605.744: Information Retrieval, Spring 2011

Lecturer

Paul McNamee

Textbooks

• Introduction to Information Retrieval by Manning, Raghavan, and Schütze, Cambridge University Press, 2008. An online version of the text is available in digital form at the companion web site.

Course Times and Location

• Lecture: Monday, 7:20pm - 10:00pm, Room K-2.
• Email should be the primary means of out-of-class communication; however I can meet with students by appointment.

Course Overview

This course covers the storage and retrieval of unstructured digital information. Topics include automatic index construction, retrieval models, textual representations, efficiency issues, search engines, text classification, and multilingual retrieval.

Grading Policy

Grades will be given based on the A,B,C, etc..., scale, per university policy. Work for the class will include homework assignments, an independent research project, exams, and classroom participation (e.g., quizzes, oral presentations, paper summaries). Refer to the course outline for details.

Academic Integrity

Work for this class is expected to be the result of individual effort; however, unless explicitly prohibited, it is perfectly acceptable to make use of published examples and source code from the literature or public domain - but only if attribution is given. Furthermore, while it is permissible to discuss the general nature of lecture material and assignments with your peers, this does not extend to discussing or revealing solutions or source code. Students are expected to uphold the academic integrity of the university. Students using without reference, published material or copying the work (i.e., particularly source code) of another individual will face consequences such as receiving a zero on the assignment and having the matter referred to the dean. Contact me if you have any questions, no matter how slight, about this policy, or if you have questions about a particular assignment.

Assigned Readings

• 1/31/11 Chapters 1 and 2 in Manning, Raghavan, and Schütze.
• Michael Lesk, The Seven Ages of Information Retrieval
• 2/7/11 Chapters 3 and 4 in Manning, Raghavan, and Schütze.
• 2/14/11 Chapters 5 - 7 in Manning, Raghavan, and Schütze.
• 2/21/11 Chapters 8 and 9 in Manning, Raghavan, and Schütze.
• Economic Impact of TREC. Executive Summary and Chapters 1-3
• 2/28/11 Chapters 11 and 12 in Manning, Raghavan, and Schütze.
• 3/7/11 Chapters 13, 14, and 15 in Manning, Raghavan, and Schütze.
• 3/7/11 Thorsten Joachims. Text categorization with support vector machines: learning with many relevant features.
• 3/7/11 Optional. Goodman et al., Spam and the on-going battle for the inbox., CACM 50(2), pp. 24-33, 2007.
• 3/14/11 Chapters 19, 20, and 21 in Manning, Raghavan, and Schütze.
• 4/4/11 K. Kashida, Technical issues of cross-language information retrieval: a review, IPM 41, pp. 433-455, 2005.
• 4/18/11 M. Sanderson, Retrieving with Good Sense, Information Retrieval 2(1), pp. 49-69, 2000.

Handouts

• 1/31/11 Course Description and tentative outline
• 1/31/11 Lecture notes for week 1. Introduction. Boolean Retrieval.
• 2/7/11 Lecture notes for week 2. Tolerant Retrieval. Index Construction.
• 2/14/11 Lecture notes for week 3. Efficiency. Vector models.
• 2/21/11 Lecture notes for week 4. Evaluation. Relevance Feedback.
• 2/28/11 Lecture notes for week 5. Binary Independence Model and Statistical Language Models.
• 3/7/11 Lecture notes for week 6. Text classification.
• 3/14/11 Lecture notes for week 7. Web IR.
• 3/14/11 Practice exam distributed in class (Email me if you didn't get it).
• 4/4/11 Lecture notes for week 9. Multilingual IR.
• 4/11/11 Lecture notes for week 10. Distributed IR and Multimedia Retrieval.
• 4/18/11 Lecture notes for week 11. NLP and IR
• 4/25/11 Lecture notes for week 12. Information Extraction and Question Answering

Assignments

• 1/31/11 HW#1: Corpus Statistics. Data : bible.txt sense.txt
• 2/7/11 HW#2: Index Construction. Data : bible.txt (same data as in HW#1)
• 2/21/11 HW#3: Query Processing. Data: time.txt and query file.
• 2/28/11 Class Project. Note proposals due by 3/21/11
• Reminder: You only need turn in any 3 of the last 4 assignments
• 3/7/11 HW#4: Text Classification. Data: coffee.train.txt coffee.test.txt ship.train.txt ship.test.txt wheat.train.txt wheat.test.txt
• 3/14/11 HW#5: Web IR. Data: Excite query log from 12/20/1999. Note the file is compressed using bzip2 and is about 37 MB compressed. The file contains around 2.5 million queries. Uncompressed the file is about 118 MB in size. (I consider this a relatively 'fun' assignment - data analysis on a messy, quirky Web query log.)
• 4/4/11 HW#6: Multilinguality. Data: English train English test Spanish train Spanish test French train French test
• 4/18/11 HW#7: Natural Language Processing and IR (Due 5/9/11)

Course related web-links

• Sources for on-line papers: CiteSeer ACL Anthology TREC Publications ACM Digital Library
• IR Textbooks: Information Retrieval: Implementingn and Evaluating Search Engines, Managing Gigabytes, Information Retrieval: Algorithms and Heuristics Readings in Information Retrieval (Amazon), Foundations of Statistical Natural Language Processing
• IR Evaluations: TREC, CLEF, NTCIR, FIRE ROMIP (a Russian language evaluation)
• Organizations that distribute corpora: LDC, ELRA
• IR Journals: JASIST, IP&M, IR ACM Transactions on Speech and Language Processing
• IR-related conferences: SIGIR, CIKM 2010, KDD, ACL 2010 WWW-2010 CEAS 2010 (email spam) AIRWeb (web spam) ISMIR (music IR)
• Blogs Probably Irrelevant, NLPers
• On-line magazines: The Noisy Channel, Search Engine Watch, D-Lib Magazine
• Peter Norvig's tutorial on spelling correction.
• Berkeley Primer: Finding Information on the Internet
• HLT Central Repository
• Discrete Mathematics Primer
• Web Protocols: HTML, Z39.50 (Information Retrieval)

Software Resources

• Lucene: a popular open-source search engine software
• Wumpus system
• Lemur / Indri: a language modelling IR toolkit
• Cornell's SMART system
• Managing Gigabytes mg system
• Very nice list of NLP, IR, CL, resources (i.e. parsers, taggers) at Stanford.
• University of Michigan tool suite: Clairlib
• Trigrams-n-Text (TnT) toolkit, a visible markov model tagger written by Thorsten Brants (now of Google).
• QTag a probabilistic POS-tagger.
• On-line translators: Systran, FreeTranslation.com
• Google's on-line translation service: Google Translate
• WordNet, a lexical database for English
• Andrew McCallum's MALLET toolkit, a Java-based API for machine learning applications using Conditional Random Fields
• Wget
• Perl LWP library (at CPAN).
• Public Domain Speech Recognition Software (from Miss State Univ).
• Machine Learning / Data Mining tool: WEKA
• Joachim's Support Vector Machine toolkit: SVMlight
• SVM-Multiclass, a multi-class version of SVMlight.
• Python-based set of tools for NLP tasks (parsing, POS tagging, etc...): NLTK

Cool Demos

• A 'meta' search engine: Dogpile
• A question-answering system: START
• An online joke recommendation system that demonstrates collaborative filtering: JESTER
• A faux computer science paper generator, SCIgen, from MIT
• No IR system with 3 billion queries a day is going to be perfect. Best of Google Bloopers ;-).

IR Test collections

• Reuters 21578
• CACM collection
• The University of Glasgow has archived a set of older test collections

Web Search Engines

• All The Web
• AltaVista
• Bing
• Google (an exemplary engine)
• Yahoo
• A lengthy list of alternative engines

JHU Links

• The Johns Hopkins University's Center for Language and Speech Processing. During the Spring and Fall semesters CLSP runs a weekly seminar series which meets Tuesday afternoons at 4:30 on the Homewood campus. You can see the schedule here.
• Human Language Technology Center of Excellence
• The Whiting school's part-time program home page.

Regional Links

• The University of Maryland's computational linguistics seminar series
• DIRE (DC-area Information Retrieval Experts) - a regional meeting of IR practitioners which meets once or twice a year.