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Information retrieval and extraction
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. |