INLS 509:
Information Retrieval

Description: The field of information retrieval (IR) is concerned with the analysis, organization, storage, and retrieval of unstructured and semi-structured data. In this course, we will focus on mostly text. While IR systems are often associated with Web search engines (e.g., Google), IR applications also include digital library search, patent search, search for local businesses, and expert search, to name a few. Likewise, IR techniques (the underlying technology behind IR systems) are used to solve a wide range of problems, such as organizing documents into an ontology, recommending news stories to users, detecting spam, and predicting reading difficulty. This course will provide an overview of the theory, implementation, and evaluation of IR systems and IR techniques. In particular, we will explore how search engines work, how they "interpret" human language, what different users expect from them, how they are evaluated, why they sometimes fail, and how they might be improved in the future.
Prerequisites: There are no prerequisites for this course.
Expectations: Information retrieval is the study of computer-based solutions to a human problem. Thus, the first half of the course will be system-focused, while the second half will be user-focused. During the first half, you should expect to see some math (e.g., basic probability and statistics and some linear algebra). However, we will focus on the concepts rather than the details.

Students will have an opportunity to explore their interests with a open-ended literature review.

Time & Location: M, W 9:30-10:45 am, Manning 307
Instructor: Jaime Arguello (email, web)
Office Hours: T, Th 9:30-10:30 am, Manning 305
Required Textbook: Search Engines - Information Retrieval in Practice, W. B. Croft, D. Metzler, and T. Strohman. Cambridge University Press. 2009. Available at the bookstore.
Additional Resources: Foundations of Statistical Natural Language Processing. C. Manning and H Schutze. 1999.

Introduction to Information Retrieval. C. Manning, P. Raghavan and H. Schutze. 2008.
Other Readings: Selected papers and chapters from other books will sometimes be assigned for reading. These will be available online.
Course Policies: Laptops, Attendance, Participation, Collaboration, Plagiarism & Cheating, Late Policy
Grading: 30% homework (10% each)
15% midterm exam
15% final exam
30% literature review (5% proposal, 10% presentation, 15% paper)
10% participation
Grade Assignments: Letter grades will be assigned using the following scale: H 95-100%, P 80-94%, L 60-79%, and F 0-59%. All homework, exams, and the literature review will be graded on a curve.
Schedule: Subject to change! The required textbook (Croft, Metzler, and Strohman) is denoted as CMS below.
Lecture Date Events Topic Reading Due
1 Wed. 1/9 Introduction to IR: The Big Picture
2 Mon. 1/14 Course Overview: Roadmap and Expectations CMS Ch. 1
3 Wed. 1/16 Introduction To Ad-hoc Retrieval I CMS Ch. 2, 5.3.0-5.3.3, 7.1.0-7.1.1
4 Mon. 1/21 Martin Luther King Day (No Class) No Class
5 Wed. 1/23 Introduction To Ad-hoc Retrieval II
6 Mon. 1/28 HW1 Out Indexing and Query Processing
7 Wed. 1/30 Statistical Properties of Text CMS Ch. 4.1-4.2
8 Mon. 2/4 Text Representation I CMS Ch. 4.3-4.7
9 Wed. 2/6 Text Representation II
10 Mon. 2/11 HW1 Due Retrieval Models: Vector Space I CMS Ch. 7.1
11 Wed. 2/13 HW2 Out Retrieval Models: Vector Space II
12 Mon. 2/18 Retrieval Models: Query-likelihood I CMS Ch. 7.3
13 Wed. 2/20 Literature Review Proposal Due Retrieval Models: Query-likelihood II
14 Mon. 2/25 Document Priors
15 Wed. 2/27 HW2 Due Evaluation Overview CMS Ch. 8
16 Mon. 3/4 Midterm Review Midterm Review
17 Wed. 3/6 Midterm Exam Midterm Solutions
18 Mon. 3/11 Spring Break (No Class) No Class
19 Wed. 3/13 Spring Break (No Class) No Class
20 Mon. 3/18 Test Collection-based Evaluation Robertson '08
21 Wed. 3/20 Test Collection-based Evaluation
22 Mon. 3/25 HW3 Out Evaluation Metrics Hersh et al., '00, Turpin & Hersh '01
23 Wed. 3/27 Evaluation Metrics
24 Mon. 4/1 Relevance Saracevic '07
25 Wed. 4/3 User Studies in Information Retrieval Kelly '09 Chapter 10 (pgs. 99-125), Tombros et al., '05
26 Mon. 4/8 HW3 Due Search-log Analysis Joachims et al., '05
27 Wed. 4/10 Distributed Informaton Retrieval
28 Mon. 4/15 Aggregated Search
29 Wed. 4/17 Student Presentations
30 Mon. 4/22 Student Presentations
31 Wed. 4/24 Student Presentations
32 Mon. 4/29 Literature Review Due
33 Fri. 5/3 Final Exam 4-7pm