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Information retrieval and extraction

Information retrieval and extraction

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Course Information

Course Information Retrieval and Extraction Code AIA1104
Directions 70610501 – Artificial Intelligence (Master) Semester 1
Type of subject Compulsory Taught Language English, Uzbek
Lectures 30 Practical Lessons 30
Subject Teacher PhD, Elmurod Quriyozov Independent Work 60
Total Hours 120 Credits 4

Lecture Topics

Code Topic Material
L1 Introduction to Information Retrieval Systems Download
L2 Basic Search Algorithms Download
L3 Text Preprocessing for Information Retrieval Download
L4 Keyword-Based Search Methods Download
L5 Boolean and Vector Space Models Download
L6 Document Indexing Techniques Download
L7 Ranking Algorithms for Search Results Download
L8 Natural Language Processing Basics for Information Retrieval Download
L9 Tokenization and Stemming Download
L10 Term Weighting with TF-IDF Download
L11 Machine Learning Foundations for Information Retrieval Download
L12 Semantic Search and Word Embeddings Download
L13 Evaluation of Information Retrieval Systems Download
L14 Information Extraction from Structured Data Download
L15 Modern Trends in Information Retrieval: AI-Based Search Systems Download

Practical Lesson Topics

Code Topic Material
P1 Exploring and Comparing Information Retrieval Systems Download
P2 Implementing Basic Search Algorithms Download
P3 Text Cleaning and Preprocessing for Retrieval Tasks Download
P4 Building a Keyword-Based Search Engine Download
P5 Implementing Boolean and Vector Space Retrieval Models Download
P6 Building Inverted Indexes for Document Collections Download
P7 Implementing Ranking Algorithms (TF-IDF, BM25) Download
P8 Applying NLP Techniques in Information Retrieval Download
P9 Tokenization and Stemming Techniques in Practice Download
P10 Computing and Applying TF-IDF Weights Download
P11 Applying Machine Learning Models for Retrieval Tasks Download
P12 Implementing Semantic Search with Word Embeddings Download
P13 Evaluating Information Retrieval Systems Download
P14 Extracting Information from Structured Data (XML, JSON) Download
P15 Developing an AI-Based Search Prototype Download

Independent Study Topics

No. Topic
1 Survey of Modern Search Engine Architectures
2 Comparative Analysis of Classical Information Retrieval Models
3 Probabilistic and Language Models for Information Retrieval
4 Evaluation Metrics and Benchmarking in Information Retrieval
5 Query Expansion and Relevance Feedback Methods
6 Semantic Search and Context-Aware Retrieval
7 Neural and Dense Retrieval Models
8 Information Extraction from Unstructured Text
9 Multilingual and Cross-Lingual Information Retrieval
10 Bias, Fairness, and Ethical Issues in Search Systems
11 Knowledge Graphs and Information Retrieval
12 Domain-Specific Information Retrieval Systems
13 Scalability and Efficiency in Large-Scale Retrieval Systems
14 Integration of Information Retrieval and Question Answering Systems
15 Recent Research Trends in AI-Based Information Retrieval

Main References

No. Reference
1 Manning, C. D., Raghavan, P., & Schütze, H. Introduction to Information Retrieval. Cambridge University Press, 2008.
2 Baeza-Yates, R., & Ribeiro-Neto, B. Modern Information Retrieval. Addison-Wesley, 2011.
3 Croft, W. B., Metzler, D., & Strohman, T. Search Engines: Information Retrieval in Practice. Pearson, 2010.
4 Jurafsky, D., & Martin, J. H. Speech and Language Processing. 3rd ed., Prentice Hall, 2023.
5 Salton, G., Wong, A., & Yang, C. S. A Vector Space Model for Automatic Indexing. Communications of the ACM, 1975.
6 Robertson, S., & Zaragoza, H. The Probabilistic Relevance Framework: BM25 and Beyond. Foundations and Trends in Information Retrieval, 2009.
7 Zhai, C., & Lafferty, J. A Study of Smoothing Methods for Language Models Applied to Information Retrieval. ACM TOIS, 2004.
8 Mitra, B., & Craswell, N. An Introduction to Neural Information Retrieval. Foundations and Trends in Information Retrieval, 2018.
9 Mikolov, T., et al. Efficient Estimation of Word Representations in Vector Space. ICLR, 2013.
10 Büttcher, S., Clarke, C. L. A., & Cormack, G. V. Information Retrieval: Implementing and Evaluating Search Engines. MIT Press, 2016.

Additional References

No. Reference
1 Stanford University – CS276: Information Retrieval and Web Search (Course Materials).
2 University of Illinois – Information Retrieval Specialization (Coursera).
3 Apache Lucene Official Documentation.
4 Elasticsearch Official Documentation and Tutorials.
5 ACL Anthology – Digital Library of NLP and Information Retrieval Research.
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