Information Retrieval – Syllabus | Seventh Semester | BSc.CSIT (TU)

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information retrieval syllabusCourse Syllabus | BSc.CSIT
Information Retrieval
Seventh Semester | Fourth Year
Tribhuvan University (TU)

Course Title: Information Retrieval
Course no: CSC-405
Credit hours: 3
Full Marks: 60+20+20 | Pass Marks: 24+8+8
Nature of course: Theory (3 Hrs.) + Lab (3 Hrs.)
Course Synopsis: Advanced aspects of Information Retrieval and Search Engine
Goal: To study advance aspects of information retrieval and working principle of search engine, encompassing the principles, research results and commercial application of the current technologies.

Course Contents:
Unit 1 Introduction: [2 Hrs.]
Introduction, History of Information Retrieval, The retrieval process, Block diagram and architecture
of IR System, Web search and IR, Areas and role of AI for IR

Unit 2. Basic IR Models: [4 Hrs.]
Introduction, Taxonomy of information retrieval models, Document retrieval and ranking, A formal
characterization of IR models, Boolean retrieval model, Vector-space retrieval model, probabilistic
model, Text-similarity metrics: TF-IDF (term frequency/inverse document frequency) weighting and
cosine similarity.

Unit 3. Basic Tokenizing, Indexing, and Implementation of Vector-Space Retrieval: [4 Hrs.]
Simple tokenizing, Word tokenization, Text Normalization, Stop-word removal, Word Stemming (Porter Algorithm), Case folding, Lemmatization, Inverted indices (Indexing architecture), Efficient processing with sparse vectors, Sentence segmentation and Decision Trees

Unit 4. Experimental Evaluation of IR: [4 Hrs.]
Relevance and Retrieval, performance metrics, Basic Measures of text retrieval (Recall, Precision and
F-measure)

Unit 5. Query Operations and Languages: [3 Hrs.]
Relevance feedback and pseudo relevance feedback, Query expansion/reformulation (with a thesaurus
or WordNet, Spelling correction like techniques), Query languages (Single-Word Queries, Context
Queries, Boolean Queries, Natural Language)

Unit 6. Text Representation: [3 Hrs.]
Word statistics (Zipf’s law), Morphological analysis, Index term selection, Using thesauri, Metadata,
Text representation using markup languages (SGML, HTML, XML)

Unit 7. Search Engine: [6 Hrs.]
Search engines (working principle), Spidering (Structure of a spider, Simple spidering algorithm,
multithreaded spidering, Bot), Directed spidering(Topic directed, Link directed) ,Crawlers (Basic
crawler architecture), Link analysis (e.g. hubs and authorities, Page ranking, Google Page Rank),
shopping agents

Unit 8. Text Categorization and Clustering: [6 Hrs.]
Categorization algorithms (Rocchio; naive Bayes; decision trees; and nearest neighbor), Clustering
algorithms (agglomerative clustering; k-means; expectation maximization (EM)) ,Applications to
information filtering; organization

Unit 9. Recommender Systems: [3 Hrs.]
Personalization, Collaborative filtering recommendation, Content-based recommendation

Unit 10. Information Extraction and Integration: [3 Hrs.]
Information extraction and applications, Extracting data from text, Evaluating IE Accuracy, XML and
Information Extraction, Semantic web (purpose, Relation to hypertext page), Collecting and integrating specialized information on the web.

Unit 11. Advanced IR Models with indexing and searching text: [4 Hrs.]
Probabilistic models, Generalized Vector Space Model, Latent Semantic Indexing (LSI), Efficient string searching, Pattern matching

Unit 12. Multimedia IR [3 Hrs.]
Introduction, multimedia data support in commercial DBMSs, Query languages, Trends and research
issues

Laboratory Works: The laboratory should contain all the features mentioned in a course
Samples
1. Program to demonstrate the Boolean Retrieval Model and Vector Space Model
2. Program to find the similarity between documents
3. Tokenize the words of large documents according to type and token.
4. Segment the documents according to sentences
5. Implement Porter stemmer
6. Try to build a stemmer for Nepali language
7. Build a spider that tracks only the link of nepali documents
8. Group the online news onto different categorize like sports, entertainment, politics
9. Build a recommender system for online music store

Reference Books:
1. Modern Information Retrieval, Ricardo Baeza-Yates, Berthier Ribeiro-Neto.
2. Information Retrieval; Data Structures & Algorithms: Bill Frakes

Homework
Assignment: Assignment should be given from the throughout the semester.
Computer Usage: No specific
Prerequisite: Server side programming language like PHP, JSP, ASP.Net (Any One) and with good concept on any programming languages
Category Content: Science Aspect: 25%, Design Aspect: 75%

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