Information Retrieval detailed syllabus scheme for Information Technology (IT), 2020-21 onwards has been taken from the DBATU official website and presented for the Bachelor of Technology students. For Subject Code, Course Title, Lecutres, Tutorials, Practice, Credits, and other information, do visit full semester subjects post given below.
For 8th Sem Scheme of Information Technology (IT), 2020-21 Onwards, do visit IT 8th Sem Scheme, 2020-21 Onwards. For the Elective-XI scheme of 8th Sem 2020-21 onwards, refer to IT 8th Sem Elective-XI Scheme 2020-21 Onwards. The detail syllabus for information retrieval is as follows.
Information Retrieval Syllabus for Information Technology (IT) 4th Year 8th Sem 2020-21 DBATU
Course Objectives:
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Course Outcomes:
After learning the course the students should be able:
- To apply information retrieval principles to locate relevant information in large collections of data
- To understand and deploy efficient techniques for the indexing of document objects that are to be retrieved
- To implement features of retrieval systems for web-based and other search tasks
- To analyze the performance of retrieval systems using test collections
- To make practical recommendations about deploying information retrieval systems in different search domains, including considerations for document management and querying
Unit I
introduction to the course: information retrieval problem, first take at building an inverted index, processing of Boolean queries, extended Boolean model vs. ranked retrieval. Term vocabulary and postings lists: document delineation and character sequence decoding, determining vocabulary of terms, Faster postings list intersection via skip pointers, positional postings and phrase queries.
Unit II
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Unit III
Scoring and IR System Evaluation: parametric and zone indexes, term frequency and weighing, vector space model for scoring, variant tf-idf functions, efficient scoring and ranking, components of an IR system, vector space scoring and query operator interaction; IR system evaluation, Standard test collections, evaluation of unranked and ranked retrieval results, Assessing relevance, System quality and user utility; Relevance feedback and pseudo relevance feedback, Global methods for query reformulation.
Unit IV
XML and Probabilistic Information Retrieval: Basic concepts of XML retrieval and challenges, vector space model for XML retrieval, Text-centric vs. data centric XML retrieval; probability ranking principal, binary independence model, appraisal and some extensions; Language models for information retrieval, query likelihood model, language modeling vs. other approaches in IR.
Unit V
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Unit VI
Document Clustering and Matrix Decomposition: Flat clustering, cardinality, evaluation of clustering, K-means, Model based clustering; Hierarchical Agglomerative clustering, singlelink and complete-link clustering, Group-average agglomerative clustering, Centroid clustering, Optimality of HAC, Divisive clustering, Cluster labeling; Matrix decompositions, Term document matrices and singular value decomposition, Low-rank approximations, Latent semantic indexing. Web Search: basics concepts, web graph, spam, search user experience, Index size and estimation, Nearduplicates and shingling; Web crawling and indexes: overview, crawler architecture, DNS resolution, URL frontier, Distributing indexes and connectivity servers; Link analysis: Anchor text and web graph, Page Rank, Hubs and Authorities.
Text Books:
- Manning, C. D., Raghavan, P., and Schutze, H. “Introduction to Information Retrieval”. Cambridge University Press. 2008
- Witten, I. H., Moffat, A., and Bell, T. C. “Managing Gigabytes: Compressing and Indexing Documents and Images.”, Morgan Kaufmann. 1999
- Grossman, D. A., “Information Retrieval: Algorithms and Heuristics”, Springer. 2004
Reference Book:
- Baeza-Yates, R., and Ribeiro-Neto, B. “Modern information Retrieval”, New York: ACM press. 1999
- Belew, R. K. “Finding Out About: A Cognitive Perspective on Search Engine Technology and the WWW”, Cambridge University Press. 2000
- Chakrabarti, S. “Mining the Web: Discovering Knowledge from Hypertext Data”, Morgan Kaufmann. 2003
- Manning, C. D. “Foundations of Statistical Natural Language Processing”, H. Schutze (Ed.) MIT press. 1999
For detail syllabus of all subjects of Information Technology (IT) 8th Sem 2020-21 onwards, visit IT 8th Sem Subjects of 2020-21 Onwards.