1. Overview and Background
2. Evaluation of IR Systems

Note: Lecture videos corresponding to this material are posted on the D2L site for the course.

• Read Chapters 1 and 8 of the IR Book
• Read the articles:
  (1) Information Retrieval -  Wikipedia
  (2) Web Search Engine - Wikipedia
  (3) History of Search Engines - From 1945 to 2017

1. Indexing and Document Analysis
2. Content Analysis & Statistical Properties of Text

• Read Chapters 4 and 5 of the IR Book
• Read the Description of Porter's Stemming Algorithm

1. Indexing Implementation Issues
2. Indexing Models & Term Weighting

• Read Chapter 2 of the IR Book
• Here is good site where you can find descriptions of various string search algorithms along with animation and implementation details.

1. Retrieval Models and Ranking Systems
2. Implementation Notes on Vector Space Retrieval

• Read Chapters 6, 7, and 11 of the IR Book.
• Read the paper Information Retrieval on the World Wide Web by N. Gudivada, et. al.
• Read the paper The Anatomy of a Large-Scale Hypertextual Web Search Engine by Sergey Brin and Lawrence Page. [This is the original Google paper]

1. Query Operation and Word Representation
2. Relevance Feedback and Personalization I
3. Releveance Feedback and Personalization II

• Read Chapters 9 and 18 of the  IR Book
• Supplemental Reading:
  • Understanding User Goals in Web Search by Rose and Levinson, Yahoo! Inc.
  • Distributed Representations of Words and Phrases and their Compositionality (Mikolov et al., 2013)
  • GloVe: Global Vectors for Word Representation (Pennington et al., 2014)

1. Query Operation and Word Representation
2. Relevance Feedback and Personalization I
3. Releveance Feedback and Personalization II

• Read Chapters 9 and 18 of the  IR Book
• Supplemental Reading:
  • Understanding User Goals in Web Search by Rose and Levinson, Yahoo! Inc.
  • Distributed Representations of Words and Phrases and their Compositionality (Mikolov et al., 2013)
  • GloVe: Global Vectors for Word Representation (Pennington et al., 2014)

1. Text Categorization - Part 1 (KNN, Rocchio)
2. Text Categorization - Part 2 (Naïve Bayes)

• Read Chapter 13 and and 14 of the IR Book.
• Supplemental Reading:
  • Text Classification: A Comprehensive Guide
  • Machine Learning in Automated Text Categorization (Sebastiani, 2002)

• Read Chapters 19-21 of the IR Book.

1. Clustering Techniques; Clustering in IR

• Read Chapters 16 and 17 of the  IR Book.
• Supplemental Reading:
  • Supplemental Notes on Clustering (Based on Notes from Ed Fox's IR Course at Virginia Tech.)

Web IR: Current Trends and the Future of Search

Readings

• Read this 2021 paper from Google Research on emerging technologies in search: "Rethinking Search Making Domain Experts out of Dilettantes".
• Read: "Language models like GPT-3 could herald a new type of search engine" form MIT Tecnology Review, May 2021.

 

Final Projects Due - Wednesday. March 15, 11:59 PM