CS798: Information Retrieval

Charlie Clarkeclaclark@plg.uwaterloo.ca

Information retrieval is concerned with representing, searching, and manipulating large collections of human-language data.

Housekeeping

Web page: http://plg.uwaterloo.ca/~claclark/cs798

Area: “Applications/Databases”

Meeting times: Mondays, 2:00-5:00, MC2036

NLP DB

ML

IR

Topics

1. Basic techniques

2. Searching, browsing, ranking, retrieval

3. Indexing algorithms and data structures

4. Evaluation

5. Application areas

1. Basic Techniques

• Text representation & Tokenization

• Inverted indices

• Phrase searching example

• Vector space model

• Boolean retrieval

• Simple proximity ranking

• Test collections & Evaluation

2. Retrieval and Ranking

• Probabilistic retrieval and Okapi BM25F

• Language modeling

• Divergence from randomness

• Passage retrieval

• Classification

• Learning to rank

• Implicit user feedback

3. Indexing

• Algorithms and data structures

• Index creation

• Dynamic update

• Index compression

• Query processing

• Query optimization

4. Evaluation

• Statistical foundations of evaluation

• Measuring Efficiency

• Measuring Effectiveness– Recall/Precision– NDCG – Other measures

• Building a test collection

5. Application Areas

• Parallel retrieval architectures

• Web search (Link analysis/Pagerank)

• XML retrieval

• Filesystem search

• Spam filtering

Other Topics (student projects)

• Image/video/speech retrieval

• Web spam

• Cross- and multi-lingual IR

• Clustering

• Advertising/Recommendation

• Distributed IR/Meta-search

• Question answering

• etc.

Resources

Textbook (partial draft on Website):

Büttcher, Clarke & Cormack. Information Retrieval: Data Structures, Algorithms and Evaluation.

(start reading ch. 1-3)

Wumpus:

www.wumpus-search.org

Grading

• Short homework exercises from text (10%)

• A literature review based on a topic area selected by the student with the agreement of the instructor (30%)

• 30-minute presentation on your selected topic (20%)

• Class project (40%) – details coming up..

“Documents”

• Documents are the basic units of retrieval in an IR system.

• In practice they might be: Web pages, email messages, LaTeX files, news articles, phone message, etc.

• Update: add, delete, append(?), modify(?)• Passages and XML elements are other

possible units of retrieval.

Probability Ranking Principle

If an IR system’s response to a query is a ranking of the documents in the collection in order of decreasing probability of relevance, the overall effectiveness of the system to its users will be maximized.

Evaluating IR systems

• Efficiency vs. effectiveness

• Manual evaluation– Topic creation and judging– TREC (Text REtreival Conference)– Google Has 10,000 Human Evaluators?

• Evaluation through implicit user feedback

• Specificity vs. exhaustivity

<topic> <title> shark attacks </title>

<desc>Where do shark attacks occur in the world?

</desc>

<narr>Are there beaches or other areas that are particularly prone to shark attacks? Documents comparing areas and providing statistics are relevant. Documents describing shark attacks at a single location are not relevant.

</narr></topic>

Class Project:Wikipedia Search

• Can we outperform Google on the Wikipedia?

• Basic project: Build a search engine for the Wikipedia (using any tools you can find).

• Ideas: Pagerank, spelling, structure, element retrieval, summarization, external information, user interfaces

Class Project: Evaluation

• Each student will create and judge n topics.

• The value of n depends on the number of students. (But workload stays the same.)

• Quantitative measure of effectiveness.

• Qualitative assessment of user interfaces.

• Volunteer needed to operate the judging interface (for credit).

Class Project: Organization• You may work in groups (check with me).• You may work individually (check with

me).• You may create and share tools with other

students. You get the credit. (e.g. Volunteer needed to set up a class wiki.)

• Programming can’t be avoided, but can be minimized. ☺

• Programming can also be maximized.

Class Project: Grading

• Topic creation and judging: 10%

• Other project work: 30%– You are responsible for submitting one

experimental run for evaluation.– Other activities are up to you.

One line?

Tokenization

• For English text: Treat each string of alphanumeric characters as a token.

• Number sequentially from the start of the text collection.

• For non-English text: Depends on the language (possible student projects)

• Other considerations: Stemming, stopwords, etc.

Inverted Indices

• Basic data structure

• More next day…

Plan• Sept 17:

– Inverted indices (from Chapter 3)– Index construction/Wumpus (Stefan)

• Sept 24:– Vector space model, Boolean retrieval, proximity– Basic evaluation methods

• October 1: – Probabilistic retrieval, language modeling– Start topic creation for class project

• October 8: Web search