course overview: an introduction to information retrieval and applications

Course Overview: An Introduction to Information

Retrieval and Applications

J. H. WangFeb. 26, 2015

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Instructor & TA

• Instructor– J. H. Wang (王正豪 )– Associate Professor, CSIE, NTUT– Office: R1534, Technology Building– E-mail: jhwang@csie.ntut.edu.tw– Tel: ext. 4238– Office Hour: 9:00-12:00 am, every Tuesday and

Thursday

• TA– Mr. Tsai (@R1424, Technology Building)

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Course Description• Course Web Page: for the latest announcements

and updates of schedule, slides, and homeworks– http://www.ntut.edu.tw/~jhwang/IR/

• Time: 9:10-12:00am, Mon.• Classroom: R1322, Technology Building• Textbook:

– Christopher D. Manning, Prabhakar Raghavan and Hinrich Schuetze, Introduction to Information Retrieval, Cambridge University Press, 2008. (Available online)

• International Student Edition, imported by Kai-Fa (開發 ) Publishing

• Prerequisites: – Basic knowledge of data structures and algorithms, linear

algebra, and probability theory – Programming experience is *required* for homeworks &

projects

Target Audience

• CSIE Seniors and graduate students• IGPEECS (International Graduate

Program in Electrical Engineering and Computer Science)

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Additional References

• References: – Ricardo Baeza-Yates and Berthier Ribeiro-Neto,

Modern Information Retrieval: The Concepts and Technology behind Search, Addison-Wesley, 2011.

• This is the second edition of their book Modern Information Retrieval in 1999. (華通 )

– Bruce Croft, Donald Metzler, and Trevor Strohman, Search Engines: Information Retrieval in Practice, Addison-Wesley, 2010. (全華 )

– Stefan Buettcher, Charles L.A. Clarke, and Gordon V. Cormack, Information Retrieval: Implementing and Evaluating Search Engines, MIT Press, 2010.

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More Books on IR• Gerald Salton, Automatic information organization

and retrieval, McGraw-Hill, 1968.• Gerald Salton and M.J. McGill, Introduction to

modern information retrieval, McGraw-Hill, 1983.– Two classics, but out-of-print.

• C. J. van Rijsbergen, Information Retrieval, Butterworths, 1979. – The classic. More than 40 years old, but still worth

reading. • K. Sparck Jones, P. Willett, Readings in Information

Retrieval, Morgan Kaufmann, 1997. – A collection of classical IR papers. (out of print)

• I.H. Witten, A. Moffat, T.C. Bell. Morgan Kaufmann, Managing Gigabytes, 2nd edition, 1999. – The authority on index construction and compression.

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Grading Policy

• Homework assignments and programming exercises: ~40%

• Mid-term exam: ~25%• Term project: ~35%

– Including proposal, presentation, and final report

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System Exercises and Term Project

• About 2-3 team-based system exercises– Maximum number of students per team:

• 4 for undergraduates• 2 for graduate students

– You can either write your own code or reuse existing open source code (to be detailed later)

• The term project– Either team-based system development (e.g.

extension to exercises)– Or academic paper presentation

• Only one person per team allowed– A proposal is *required* one week after midterm

(May 4, 2015)

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About the Term Project

• The score you’ll get depends on the functions, difficulty and quality of your project – For system development:

• System functions and correctness

– For academic paper presentation• Quality and your presentation of the paper• Major methods/experimental results *must* be presented• Papers from top conferences are strongly suggested

– E.g. SIGIR, WWW, CIKM, WSDM, JCDL, ICMR, …

• Proposals are *required* for each team, and will be counted in the score

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Online Submission

• Submission instructions– Systems, programs, project proposals,

and project reports in electronic files must be submitted to the TA online at:• Submissions website & instructions : (To be

announced)

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What this Course is NOT about

• This course will NOT tell you– The tips and tricks of using search engines,

although power users might have better ideas on how to improve them

• There’re plenty of books and websites on that…

– How to find books in libraries, although it’s somewhat related to the basic IR concepts

– How to make money on the Web, although the currently largest search engine did it

What’s Information Retrieval?

• Things that you have been doing everyday!– Searching for something interesting: Web, news,

tweets, e-mails, images, videos, …– Asking for advices: shopping, restaurants,

movies, …– …

• User interests are changing all the time…– 2011: New Zealand Earthquake– 2012: Jeremy Lin– 2013: Meteor Russia– 2014: Ukraine riots– 2015: ?

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What’s Information Retrieval

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In Google News

In Web Pages

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In Wikipedia

In Google Images

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More details

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Related Keywords

• TransAsia Airways• Flight 235 (GE235)• ATR 72-500, turboprop• Keelung River• Songshan, kinmen• Mayday, engine flameout• TransAsia Airways Flight 235• …

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Related Keywords in Chinese

• 復興航空• 墜毀• 基隆河• 台北松山 , 金門• 引擎熄火• 復興航空 235號班機空難• …• And this can go on:

– for other languages…– and other search engines…– and social websites…

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In Google Trends

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Similar Events

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And Social Search…

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On Facebook

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How do I Know What People Care about?

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What Is Information Retrieval?

• “Information retrieval is a field concerned with the structure, analysis, organization, storage, searching, and retrieval of information.” (Salton, 1968)

• Information vs. data

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Goal

• Information retrieval (IR): a research field that targets at effectively and efficiently searching information in text and multimedia documents

• In this course, we will introduce the basic text and query models in IR, retrieval evaluation, indexing and searching, and applications for IR

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A Big Picture

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Inverted Index

UserInterface

Text Operations

Query Expansion IndexingIndexing

RetrievalRetrieval

RankingRanking

Text

query

user need

user feedback

ranked docs

retrieved docs

Doc representationlogical view

inverted file

Document Collection

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Topics

• Text IR– Indexing and searching– Query languages and operations

• Retrieval evaluation• Modeling

– Boolean model– Vector space model– Probabilistic model

• Applications for IR– Multimedia IR– Web search– Digital libraries

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Organization of the Textbook

• Basics in IR (focus)– Inverted indexes for boolean queries (Ch.1-5)– Term weighting and vector space model (Ch. 6-7)– Evaluation in IR (Ch. 8)

• Advanced Topics– Relevance feedback (Ch. 9)– XML retrieval (Ch. 10)– Probabilistic IR (Ch. 11)– Language models (Ch. 12)

• Machine learning in IR (useful)– Text classification (Ch. 13-15)– Document clustering (Ch. 16-18)

• Web Search– Web crawling and indexes (Ch. 19-20)– Link analysis (Ch. 21)

Some Overlap with Other Fields

• Data mining, Text mining, Information Extraction

• Machine Learning• Natural Language Processing• Social Network Analysis• …

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Pointers to Other Topics

• Cross-language IR• Image, video, and multimedia IR• Speech retrieval• Music retrieval• User interfaces• Parallel, distributed, and P2P IR• Digital libraries• Information science perspective• Logic-based approaches to IR• Natural language processing techniques• …

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Tentative Schedule

• Before midterm– Boolean retrieval (1 wk)– Indexing (2 wks)– Vector space model and evaluation (2 wk)– Relevance feedback (1 wk)– Probabilistic IR (2 wk)

• After midterm – Text classification (1-2 wk)– Document clustering (1-2 wk)– Web search (2 wks)– Advanced topics: CLIR, IE, … (2 wks)– Term Project Presentation (3 wks)

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Generic Resources

• Wikipedia page on Information Retrieval: http://en.wikipedia.org/wiki/Information_retrieval

• Information Retrieval Resources: http://www-csli.stanford.edu/~hinrich/information-retrieval.html

•

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Academic Resources• Journals

– ACM TOIS: Transactions on Information Systems – JASIST: Journal of the American Society of Information

Sciences– IP&M: Information Processing and Management– IEEE TKDE: Transactions on Knowledge and Data Engineering

• Conferences– ACM SIGIR: International Conference on Information Retrieval– WWW: World Wide Web Conference– ACM CIKM: Conference on Information Knowledge and

Management– JCDL: ACM/IEEE Joint Conference on Digital Libraries– ACM WSDM: International Conference on Web Search and

Data Mining– TREC: Text Retrieval Conference

Teaching in English…

• Slides and lectures will be offered mainly in English

• For better understanding for domestic students, important concepts will be briefly summarized in Chinese

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More on Term Projects

• Options for term projects– Option 1: team-based system project

• i.e., extension to system exercises

– Option 2: academic paper presentation• Only one person, NOT team-based

• Tentative schedule for all teams:– Proposal: *required* one week after midterm (May 4,

2015)– Presentations (including demos): *required* in the last

three weeks (starting from Jun. 8 or 15, 2015)– Final report: before the end of the semester (Jun. 30,

2015)• Slides, source code, documentation

For System Development

• You can write your own code in any programming language

• Or you can reuse existing open-source information retrieval tools

• Any topic relevant to information retrieval– Retrieval, analysis, extraction of entities,

topics, or their relations from various resources from the documents, Web, social media

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Some Open Source Tools

• Apache Lucene/Solr (in Java)– for indexing/search engine

• Apache Hadoop, Spark (in Java, Scala, Python)– For distributed computing and data analysis

• The Lemur Project, Indri, Galago – by CMU/Umass, (in C++)– For search engine, text analysis

• Terrier – by U. Glasgow (in Java)– For search engine

• …• You are encouraged to explore more!

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Thanks for Your Attention!

• Any question or comment? Please feel free to send e-mails to jhwang@csie.ntut.edu.tw or discuss with me at my office