Information Retrieval(IR) Syllabus

This page contains Syllabus of Information Retrieval of CSIT.

Title Information Retrieval
Short Name IR
Course code CSC413
Nature of course Theory + Lab
Seventh Semester
Full marks 60 + 20 + 20
Pass marks 24 + 8 + 8
Credit Hrs 3
Elective/Compulsary Elective

Course Description

Course Description:

This course familiarizes students with different concepts of information retrieval techniques mainly focused on clustering, classification, search engine, ranking and query operations techniques.

Course Objective:

The main objective of this course is to provide knowledge of different information retrieval techniques so that the students will be able to develop information retrieval engine.

Units and Unit Content

1. Introduction to IR and Web Search
teaching hours: 2 hrs

Introduction, Data vs Information Retrieval, Logical view of the documents, Architecture of IR

System, Web search system, History of IR, Related areas

2. Text properties, operations and preprocessing
teaching hours: 5 hrs

Tokenization, Text Normalization, Stop-word removal, Morphological Analysis, Word Stemming (Porter Algorithm), Case folding, Lemmatization, Word statistics (Zipf's law, Heaps’ Law), Index term selection, Inverted indices, Positional Inverted index, Natural Language Processing in Information Retrieval, Basic NLP tasks – POS tagging; shallow parsing

3. Basic IR Models
teaching hours: 5 hrs

Classes of Retrieval Model, Boolean model, Term weighting mechanism – TF, IDF, TF-IDF weighting, Cosine Similarity, Vector space model , Probabilistic models (the binary independence model ,Language models; · KL-divergence; · Smoothing), Non-Overlapping Lists, Proximal Nodes Mode

4. Evaluation of IR
teaching hours: 2 hrs

Precision, Recall, F-Measure, MAP (Mean Average Precision), (DCG) Discounted Cumulative Gain, Known-item Search Evaluation

5. Query Operations and Languages
teaching hours: 4 hrs

Relevance feedback and pseudo relevance feedback, Query expansion (with a thesaurus or WordNet and correlation matrix), Spelling correction (Edit distance, K – Gram indexes, Context sensitive spelling correction), Query languages (Single-Word Queries, Context Queries, Boolean Queries, Structural Query, Natural Language)

6. Web Search
teaching hours: 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 (HITS, Page ranking), Query log analysis, Handling “invisible” Web – Snippet generation, CLIR (Cross Language Information Retrieval)

7. Text Categorization
teaching hours: 4 hrs

Categorization, Learning for Categorization, General learning issues, Learning algorithms: Bayesian (naïve), Decision tree, KNN, Rocchio)

8. Text Clustering
teaching hours: 4 hrs

Clustering, Clustering algorithms (Hierarchical clustering, k-means, k-medoid, Expectation maximization (EM), Text shingling)

9. Recommender System
teaching hours: 3 hrs

Personalization, Collaborative filtering recommendation, Content-based recommendation

10. Question Answering
teaching hours: 5 hrs

Information bottleneck, Information Extraction, Ambiguities in IE, Architecture of QA system, Question processing, Paragraph retrieval, Answer processing

11. Advanced IR Models
teaching hours: 5 hrs

Latent Semantic Indexing (LSI), Singular value decomposition, Latent Dirichlet Allocation, Efficient string searching, Knuth – Morris – Pratt, Boyer – Moore Family, Pattern matching

Lab and Practical works

Laboratory Works:

The laboratory should contain all the features mentioned in a course. The Laboratory work should contain at least following tasks

  1. Program to demonstrate the Boolean Retrieval Model and Vector Space Model
  2. Tokenize the words of large documents according to type and token
  3. Program to find the similarity between documents
  4. Implement Porter stemmer
  5. Build a spider that tracks only the link of nepali documents
  6. Group the online news onto different categorize like sports, entertainment, politics
  7. Build a recommender system for online music store