Lecture 1: Overview of IR

1. Lecture 1: Overview of IR Maya Ramanath

2. Who hasn’t used Google? • Why did Google return these results first ? • Can we improve on it? • Is this a good result for the query “mayaramanath”? • OR: How good is Google?

3. Lectures • Overview (this lecture) • Retrieval Models • Retrieval Evaluation • Why DB and IR?

4. Information Retrieval • “An information retrieval system does not inform (i.e. change the knowledge of) the user on the subject of his inquiry. It merely informs on the existence (or non-existence) and whereabouts of documents relating to his request.” • “Information retrieval (IR) is finding material (usually documents) of an unstructured nature (usually text) that satisfies an information need from within large collections (usually stored on computers).”

5. Basic Terms

6. What is a retrieval system? Source: Hiemstra, D. (2009) Information Retrieval Models, in Information Retrieval: Searching in the 21st Century (eds A. Göker and J. Davies), John Wiley & Sons, Ltd, Chichester, UK.

7. Retrieval Models Source and Further Reading: Hiemstra, D. (2009) Information Retrieval Models, in Information Retrieval: Searching in the 21st Century (eds A. Göker and J. Davies), John Wiley & Sons, Ltd, Chichester, UK.

8. 2 kinds of models • No Ranking • Boolean models • Region models • Ranking • Vector space model • Probabilistic models • Language models

9. Boolean Model • Based on set theory • Simple query language Ex: information AND (retrieval OR management) information retrieval management

10. Vector Space Model (1/2) • Based on the notion of “similarity” between query and document • Query is the representation of the document that you want to retrieve • Compare similarity between query and document • Luhn’s formulation: The more two representations agreed in given elements and their distribution, the higher would be the probability of their representing similar information.

11. Vector Space Model (2/2) Document Query We will study more in the next lecture

12. Probabilistic IR (1/2) • Based on probability theory • Specifically, we would like to estimate the probability of relevance The Probability Ranking Principle If a reference retrieval system’s response to each request is a ranking of the documents in the collections in order of decreasing probability of usefulness to the user who submitted the request, where the probabilities are estimated as accurately as possible on the basis of whatever data has been made available to the system for this purpose, then the overall effectiveness of the system to its users will be the best that is obtainable on the basis of that data.

13. Probabilistic IR (2/2) Ranking of documents based on Odds We will study more in the next lecture

14. Language Models (1/3) • Based on generative models for documents and queries • Documents, Query: Samples of an underlying probabilistic process • Estimate the parameters of this process • Measure how close the distributions are (KL-divergence) • “Closeness” gives a measure of relevance

15. Language Models (2/3) Documents d1 Query q d2

16. Language Models (3/3) The Maximum Likelihood Estimator + smoothing • We will study more in the next lecture

17. Evaluation (Which system is best?)

18. Benchmarking IR Systems (1/2) • Why do we need to benchmark? • To benchmark an IR system • Efficiency • Quality • Results • Power of interface • Ease of use, etc.

19. Benchmarking IR Systems (2/2) Result Quality • Data Collection • Ex: Archives of the NYTimes • Query set • Provided by experts, identified from real search logs, etc. • Relevance judgements • For a given query, is the document relevant?

20. Precision, Recall, F-Measure • Precision • Recall • F-Measure: Weighted harmonic mean of Precision and Recall

21. That’s it for today!