7) ii) Examine the various impact of WEB on IR (6) 5 Discuss in detail about the framework of Open Source Search engine with necessary diagrams. (13) 6 i) Compare in detail Information Retrieval and Web Search with examples.(8) ii) the fundamental concepts involved in IR system. (5) 7 Demonstrate the role of Artificial Intelligence in Information Retrieval Systems. (13) BTL 5 BTL 1 BTL 1 BTL 1 BTL 2 BTL 3 Evaluate Remember Remember Remember Understand Apply 8 i)describe the various components of a Search Engine. (8) ii) Express the various Search Engine available in current world. (5) 9 i)formulate the working of Search Engine.(8) BTL 6 Create ii)generalize the Process of Search Engine in detail.(5) 10 i) Demonstrate the working of IR architecture with a diagram.(6) ii) Infer How Designing Parsing and Scoring functions BTL 3 Apply works in detail. (7) 11 i)define Information Retrieval.(2) ii) Describe in detail the IR system, Fundamental concepts, need and purpose of the system.(4+4+3) 12 Explain how to characterize the web in detail. (13) 13 Explain the different types of computer software used in computer architecture.(13) 14 i) Differentiate database and Information Retrieval with example (4) ii)summarize the functions and features of Information Retrieval Systems.( 9) PART-C 1 Create an open source search engine like Google with suitable BTL 6 Create functionalities. 2 Evaluate the best search engines other than Google and explain any BTL 5 Evaluate five of them in detail. 3 how the AI impact Search and Search Engine optimization 4 Generalize the Deep Learning and Human Learning capabilities in Future of Search engine Optimization. BTL 6 Create

4 UNIT II - INFORMATION RETRIEVAL Boolean and vector-space retrieval models- Term weighting TF-IDF weighting- cosine similarity Preprocessing Inverted indices efficient processing with sparse vectors Language Model based IR Probabilistic IR Latent Semantic Indexing Relevance feedback and query expansion. PART-A 1 Demonstrate probabilistic Information Retrieval. BTL 3 Apply 2 the Boolean model. 3 Construct the Vector space model representation. BTL 3 Apply 4 List the classes of retrieval model. 5 Define Retrieval model. 6 Express language modelling with example. 7 Illustrate similarity measure. BTL 3 Apply 8 the problems in lexical semantics. 9 Differentiate language model and naïve bayes. 10 Formulate the Bayesian rule. BTL 6 Create 11 What is meant by sparse vector? 12 Design an Inverted file with an example. BTL 6 Create 13 Evaluate the goals of LSI. BTL 5 Evaluate 14 What is smoothing? 15 Give probabilistic approaches to IR. 16 What is meant Zone Index? 17 Interpret cosine similarity measure. 18 relevance feedback 19 List the steps involved in preprocessing. 20 Generalize on why distance is not preferred compared to angle. BTL 5 Evaluate PART-B 1 i) Express what is Boolean retrieval model. (4) ii) Discuss the Boolean retrieval in detail with diagram. (9) BTL 2 Understand 2 Illustrate the Vector space retrieval model with example (13) BTL 3 Apply 3 Describe about basic concepts of Cosine similarity. (13) 4 Develop on example to implement term weighting.(min docs = 5) (13) BTL 6 Create 5 i) Tabulate the common preprocessing steps.(4) ii)describe the document preprocessing steps in detail.(9) 6 i)discuss in detail about term frequency and Inverse Document Frequency. (7) ii)compute TF-IDF.given a document containing terms with the given frequencies A(3),B(2), C(1).Assume document collections 10,000 and document frequencies of these terms are A(50), B(1300), C(250) (6) 7 i)explain Latent semantic Indexing and latent semantic space with an illustration.(9)

5 ii) the use of LSI in Information Retrieval. What is its need in synonymy and semantic relatedness.(4) 8 i)examine, how to form a binary term - document incidence matrix (7) ii) Give an example for the above. (6) 9 Describe document preprocessing and its stages in detail. (13) 10 i) Discuss the structure of inverted indices. (7) ii)discuss the searching process in inverted file (6) 11 i)why do we need sparse vectors? (4) BTL 5 Evaluate ii)explain sparse vectors and its efficiency with diagram.(9) 12 i) the language model based IR and its probabilistic representation. (7) ii)compare Language model vs Naive bayes and Language model vs Vector space model (6) 13 Differentiate the various query expansion method with relevance feedback methods.(13) 14 (i)apply how Probabilistic approaches to Information Retrieval are done. (7) (ii) Illustrate the following (6) a) Probabilistic relevance feedback. b) Pseudo relevance feedback. c) Indirect relevance feedback PART-C BTL 3 Apply Q.No Questions BT 1 Compose the information Retrieval services of the internet with suitable BTL 6 design. 2 Assess the best Language model to computational linguistics for BTL 5 investigating the use of software to translate text or speech from one language to another. Competence Create Evaluate 3 Contrast the uses of probabilistic IR in indexing the search in the internet. 4 Create a Relevance feedback mechanism for your college website search in the internet. BTL 6 Create

6 UNIT III-WEB SEARCH ENGINE INTRODUCTION AND CRAWLING Web search overview, web structure, the user, paid placement, search engine optimization/ spam. Web size measurement search engine optimization/spam Web Search Architectures crawling meta-crawlers- Focused Crawling web indexes - Near-duplicate detection Index Compression XML retrieval. PART-A 1 Express the basics of web search with a neat diagram. 2 Define Pay for Placement. 3 What is meant by Search Engine Optimization? 4 List the need of web search engine. 5 Draw the architecture of search engine. 6 Distinguish parallel crawler and meta crawler. 7 List the SPAM Techniques. 8 Evaluate use of Full text indexing and In human indexing. BTL 5 Evaluate 9 State the issues in search engines. 10 Design the Politeness policies used in web crawler. BTL 6 Create 11 Classify the ways to identify duplication. 12 How to Apply duplicate Deduction to web pages? BTL 3 Apply 13 Assess the need for keyword stuffing. BTL 5 Evaluate 14 What are the challenges in data traversing by search engines? 15 Show the applications of search engines. BTL 3 Apply 16 Point out the use of Web indexing and inversion of indexing process. 17 What is focused crawler? 18 Illustrate the hashing technique with example. BTL 3 Apply 19 Classify the types of search engines. 20 Generalize on XML Retrieval. BTL6 Create PART-B 1 Discuss the Search Engine Optimization/SPAM in detail.(13) 2 i)describe in detail about XML Retrieval.(9) ii)what is Structured and Unstructured Retrieval.(4) 3 i)list the types of Search Engine and explain them. (7) ii)describe the working of Search Engine.(6) 4 Design and develop a Web search Architecture and the components of BTL 6 Create search engine and its issues.(13) 5 i)what is P4P? Elaborate on Paid Placement.(7) ii) What is the purpose of Web indexing?(6) 6 i) Summarize on the working of WEB CRAWLER with its diagram.(8) ii) Distinguish visual vs programmatic crawler.(5) 7 i)differentiate meta crawler and focused crawler. (8) ii) on URL normalization.(5) 8 Recommend the need for Near-Duplication Detection by the ways to identify the duplication. (13) BTL 5 Evaluate 9 i)examine the behavior of web crawler and the outcome of crawling policies.(5) ii) Illustrate the following(8) BTL 3 Apply

7 a) Focused Crawling b) Deep web c) Distributed crawling d) Site map 10 i)explain the overview of Web search.(8) ii)describe the structure of WEB and its characteristics(5) 11 Discuss the process of index compression in detail.(13) 12 (i)explain the need for Web Search Engine.(6) (ii)point out the challenges in data traversing by search engine and how will you overcome it.(7) 13 Describe the following with example. (13) i)bag of Words ii) Shingling iii) Hashing iv)min Hash and Sim Hash 14 i)based on the Application of Search Engines, How will you categorize BTL 3 Apply them and what are the issues faced by them? (9) (ii) Demonstrate about Search Engine Optimization. (4) PART-C 1 Develop a web search structure for searching a newly hosted web BTL 6 Create domain by the naïve user with step by step procedure. 2 Grade the optimization techniques available for search engine and rank them by your justification. BTL 5 Evaluate 3 Classify the web crawling methods and illustrate the effects of different crawling policies on data collection. 4 Formulate the application of Near Duplicate Document Detection techniques and also Generalize the advantages in Plagiarism checking. BTL 6 Create UNIT IV- WEB SEARCH LINK ANALYSIS AND SPECIALIZED SEARCH Link Analysis hubs and authorities Page Rank and HITS algorithms Searching and Ranking Relevance Scoring and ranking for Web Similarity Hadoop & Map Reduce Evaluation Personalized search Collaborative filtering and content-based recommendation of documents and products handling invisible Web Snippet generation, Summarization, Question Answering, Cross- Lingual Retrieval. PART-A 1 Describe the main idea of Link Analysis. 2 Illustrate the web as a directed graph. BTL 3 Apply 3 List the issues of page rank algorithm and characteristics of Map reduce Strategy. 4 how citation analysis in done. 5 Quote the importance of Anchor text and indexing. BTL1 Remember 6 Define Hub. BTL1 Remember

8 7 What is meant by Query independent ordering? 8 State the aim of question answering. 9 Differentiate between citations and links. 10 Show the working of random walks in Graphs. BTL 3 Apply 11 Evaluate on Recommender System. BTL 5 Evaluate 12 Define Lossy compression mechanisms 13 Integrate the ideas of HITS Algorithm. BTL 6 Create 14 Assess on the parts of Search engine. BTL 5 Evaluate 15 What is map reduce and snippet generation? 16 Express Recall at rank and Precision at rank. 17 Formulate the examples for boolean queries. BTL 6 Create 18 Categorize the modules of Hadoop Framework. 19 the Collaborative filtering and challenges. 20 Demonstrate Bayesian Inferencing. BTL 3 Apply PART-B 1 i)define Link Analysis and explain in detail.(7) ii)describe in detail about HUBS and Authorities.(6) 2 BTL 2 Understand i) Give the concept of PAGE Ranking in detail. (6) ii)summarize the preprocessing and Query Processing of Page Rank along with its issues.(7) 3 Discuss in detail about HITS Algorithm with necessary examples.(13) 4 BTL 3 Apply Illustrate the abstract search engine and how will you speed snippet generation? Explain with algorithm (13) 5 Describe the aim and purpose of Question Answering in detail. (13) 6 i) Point out stages of summarization. (7) ii) how Handling Invisible Web is done. (6) 7 i)evaluate the concept of Personalized Search.(7) ii)assess the methodology used in it. (6) 8 i) content based recommendations of documents and products.(7) ii) the process of cross lingual retrieval (6) 9 i)formulate the working of HADOOP.(7) ii) Compose the Map Reduce in detail. (6) 10 i)define contextual computing and discuss on Personalized search (9) ii)describe how to solve privacy problems(4) 11 i) Explain working of collaborative filtering by analyzing any two case study. (8) ii) Give the challenges of Collaborative filtering.(5) BTL 5 BTL 6 BTL 1 Evaluate Create Remember 12 Describe the Searching and Ranking process in detail with necessary examples. (13) BTL 1 Remember 13 BTL 3 Apply

9 i) Show the performance of TREC Systems. (7) ii) Illustrate the CLIR Approaches (6) 14 i)describe in detail about of SNIPPET Generation along with example.(6) ii) Summarize in detail about community-based Question Answering system in IR.(7) PART-C 1 Generalize how Link analysis has been instrumental in the development BTL 6 Create of web search. 2 Summarize the impacts of in-links and link-spam in the link analysis. BTL 5 Evaluate 3 any five online utility tools available for searching and ranking in information retrieval. 4 Design a Plan to overcome the challenges in Cross-Lingual Retrieval. BTL 6 Create UNIT V-DOCUMENT TEXT MINING Information filtering; organization and relevance feedback Text Mining Text classification and clustering Categorization algorithms: naïve Bayes; decision trees; and nearest neighbor Clustering algorithms: agglomerative clustering; k-means; expectation maximization (EM). PART-A Q.No Questions BT Competence 1 Distinguish IF vs IR. 2 Define the general features of Filtering. 3 Give the idea of filtering rules and attributes 4 Compare Automatic vs Social Filtering 5 What is the need of Filtering against spamming? 6 Give some examples of EM. 7 What is Text Mining and Dendrogram? 8 Evaluate the process of Text Mining. BTL 5 Evaluate 9 Formulate the estimation of Multinomial document model and BTL 6 Create Bernoulli document model. 10 the types of filters 11 Integrate the problems of k-means method. BTL 6 Create 12 State positive and negative feedback. 13 Summarize relevance feedback with example.. 14 What are the types of data in clustering analysis? 15 Point out the advantages and disadvantages of Decision Tree algorithm. 16 Show the applications of text mining. BTL 3 Apply 17 Illustrate the advantages of Naiye Bayes. BTL 3 Apply 18 Assess how to measure distance of clusters? BTL 5 Evaluate 19 Distinguish Supervised learning and Unsupervised Learning. 20 Summarize the major clustering approaches. BTL 3 Apply PART-B 1 i)list the general features of Filtering, rules and its attributes.(7)

10 ii) Describe the filtering using IR in detail.(6) 2 i)describe in detail the various types of filters, Profiling and filtering technologies in detail.(9) ii) Describe in detail the Multiple-Bernoulli and the multinominal mode.(4) 3 (i)give the examples of EM method. (4) (ii)summarize the profiling and Filtering Technologies.(9) 4 i)express the process of Text Mining. (6) ii)explain the challenges and application of Text Mining.(7) 5 the procedure involved in Expectation Maximization along with the steps involved in it. (13) 6 (i)define Topic detection and tracking, Clustering in TDT. (4) (ii)examine in detail about Cluster Analysis in Text Clustering.(9) 7 Illustrate in detail with examples about Organization and Relevance BTL 3 Apply feedback. (13) 8 (i)evaluate the Agglomerative Clustering and HAC in detail. (7) BTL 5 Evaluate (ii) Evaluate on the various classification methods of Text. (6) 9 BTL 6 Create i)summarize on Clustering Algorithms.(6) ii) Rearrange the Types of data in cluster analysis.(7) 10 i) the working of Nearest Neighbor algorithm along with one representation. (7) ii) the K-Means Clustering method and the problems in it. (6) 11 about Decision Tree Algorithm with illustration. (13) 12 i)describe in detail about Text Mining.(7) ii)examine the process of Mining with detailed example.(6) 13 i) Discuss in detail about Text Classification.(7) ii) Summarize Text Clustering in detail.(6) 14 i)apply Naïve Bayes Algorithm for an example.(7) BTL 3 Apply ii) Demonstrate its working in detail. (6) PART-C 1 Prepare how Information Filtering has been instrumental in the BTL 6 Create development of document text mining by the massive users in internet. 2 Rank the impacts of Categorization and clustering of text in the mining with the suitable examples. BTL 5 Evaluate 3 any online utility tools available for text analytics software to transform unstructured text into structured data in text mining. 4 Design a Plan to overcome the gap in decision theoretic approach for evaluation in text mining. BTL 6 Create

Department of Computer Science and Engineering B.E/B.Tech/M.E/M.Tech : B.E. Regulation: 2013 PG Specialisation : _

Department of Computer Science and Engineering B.E/B.Tech/M.E/M.Tech : B.E. Regulation: 2013 PG Specialisation : _ COURSE DELIVERY PLAN - THEORY Page 1 of 6 Department of Computer Science and Engineering B.E/B.Tech/M.E/M.Tech : B.E. Regulation: 2013 PG Specialisation : _ LP: CS6007 Rev. No: 01 Date: 27/06/2017 Sub.

More information

S.No QUESTIONS COMPETENCE LEVEL UNIT -1 PART A 1. Illustrate the evolutionary trend towards parallel distributed and cloud computing.

S.No QUESTIONS COMPETENCE LEVEL UNIT -1 PART A 1. Illustrate the evolutionary trend towards parallel distributed and cloud computing. VALLIAMMAI ENGNIEERING COLLEGE SRM Nagar, Kattankulathur 603203. DEPARTMENT OF COMPUTER SCIENCE AND ENGINEERING Year & Semester : IV & VII Section : CSE -1& 2 Subject Code : CS6703 Subject Name : Grid

More information

VALLIAMMAI ENGINEERING COLLEGE

VALLIAMMAI ENGINEERING COLLEGE VALLIAMMAI ENGINEERING COLLEGE SRM Nagar, Kattankulathur 603 203 DEPARTMENT OF COMPUTER SCIENCE AND ENGINEERING QUESTION BANK V SEMESTER CS6502-OBJECT ORIENTED ANALYSIS AND DESIGN Regulation 2013 Academic

More information

VALLIAMMAI ENGNIEERING COLLEGE SRM Nagar, Kattankulathur 603203. DEPARTMENT OF COMPUTER SCIENCE AND ENGINEERING Year & Semester : III & VI Section : CSE - 2 Subject Code : IT6702 Subject Name : Data warehousing

More information

VALLIAMMAI ENGINEERING COLLEGE SRM Nagar, Kattankulathur-603203 DEPARTMENT OF COMPUTER SCIENCE AND ENGINEERING Academic Year: 2015-16 QUESTION BANK - EVEN SEMESTER Year & Semester : II year & IV sem Section

More information

VALLIAMMAI ENGINEERING COLLEGE

VALLIAMMAI ENGINEERING COLLEGE VALLIAMMAI ENGINEERING COLLEGE SRM Nagar, Kattankulathur 60 20 DEPARTMENT OF INFORMATION TECHNOLOGY QUESTION BANK VI SEMESTER CS6660 COMPILER DESIGN Regulation 20 Academic Year 207 8 Prepared by Ms. S.

More information

Part I: Data Mining Foundations

Part I: Data Mining Foundations Table of Contents 1. Introduction 1 1.1. What is the World Wide Web? 1 1.2. A Brief History of the Web and the Internet 2 1.3. Web Data Mining 4 1.3.1. What is Data Mining? 6 1.3.2. What is Web Mining?

More information

Chapter 27 Introduction to Information Retrieval and Web Search

Chapter 27 Introduction to Information Retrieval and Web Search Chapter 27 Introduction to Information Retrieval and Web Search Copyright 2011 Pearson Education, Inc. Publishing as Pearson Addison-Wesley Chapter 27 Outline Information Retrieval (IR) Concepts Retrieval

More information

Chapter 6: Information Retrieval and Web Search. An introduction

Chapter 6: Information Retrieval and Web Search. An introduction Chapter 6: Information Retrieval and Web Search An introduction Introduction n Text mining refers to data mining using text documents as data. n Most text mining tasks use Information Retrieval (IR) methods

More information

UNIT 1-UMAL DIAGRAMS. Q.No. Question Competence Level. 1 What is Object Oriented analysis & Design? Remembering BTL1

UNIT 1-UMAL DIAGRAMS. Q.No. Question Competence Level. 1 What is Object Oriented analysis & Design? Remembering BTL1 Year & Semester : III & VI Section : CSE 1 & 2 Subject Code : CS6502 Subject Name : OBJECT ORIENTED ANALYSIS AND DESIGN Degree & Branch : B.E (CSE) Staff in charge : Dr.B.VANATHI & Mr.K.SHANMUGAM PART

More information

Introduction p. 1 What is the World Wide Web? p. 1 A Brief History of the Web and the Internet p. 2 Web Data Mining p. 4 What is Data Mining? p.

Introduction p. 1 What is the World Wide Web? p. 1 A Brief History of the Web and the Internet p. 2 Web Data Mining p. 4 What is Data Mining? p. Introduction p. 1 What is the World Wide Web? p. 1 A Brief History of the Web and the Internet p. 2 Web Data Mining p. 4 What is Data Mining? p. 6 What is Web Mining? p. 6 Summary of Chapters p. 8 How

More information

VALLIAMMAI ENGNIEERING COLLEGE SRM Nagar, Kattankulathur

VALLIAMMAI ENGNIEERING COLLEGE SRM Nagar, Kattankulathur VALLIAMMAI ENGNIEERING COLLEGE SRM Nagar, Kattankulathur 603203. DEPARTMENT OF COMPUTER SCIENCE AND ENGINEERING Year & Semester : III & VI Section : CSE 1 & 2 Subject Code : CS6660 Subject Name : COMPILER

More information

VALLIAMMAI ENGINEERING COLLEGE

VALLIAMMAI ENGINEERING COLLEGE VALLIAMMAI ENGINEERING COLLEGE SRM Nagar, Kattankulathur 603 203 DEPARTMENT OF INFORMATION TECHNOLOGY QUESTION BANK VIII SEMESTER IT6012- TCP/IP DESIGN AND IMPLEMENTATION Regulation 2013 Academic Year

More information

VALLIAMMAI ENGINEERING COLLEGE

VALLIAMMAI ENGINEERING COLLEGE VALLIAMMAI ENGINEERING COLLEGE SRM Nagar, Kattankulathur 603 203 DEPARTMENT OF ELECTRONICS AND COMMUNICATION ENGINEERING QUESTION BANK VII SEMESTER EC6013 Advanced Microprocessors and Microcontrollers

More information

VALLIAMMAI ENGINEERING COLLEGE

VALLIAMMAI ENGINEERING COLLEGE VALLIAMMAI ENGINEERING COLLEGE SRM Nagar, Kattankulathur 603 203 DEPARTMENT OF ELECTRICAL AND ELECTRONICS ENGINEERING QUESTION BANK V SEMESTER EE6502- MICROPROCESSORS AND MICROCONTROLLERS Regulation 2013

More information

VALLIAMMAI ENGINEERING COLLEGE SRM Nagar, Kattankulathur

VALLIAMMAI ENGINEERING COLLEGE SRM Nagar, Kattankulathur VALLIAMMAI ENGINEERING COLLEGE SRM Nagar, Kattankulathur 60 0 DEPARTMENT OF COMPUTER SCIENCE AND ENGINEERING QUESTION BANK III SEMESTER CS89- DATA STRUCTURES Regulation 07 Academic Year 08 9 Prepared by

More information

Bing Liu. Web Data Mining. Exploring Hyperlinks, Contents, and Usage Data. With 177 Figures. Springer

Bing Liu. Web Data Mining. Exploring Hyperlinks, Contents, and Usage Data. With 177 Figures. Springer Bing Liu Web Data Mining Exploring Hyperlinks, Contents, and Usage Data With 177 Figures Springer Table of Contents 1. Introduction 1 1.1. What is the World Wide Web? 1 1.2. A Brief History of the Web

More information

Mining Web Data. Lijun Zhang

Mining Web Data. Lijun Zhang Mining Web Data Lijun Zhang zlj@nju.edu.cn http://cs.nju.edu.cn/zlj Outline Introduction Web Crawling and Resource Discovery Search Engine Indexing and Query Processing Ranking Algorithms Recommender Systems

More information

VALLIAMMAI ENGNIEERING COLLEGE SRM Nagar, Kattankulathur 603203. DEPARTMENT OF COMPUTER SCIENCE AND ENGINEERING Year & Semester : III and VI Section : CSE- 1 & 2 Subject Code : CS6601 Subject Name : DISTRIBUTED

More information

CS473: Course Review CS-473. Luo Si Department of Computer Science Purdue University

CS473: Course Review CS-473. Luo Si Department of Computer Science Purdue University CS473: CS-473 Course Review Luo Si Department of Computer Science Purdue University Basic Concepts of IR: Outline Basic Concepts of Information Retrieval: Task definition of Ad-hoc IR Terminologies and

More information

VALLIAMMAI ENGINEERING COLLEGE

VALLIAMMAI ENGINEERING COLLEGE VALLIAMMAI ENGINEERING COLLEGE SRM Nagar, Kattankulathur 60 20 DEPARTMENT OF COMPUTER SCIENCE AND ENGINEERING QUESTION BANK B.E I SEMESTER GE85- Problem Solving and Python Programming Regulation 207 Academic

More information

Mining Web Data. Lijun Zhang

Mining Web Data. Lijun Zhang Mining Web Data Lijun Zhang zlj@nju.edu.cn http://cs.nju.edu.cn/zlj Outline Introduction Web Crawling and Resource Discovery Search Engine Indexing and Query Processing Ranking Algorithms Recommender Systems

More information

VALLIAMMAI ENGINEERING COLLEGE

VALLIAMMAI ENGINEERING COLLEGE VALLIAMMAI ENGINEERING COLLEGE SRM Nagar, Kattankulathur 603 203 DEPARTMENT OF COMPUTER SCIENCE AND ENGINEERING QUESTION BANK IV SEMESTER CS6401 OPERATING SYSTEMS Regulation 2013 Academic Year 2017 18

More information

VALLIAMMAI ENGINEERING COLLEGE

VALLIAMMAI ENGINEERING COLLEGE VALLIAMMAI ENGINEERING COLLEGE SRM Nagar, Kattankulathur 603 203 DEPARTMENT OF INFORMATION TECHNOLOGY QUESTION BANK III SEMESTER CS8391-Data Structures Regulation 2017 Academic Year 2018 19(odd Semester)

More information

VALLIAMMAI ENGINEERING COLLEGE

VALLIAMMAI ENGINEERING COLLEGE VALLIAMMAI ENGINEERING COLLEGE SRM Nagar, Kattankulathur 603 203 DEPARTMENT OF COMPUTER SCIENCE AND ENGINEERING QUESTION BANK II SEMESTER CP7204 Advanced Operating Systems Regulation 2013 Academic Year

More information

Information Retrieval. CS630 Representing and Accessing Digital Information. What is a Retrieval Model? Basic IR Processes

Information Retrieval. CS630 Representing and Accessing Digital Information. What is a Retrieval Model? Basic IR Processes CS630 Representing and Accessing Digital Information Information Retrieval: Retrieval Models Information Retrieval Basics Data Structures and Access Indexing and Preprocessing Retrieval Models Thorsten

More information

Information Retrieval

Information Retrieval Information Retrieval CSC 375, Fall 2016 An information retrieval system will tend not to be used whenever it is more painful and troublesome for a customer to have information than for him not to have

More information

VALLIAMMAI ENGINEERING COLLEGE

VALLIAMMAI ENGINEERING COLLEGE VALLIAMMAI ENGINEERING COLLEGE SRM Nagar, Kattankulathur 603 203 DEPARTMENT OF COMPUTER SCIENCE AND ENGINEERING QUESTION BANK IV SEMESTER EC6504 MICROPROCESSOR AND MICROCONTROLLER Regulation 2013 Academic

More information

DEPARTMENT OF INFORMATION TECHNOLOGY / COMPUTER SCIENCE AND ENGINEERING UNIT -1-INTRODUCTION TO COMPILERS 2 MARK QUESTIONS

DEPARTMENT OF INFORMATION TECHNOLOGY / COMPUTER SCIENCE AND ENGINEERING UNIT -1-INTRODUCTION TO COMPILERS 2 MARK QUESTIONS BHARATHIDASAN ENGINEERING COLLEGE DEPARTMENT OF INFORMATION TECHNOLOGY / COMPUTER SCIENCE AND ENGINEERING Year & Semester : III & VI Degree & Branch : B.E (CSE) /B.Tech (Information Technology) Subject

More information

INFORMATION TECHNOLOGY HANDLED & PREPARED BY Dr. N.KRISHNARAJ,A.P(Sel.G) MS. R. THENMOZHI, AP (Sel.G)

INFORMATION TECHNOLOGY HANDLED & PREPARED BY Dr. N.KRISHNARAJ,A.P(Sel.G) MS. R. THENMOZHI, AP (Sel.G) VALLIAMMAI ENGINEERING COLLEGE SRM Nagar, Kattankulathur-603203 DEPARTMENT OF INFORMATION TECHNOLOGY Academic Year: 2016-17 QUESTION BANK ODD SEMESTER NAME OF THE SUBJECT GRAPHICS AND MULTIMEDIA SUBJECT

More information

modern database systems lecture 4 : information retrieval

modern database systems lecture 4 : information retrieval modern database systems lecture 4 : information retrieval Aristides Gionis Michael Mathioudakis spring 2016 in perspective structured data relational data RDBMS MySQL semi-structured data data-graph representation

More information

Chapter 2. Architecture of a Search Engine

Chapter 2. Architecture of a Search Engine Chapter 2 Architecture of a Search Engine Search Engine Architecture A software architecture consists of software components, the interfaces provided by those components and the relationships between them

More information

NAME OF THE SUBJECT SUBJECT CODE SEMESTER YEAR DEPARTMENT HANDLED & PREPARED BY VALLIAMMAI ENGINEERING COLLEGE SRM Nagar, Kattankulathur-60303 DEPARTMENT OF COMPUTER SCIENCE AND ENGINEERING Academic Year:

More information

DEPARTMENT OF ELECTRONICS & COMMUNICATION ENGINEERING QUESTION BANK

DEPARTMENT OF ELECTRONICS & COMMUNICATION ENGINEERING QUESTION BANK DEPARTMENT OF ELECTRONICS & COMMUNICATION ENGINEERING QUESTION BANK SUBJECT : CS6303 / COMPUTER ARCHITECTURE SEM / YEAR : VI / III year B.E. Unit I OVERVIEW AND INSTRUCTIONS Part A Q.No Questions BT Level

More information

Introduction to Information Retrieval

Introduction to Information Retrieval Introduction to Information Retrieval Mohsen Kamyar چهارمین کارگاه ساالنه آزمایشگاه فناوری و وب بهمن ماه 1391 Outline Outline in classic categorization Information vs. Data Retrieval IR Models Evaluation

More information

CS371R: Final Exam Dec. 18, 2017

CS371R: Final Exam Dec. 18, 2017 CS371R: Final Exam Dec. 18, 2017 NAME: This exam has 11 problems and 16 pages. Before beginning, be sure your exam is complete. In order to maximize your chance of getting partial credit, show all of your

More information

7. Discuss the hardware signals and superscalar architecture of Pentium BTL 2 Understand

7. Discuss the hardware signals and superscalar architecture of Pentium BTL 2 Understand UNIT I HIGH PERFORMANCE CISC ARCHITECTURE PENTIUM CPU Architecture- Bus Operations Pipelining Branch predication floating point unit- Operating Modes Paging Multitasking Exception and Interrupts Instruction

More information

60-538: Information Retrieval

60-538: Information Retrieval 60-538: Information Retrieval September 7, 2017 1 / 48 Outline 1 what is IR 2 3 2 / 48 Outline 1 what is IR 2 3 3 / 48 IR not long time ago 4 / 48 5 / 48 now IR is mostly about search engines there are

More information

Search Engines. Information Retrieval in Practice

Search Engines. Information Retrieval in Practice Search Engines Information Retrieval in Practice All slides Addison Wesley, 2008 Classification and Clustering Classification and clustering are classical pattern recognition / machine learning problems

More information

Collective Intelligence in Action

Collective Intelligence in Action Collective Intelligence in Action SATNAM ALAG II MANNING Greenwich (74 w. long.) contents foreword xv preface xvii acknowledgments xix about this book xxi PART 1 GATHERING DATA FOR INTELLIGENCE 1 "1 Understanding

More information

CS54701: Information Retrieval

CS54701: Information Retrieval CS54701: Information Retrieval Basic Concepts 19 January 2016 Prof. Chris Clifton 1 Text Representation: Process of Indexing Remove Stopword, Stemming, Phrase Extraction etc Document Parser Extract useful

More information

Search Engines Information Retrieval in Practice

Search Engines Information Retrieval in Practice Search Engines Information Retrieval in Practice W. BRUCE CROFT University of Massachusetts, Amherst DONALD METZLER Yahoo! Research TREVOR STROHMAN Google Inc. ----- PEARSON Boston Columbus Indianapolis

More information

CS 6320 Natural Language Processing

CS 6320 Natural Language Processing CS 6320 Natural Language Processing Information Retrieval Yang Liu Slides modified from Ray Mooney s (http://www.cs.utexas.edu/users/mooney/ir-course/slides/) 1 Introduction of IR System components, basic

More information

Representation/Indexing (fig 1.2) IR models - overview (fig 2.1) IR models - vector space. Weighting TF*IDF. U s e r. T a s k s

Representation/Indexing (fig 1.2) IR models - overview (fig 2.1) IR models - vector space. Weighting TF*IDF. U s e r. T a s k s Summary agenda Summary: EITN01 Web Intelligence and Information Retrieval Anders Ardö EIT Electrical and Information Technology, Lund University March 13, 2013 A Ardö, EIT Summary: EITN01 Web Intelligence

More information

VALLIAMMAI ENGINEERING COLLEGE

VALLIAMMAI ENGINEERING COLLEGE VALLIAMMAI ENGINEERING COLLEGE SRM Nagar, Kattankulathur 603 203 DEPARTMENT OF ELECTRONICS AND INSTRUMENTATION ENGINEERING QUESTION BANK VI SEMESTER EE6602 EMBEDDED SYSTEMS Regulation 2013 Academic Year

More information

Information Retrieval CS Lecture 01. Razvan C. Bunescu School of Electrical Engineering and Computer Science

Information Retrieval CS Lecture 01. Razvan C. Bunescu School of Electrical Engineering and Computer Science Information Retrieval CS 6900 Razvan C. Bunescu School of Electrical Engineering and Computer Science bunescu@ohio.edu Information Retrieval Information Retrieval (IR) is finding material of an unstructured

More information

VALLIAMMAI ENGINEERING COLLEGE

VALLIAMMAI ENGINEERING COLLEGE VALLIAMMAI ENGINEERING COLLEGE SRM Nagar, Kattankulathur 603 203 DEPARTMENT OF COMPUTER SCIENCE AND ENGINEERING QUESTION BANK VI SEMESTER CS6660-COMPILER DESIGN Regulation 2013 Academic Year 2017 18 (Even)

More information

Information Retrieval Spring Web retrieval

Information Retrieval Spring Web retrieval Information Retrieval Spring 2016 Web retrieval The Web Large Changing fast Public - No control over editing or contents Spam and Advertisement How big is the Web? Practically infinite due to the dynamic

More information

CS377: Database Systems Text data and information. Li Xiong Department of Mathematics and Computer Science Emory University

CS377: Database Systems Text data and information. Li Xiong Department of Mathematics and Computer Science Emory University CS377: Database Systems Text data and information retrieval Li Xiong Department of Mathematics and Computer Science Emory University Outline Information Retrieval (IR) Concepts Text Preprocessing Inverted

More information

Information Retrieval: Retrieval Models

Information Retrieval: Retrieval Models CS473: Web Information Retrieval & Management CS-473 Web Information Retrieval & Management Information Retrieval: Retrieval Models Luo Si Department of Computer Science Purdue University Retrieval Models

More information

Information Retrieval

Information Retrieval Multimedia Computing: Algorithms, Systems, and Applications: Information Retrieval and Search Engine By Dr. Yu Cao Department of Computer Science The University of Massachusetts Lowell Lowell, MA 01854,

More information

Building Search Applications

Building Search Applications Building Search Applications Lucene, LingPipe, and Gate Manu Konchady Mustru Publishing, Oakton, Virginia. Contents Preface ix 1 Information Overload 1 1.1 Information Sources 3 1.2 Information Management

More information

DATA MINING - 1DL105, 1DL111

DATA MINING - 1DL105, 1DL111 1 DATA MINING - 1DL105, 1DL111 Fall 2007 An introductory class in data mining http://user.it.uu.se/~udbl/dut-ht2007/ alt. http://www.it.uu.se/edu/course/homepage/infoutv/ht07 Kjell Orsborn Uppsala Database

More information

VALLIAMMAI ENGINEERING COLLEGE

VALLIAMMAI ENGINEERING COLLEGE VALLIAMMAI ENGINEERING COLLEGE SRM Nagar, Kattankulathur 603 203 DEPARTMENT OF ELECTRONICS AND COMMUNICATION ENGINEERING QUESTION BANK V SEMESTER EC6504 MICROPROCESSOR AND MICROCONTROLLER Regulation 2013

More information

Birkbeck (University of London)

Birkbeck (University of London) Birkbeck (University of London) MSc Examination for Internal Students Department of Computer Science and Information Systems Information Retrieval and Organisation (COIY64H7) Credit Value: 5 Date of Examination:

More information

TEXT MINING APPLICATION PROGRAMMING

TEXT MINING APPLICATION PROGRAMMING TEXT MINING APPLICATION PROGRAMMING MANU KONCHADY CHARLES RIVER MEDIA Boston, Massachusetts Contents Preface Acknowledgments xv xix Introduction 1 Originsof Text Mining 4 Information Retrieval 4 Natural

More information

Pre-Requisites: CS2510. NU Core Designations: AD

Pre-Requisites: CS2510. NU Core Designations: AD DS4100: Data Collection, Integration and Analysis Teaches how to collect data from multiple sources and integrate them into consistent data sets. Explains how to use semi-automated and automated classification

More information

VALLIAMMAI ENGINEERING COLLEGE

VALLIAMMAI ENGINEERING COLLEGE VALLIAMMAI ENGINEERING COLLEGE SRM Nagar, Kattankulathur 603 203 DEPARTMENT OF MECHANICAL ENGINEERING QUESTION BANK M.E: CAD/CAM I SEMESTER ED5151 COMPUTER APPLICATIONS IN DESIGN Regulation 2017 Academic

More information

Exam IST 441 Spring 2011

Exam IST 441 Spring 2011 Exam IST 441 Spring 2011 Last name: Student ID: First name: I acknowledge and accept the University Policies and the Course Policies on Academic Integrity This 100 point exam determines 30% of your grade.

More information

A Survey Of Different Text Mining Techniques Varsha C. Pande 1 and Dr. A.S. Khandelwal 2

A Survey Of Different Text Mining Techniques Varsha C. Pande 1 and Dr. A.S. Khandelwal 2 A Survey Of Different Text Mining Techniques Varsha C. Pande 1 and Dr. A.S. Khandelwal 2 1 Department of Electronics & Comp. Sc, RTMNU, Nagpur, India 2 Department of Computer Science, Hislop College, Nagpur,

More information

DEPARTMENT OF COMPUTER SCIENCE AND ENGINEERING

DEPARTMENT OF COMPUTER SCIENCE AND ENGINEERING SHRI ANGALAMMAN COLLEGE OF ENGINEERING & TECHNOLOGY (An ISO 9001:2008 Certified Institution) SIRUGANOOR,TRICHY-621105. DEPARTMENT OF COMPUTER SCIENCE AND ENGINEERING Year / Semester: IV/VII CS1011-DATA

More information

VALLIAMMAI ENGINEERING COLLEGE SRM Nagar, Kattankulathur

VALLIAMMAI ENGINEERING COLLEGE SRM Nagar, Kattankulathur VALLIAMMAI ENGINEERING COLLEGE SRM Nagar, Kattankulathur 603 203. DEPARTMENT OF COMPUTER SCIENCE AND ENGINEERING QUESTION BANK VII SEMESTER CS 6004 CYBER FORENSICS Regulation 2013 Academic Year 2017 2018

More information

VALLIAMMAI ENGNIEERING COLLEGE SRM Nagar, Kattankulathur DEPARTMENT OF COMPUTER SCIENCE AND ENGINEERING QUESTION BANK

VALLIAMMAI ENGNIEERING COLLEGE SRM Nagar, Kattankulathur DEPARTMENT OF COMPUTER SCIENCE AND ENGINEERING QUESTION BANK VALLIAMMAI ENGNIEERING COLLEGE SRM Nagar, Kattankulathur 603203. DEPARTMENT OF COMPUTER SCIENCE AND ENGINEERING QUESTION BANK Degree & Branch : B.E E.C.E. Year & Semester : II / IV Section : ECE 1, 2 &

More information

An Introduction to Search Engines and Web Navigation

An Introduction to Search Engines and Web Navigation An Introduction to Search Engines and Web Navigation MARK LEVENE ADDISON-WESLEY Ал imprint of Pearson Education Harlow, England London New York Boston San Francisco Toronto Sydney Tokyo Singapore Hong

More information

10/10/13. Traditional database system. Information Retrieval. Information Retrieval. Information retrieval system? Information Retrieval Issues

10/10/13. Traditional database system. Information Retrieval. Information Retrieval. Information retrieval system? Information Retrieval Issues COS 597A: Principles of Database and Information Systems Information Retrieval Traditional database system Large integrated collection of data Uniform access/modifcation mechanisms Model of data organization

More information

James Mayfield! The Johns Hopkins University Applied Physics Laboratory The Human Language Technology Center of Excellence!

James Mayfield! The Johns Hopkins University Applied Physics Laboratory The Human Language Technology Center of Excellence! James Mayfield! The Johns Hopkins University Applied Physics Laboratory The Human Language Technology Center of Excellence! (301) 219-4649 james.mayfield@jhuapl.edu What is Information Retrieval? Evaluation

More information

International Journal of Advance Foundation and Research in Science & Engineering (IJAFRSE) Volume 1, Issue 2, July 2014.

International Journal of Advance Foundation and Research in Science & Engineering (IJAFRSE) Volume 1, Issue 2, July 2014. A B S T R A C T International Journal of Advance Foundation and Research in Science & Engineering (IJAFRSE) Information Retrieval Models and Searching Methodologies: Survey Balwinder Saini*,Vikram Singh,Satish

More information

Outline. Structures for subject browsing. Subject browsing. Research issues. Renardus

Outline. Structures for subject browsing. Subject browsing. Research issues. Renardus Outline Evaluation of browsing behaviour and automated subject classification: examples from KnowLib Subject browsing Automated subject classification Koraljka Golub, Knowledge Discovery and Digital Library

More information

Text Analytics (Text Mining)

Text Analytics (Text Mining) CSE 6242 / CX 4242 Apr 1, 2014 Text Analytics (Text Mining) Concepts and Algorithms Duen Horng (Polo) Chau Georgia Tech Some lectures are partly based on materials by Professors Guy Lebanon, Jeffrey Heer,

More information

Information Retrieval May 15. Web retrieval

Information Retrieval May 15. Web retrieval Information Retrieval May 15 Web retrieval What s so special about the Web? The Web Large Changing fast Public - No control over editing or contents Spam and Advertisement How big is the Web? Practically

More information

Models for Document & Query Representation. Ziawasch Abedjan

Models for Document & Query Representation. Ziawasch Abedjan Models for Document & Query Representation Ziawasch Abedjan Overview Introduction & Definition Boolean retrieval Vector Space Model Probabilistic Information Retrieval Language Model Approach Summary Overview

More information

Information Retrieval

Information Retrieval Natural Language Processing SoSe 2015 Information Retrieval Dr. Mariana Neves June 22nd, 2015 (based on the slides of Dr. Saeedeh Momtazi) Outline Introduction Indexing Block 2 Document Crawling Text Processing

More information

Keyword Extraction by KNN considering Similarity among Features

Keyword Extraction by KNN considering Similarity among Features 64 Int'l Conf. on Advances in Big Data Analytics ABDA'15 Keyword Extraction by KNN considering Similarity among Features Taeho Jo Department of Computer and Information Engineering, Inha University, Incheon,

More information

Machine Learning using MapReduce

Machine Learning using MapReduce Machine Learning using MapReduce What is Machine Learning Machine learning is a subfield of artificial intelligence concerned with techniques that allow computers to improve their outputs based on previous

More information

Natural Language Processing

Natural Language Processing Natural Language Processing Information Retrieval Potsdam, 14 June 2012 Saeedeh Momtazi Information Systems Group based on the slides of the course book Outline 2 1 Introduction 2 Indexing Block Document

More information

Basic Tokenizing, Indexing, and Implementation of Vector-Space Retrieval

Basic Tokenizing, Indexing, and Implementation of Vector-Space Retrieval Basic Tokenizing, Indexing, and Implementation of Vector-Space Retrieval 1 Naïve Implementation Convert all documents in collection D to tf-idf weighted vectors, d j, for keyword vocabulary V. Convert

More information

Bruno Martins. 1 st Semester 2012/2013

Bruno Martins. 1 st Semester 2012/2013 Link Analysis Departamento de Engenharia Informática Instituto Superior Técnico 1 st Semester 2012/2013 Slides baseados nos slides oficiais do livro Mining the Web c Soumen Chakrabarti. Outline 1 2 3 4

More information

Clustering Results. Result List Example. Clustering Results. Information Retrieval

Clustering Results. Result List Example. Clustering Results. Information Retrieval Information Retrieval INFO 4300 / CS 4300! Presenting Results Clustering Clustering Results! Result lists often contain documents related to different aspects of the query topic! Clustering is used to

More information

Search Engines. Information Retrieval in Practice

Search Engines. Information Retrieval in Practice Search Engines Information Retrieval in Practice All slides Addison Wesley, 2008 Web Crawler Finds and downloads web pages automatically provides the collection for searching Web is huge and constantly

More information

Introduction to Text Mining. Hongning Wang

Introduction to Text Mining. Hongning Wang Introduction to Text Mining Hongning Wang CS@UVa Who Am I? Hongning Wang Assistant professor in CS@UVa since August 2014 Research areas Information retrieval Data mining Machine learning CS@UVa CS6501:

More information

DATA MINING II - 1DL460. Spring 2014"

DATA MINING II - 1DL460. Spring 2014 DATA MINING II - 1DL460 Spring 2014" A second course in data mining http://www.it.uu.se/edu/course/homepage/infoutv2/vt14 Kjell Orsborn Uppsala Database Laboratory Department of Information Technology,

More information

Name of the lecturer Doç. Dr. Selma Ayşe ÖZEL

Name of the lecturer Doç. Dr. Selma Ayşe ÖZEL Y.L. CENG-541 Information Retrieval Systems MASTER Doç. Dr. Selma Ayşe ÖZEL Information retrieval strategies: vector space model, probabilistic retrieval, language models, inference networks, extended

More information

Text Analytics (Text Mining)

Text Analytics (Text Mining) CSE 6242 / CX 4242 Text Analytics (Text Mining) Concepts and Algorithms Duen Horng (Polo) Chau Georgia Tech Some lectures are partly based on materials by Professors Guy Lebanon, Jeffrey Heer, John Stasko,

More information

Home Page. Title Page. Page 1 of 14. Go Back. Full Screen. Close. Quit

Home Page. Title Page. Page 1 of 14. Go Back. Full Screen. Close. Quit Page 1 of 14 Retrieving Information from the Web Database and Information Retrieval (IR) Systems both manage data! The data of an IR system is a collection of documents (or pages) User tasks: Browsing

More information

Multimedia Information Systems

Multimedia Information Systems Multimedia Information Systems Samson Cheung EE 639, Fall 2004 Lecture 6: Text Information Retrieval 1 Digital Video Library Meta-Data Meta-Data Similarity Similarity Search Search Analog Video Archive

More information

Information Retrieval. hussein suleman uct cs

Information Retrieval. hussein suleman uct cs Information Management Information Retrieval hussein suleman uct cs 303 2004 Introduction Information retrieval is the process of locating the most relevant information to satisfy a specific information

More information

VALLIAMMAI ENGINEERING COLLEGE. SRM Nagar, Kattankulathur DEPARTMENT OF COMPUTER SCIENCE ENGINEERING

VALLIAMMAI ENGINEERING COLLEGE. SRM Nagar, Kattankulathur DEPARTMENT OF COMPUTER SCIENCE ENGINEERING VALLIAMMAI ENGINEERING COLLEGE SRM Nagar, Kattankulathur-603 203 DEPARTMENT OF COMPUTER SCIENCE ENGINEERING EC6504 MICROPROCESSOR AND MICROCONTROLLER YEAR / SEMESTER: II / IV ACADEMIC YEAR: 2015-2016 (EVEN

More information

Information Retrieval

Information Retrieval Natural Language Processing SoSe 2014 Information Retrieval Dr. Mariana Neves June 18th, 2014 (based on the slides of Dr. Saeedeh Momtazi) Outline Introduction Indexing Block 2 Document Crawling Text Processing

More information

The Security Role for Content Analysis

The Security Role for Content Analysis The Security Role for Content Analysis Jim Nisbet Founder, Tablus, Inc. November 17, 2004 About Us Tablus is a 3 year old company that delivers solutions to provide visibility to sensitive information

More information

Automatic Summarization

Automatic Summarization Automatic Summarization CS 769 Guest Lecture Andrew B. Goldberg goldberg@cs.wisc.edu Department of Computer Sciences University of Wisconsin, Madison February 22, 2008 Andrew B. Goldberg (CS Dept) Summarization

More information

Information Retrieval. (M&S Ch 15)

Information Retrieval. (M&S Ch 15) Information Retrieval (M&S Ch 15) 1 Retrieval Models A retrieval model specifies the details of: Document representation Query representation Retrieval function Determines a notion of relevance. Notion

More information

University of Virginia Department of Computer Science. CS 4501: Information Retrieval Fall 2015

University of Virginia Department of Computer Science. CS 4501: Information Retrieval Fall 2015 University of Virginia Department of Computer Science CS 4501: Information Retrieval Fall 2015 5:00pm-6:15pm, Monday, October 26th Name: ComputingID: This is a closed book and closed notes exam. No electronic

More information

Chrome based Keyword Visualizer (under sparse text constraint) SANGHO SUH MOONSHIK KANG HOONHEE CHO

Chrome based Keyword Visualizer (under sparse text constraint) SANGHO SUH MOONSHIK KANG HOONHEE CHO Chrome based Keyword Visualizer (under sparse text constraint) SANGHO SUH MOONSHIK KANG HOONHEE CHO INDEX Proposal Recap Implementation Evaluation Future Works Proposal Recap Keyword Visualizer (chrome

More information

Classification and Clustering

Classification and Clustering Chapter 9 Classification and Clustering Classification and Clustering Classification/clustering are classical pattern recognition/ machine learning problems Classification, also referred to as categorization

More information

CSE 5243 INTRO. TO DATA MINING

CSE 5243 INTRO. TO DATA MINING CSE 5243 INTRO. TO DATA MINING Graph Data & Introduction to Information Retrieval Huan Sun, CSE@The Ohio State University 11/21/2017 Slides adapted from Prof. Srinivasan Parthasarathy @OSU 2 Chapter 4

More information

ΕΠΛ660. Ανάκτηση µε το µοντέλο διανυσµατικού χώρου

ΕΠΛ660. Ανάκτηση µε το µοντέλο διανυσµατικού χώρου Ανάκτηση µε το µοντέλο διανυσµατικού χώρου Σηµερινό ερώτηµα Typically we want to retrieve the top K docs (in the cosine ranking for the query) not totally order all docs in the corpus can we pick off docs

More information

Desktop Crawls. Document Feeds. Document Feeds. Information Retrieval

Desktop Crawls. Document Feeds. Document Feeds. Information Retrieval Information Retrieval INFO 4300 / CS 4300! Web crawlers Retrieving web pages Crawling the web» Desktop crawlers» Document feeds File conversion Storing the documents Removing noise Desktop Crawls! Used

More information

CLUSTERING, TIERED INDEXES AND TERM PROXIMITY WEIGHTING IN TEXT-BASED RETRIEVAL

CLUSTERING, TIERED INDEXES AND TERM PROXIMITY WEIGHTING IN TEXT-BASED RETRIEVAL STUDIA UNIV. BABEŞ BOLYAI, INFORMATICA, Volume LVII, Number 4, 2012 CLUSTERING, TIERED INDEXES AND TERM PROXIMITY WEIGHTING IN TEXT-BASED RETRIEVAL IOAN BADARINZA AND ADRIAN STERCA Abstract. In this paper

More information

Shrey Patel B.E. Computer Engineering, Gujarat Technological University, Ahmedabad, Gujarat, India

Shrey Patel B.E. Computer Engineering, Gujarat Technological University, Ahmedabad, Gujarat, India International Journal of Scientific Research in Computer Science, Engineering and Information Technology 2018 IJSRCSEIT Volume 3 Issue 3 ISSN : 2456-3307 Some Issues in Application of NLP to Intelligent

More information

Relevance Feedback and Query Reformulation. Lecture 10 CS 510 Information Retrieval on the Internet Thanks to Susan Price. Outline

Relevance Feedback and Query Reformulation. Lecture 10 CS 510 Information Retrieval on the Internet Thanks to Susan Price. Outline Relevance Feedback and Query Reformulation Lecture 10 CS 510 Information Retrieval on the Internet Thanks to Susan Price IR on the Internet, Spring 2010 1 Outline Query reformulation Sources of relevance

More information